Journal of Computer Science and Artificial Intelligence
Journal of Computer Science and Artificial Intelligence (JCSAI) 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 computer science and artificial intelligence across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Multimodal Large Model Guided Diffusion Model for Transformer Oil Leakage Image Generation
Transformer oil leakage is a critical defect in power equipment inspection, yet its automatic detection remains challenging because real leakage samples are scarce, highly irregular in shape, variable in color, and easily confused with water stains, rust, shadows, and complex structural backgrounds. To alleviate the data scarcity problem and improve downstream segmentation performance, this paper proposes a multimodal semantic-heuristic diffusion framework, termed MSH-Diff, for automatic transformer oil-leakage image synthesis. The proposed framework exploits the visual reasoning capability and prior industrial knowledge of a multimodal large language model to identify potential leakage-prone components, such as flanges, valves, and radiator fin roots, through expert-role prompt engineering. The recognized component semantics are further mapped to spatial Gaussian anchors by analyzing cross-attention responses in the latent diffusion model, enabling leakage-center localization without manually drawn masks or key-point annotations. In addition, MSH-Diff automatically constructs dense scene descriptions involving illumination, viewpoint, and surface material, which are encoded as semantic constraints to enhance structural consistency and physical realism during diffusion sampling. Experimental results demonstrate that MSH-Diff achieves competitive image quality and diversity, with an FID of 38.14 and an IC-L score of 0.29. When the generated samples are incorporated into downstream semantic segmentation training, the mIoU of DeepLabV3+ increases from 60.09% to 62.31%, confirming the effectiveness of the proposed framework for industrial defect data augmentation.
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Load Balancing Techniques in Multi-Sink Wireless Sensor Networks
Wireless sensor networks (WSNs) rely on battery-powered sensor nodes, making load balancing essential for prolonging network lifetime and ensuring reliable data delivery. Multi-sink WSNs can mitigate the hot-spot problem of single-sink architectures by distributing data collection points, but they also introduce complex imbalance among nodes, paths, cluster heads, and sink service regions. This paper reviews load balancing in multi-sink WSNs by first defining major load types and balancing objects, including communication, energy, path, cluster-head, and sink-region loads. Then, this paper analyzes imbalance formation from network topology, sink placement, node-sink association, routing strategy, traffic dynamics, and residual energy variation. Existing methods are classified into sink deployment, node association, load-aware routing, clustering, mobile sink scheduling, and intelligent optimization approaches, with their advantages and limitations compared. Finally, key challenges are discussed, including dynamic load awareness, low-overhead state maintenance, multi-objective trade-offs, static-mobile sink coordination, and lightweight adaptive mechanisms for practical deployment.
Event-Triggered Control for Course Tracking of Underactuated Ships Considering Actuator Faults
Addressing the course-keeping control problem of underactuated surface vessels subject to strong unknown environmental disturbances and asymmetric actuator saturation, this paper proposes an event-triggered (ETC) adaptive control scheme. A Radial Basis Function (RBF) neural network is constructed to approximate external disturbances and model uncertainties. To handle the impact of non-smooth asymmetric saturation nonlinearities, a continuously differentiable model based on the Gaussian error function is employed. An ETC is introduced to significantly reduce the frequency of controller updates, and a trajectory tracker is designed utilizing the backstepping method. The stability of the closed-loop system is theoretically proven via Lyapunov stability analysis. The control algorithm is simulated within the MATLAB environment to evaluate tracking performance. Simulation results demonstrate that the controller achieves precise and stable tracking, with the number of updates reduced by 1,056—amounting to a 44.0% reduction in communication resource utilization. This research provides a theoretical reference for the trajectory tracking of underactuated vessels under event-triggered mechanisms and asymmetric actuator constraints, with potential for further application in practical engineering.
Fan Vote Share Estimation Based on Constrained Inversion and Bayesian Validation
This study develops a fan vote share estimation model for competition settings in which weekly audience support is not directly observable. The model uses known judge scores, elimination outcomes, and rule mechanisms to infer contestant-level fan vote shares through constrained nonlinear optimization. The framework defines active contestants by elimination week, aggregates multi-judge scores, standardizes judge-score shares, and combines judge scores with estimated fan vote shares under percentage-based scoring rules. To address the non-uniqueness of inverse estimation, the model introduces temporal smoothness and maximum entropy regularization, while hard elimination constraints ensure that inferred results reproduce observed eliminations. The SLSQP algorithm is used for sequential season-week optimization, and Bootstrap perturbation provides confidence intervals and certainty indices. Verification results include champion prediction, cross-validation, residual analysis, and Bayesian latent popularity validation. The model explains cases in which low judge scores coexist with high fan support and shows strong consistency between constrained inversion and Bayesian posterior estimates.
Extended Horizon Model Predictive Control for Cooperative Encirclement of Unmanned Surface Vehicle Swarm
Cooperative encirclement of moving targets using unmanned surface vehicle (USV) swarms plays a vital role in maritime patrol, active interception, and autonomous cluster confrontation missions. Conventional finite-horizon model predictive control strategies often suffer from short-sighted control decisions and gradual performance degradation in long-duration cooperative tasks, which may cause unstable formation evolution and potential constraint violations. To address these limitations, this paper proposes an extended horizon model predictive control (EH-MPC) method for USV swarm cooperative encirclement. By extending the optimization prediction horizon, the proposed method endows the controller with long-term decision-making capability, effectively optimizing the swarm formation evolution while strictly maintaining multiple physical constraints, including inter-vehicle safety distance and formation centroid tracking. Comprehensive numerical simulations demonstrate that the EH-MPC strategy can achieve smooth and stable encirclement trajectory evolution, sustained collision-free performance, and high-precision centroid tracking during long-time marine missions. The results verify that the proposed method possesses excellent stability, constraint adaptability, and cooperative consistency for USV swarm encirclement applications.
Research on the Mechanism of Artificial Intelligence in Tourism Management
With the rapid development of information technology, the application of artificial intelligence in various industries is becoming increasingly extensive, and the tourism industry is also facing the opportunities and challenges of digital transformation. Against this background, this study aims to deeply analyze the mechanism of the role of artificial intelligence in tourism management. Through methods such as literature research, case analysis and empirical research, a systematic study is carried out on the application of artificial intelligence in various links such as tourism resource planning, tourist service, marketing management and security assurance. The research finds that artificial intelligence has significantly improved the efficiency of tourism management and the tourist experience through mechanisms such as technology empowerment, data-driven and collaborative optimization. However, in the process of its application, it also faces problems such as data privacy and security, technology cost and accessibility, and shortage of talents. This study provides a theoretical basis and practical guidance for the rational use of artificial intelligence technology in the tourism industry and promoting innovative development, and is of great significance for promoting the intelligent transformation of the tourism industry.
The Application of the Function and Equation Thought in Middle School Mathematics Problem-Solving
The idea of functions and equations is a core approach running through algebra, geometry, probability and other knowledge modules in middle school mathematics. Its value in cross-module problem-solving and connection with curriculum standards has become increasingly prominent. Taking sequences, trigonometric functions and solid geometry as research objects, this study systematically analyzes the problem-solving logic of the function and equation idea, so as to provide empirical references for front-line teaching to implement curriculum standards and improve students’ key mathematical competencies. This paper adopts literature review, case analysis and classification research. First, it defines the connotation and relationship between function thought and equation thought, clarifies their core feature of “interconversion”, and meets the curriculum standard requirement of “understanding the essence of mathematical thought”. Second, based on recent college entrance examination questions and typical examples, it analyzes the application paths of these thoughts by module. Finally, it summarizes their general problem-solving logic and puts forward teaching and problem-solving suggestions in line with curriculum standards. The research shows that the function and equation idea permeates middle school mathematics and is crucial for developing core competencies such as mathematical abstraction and logical reasoning. It is often tested with the combination of numbers and shapes, classified discussion in the college entrance examination, reflecting the orientation of "emphasizing literacy and application". It simplifies complex problems and improves students’ problem-solving efficiency and thinking quality.
Standardized Entry and Three-Dimensional Visualization Management of Basic Data for Coal Reserves
With the continuous advancement of intelligent coal mine construction and refined resource management, coal reserve management has imposed higher requirements on the standardized organization of basic data, intuitive expression of spatial objects, rapid query of results, and traceability of management processes. In response to problems such as diverse data sources, inconsistent formats, and the dispersed management of two-dimensional drawings and statistical results, this paper constructs a workflow for standardized data entry and three-dimensional visualization management around data sorting, standardized database entry, two-dimensional drawing extraction, three-dimensional visual expression, and result version management in reserve management. Taking boreholes, boundaries, faults, protective coal pillars, reserve blocks, and parameter tables as the main data objects, the workflow realizes the standardized organization of basic coal reserve data through field unification, coordinate checking, object classification, file archiving, attribute association, and version recording. On this basis, functional modules such as two-dimensional drawing preview, three-dimensional model browsing, result query, and version tracking are designed to form a management loop from data preparation to result display. Application using coal mine data shows that the proposed workflow can improve the standardization and traceability of reserve data management, enhance the spatial expression capability of reserve management objects, and provide basic support for the informatization of coal reserve management.
A Review of State Estimation for Time-Delay Neural Networks
In practical engineering, neural networks inevitably suffer from time-delay phenomena caused by signal transmission, hardware response, and data communication. Time-delay will degrade the dynamic performance of neural networks, even lead to oscillation, chaos, and instability. Meanwhile, affected by sensor constraints and external interference, only a small number of system states can be measured directly, which makes state estimation become one of the key technologies in the analysis and application of time-delay neural networks. State estimation refers to reconstructing the real full state of the system through measurable output data, which provides the state basis for stability analysis, fault detection, and feedback control. In recent years, state estimation of time-delay neural networks has become a research hotspot in the fields of control science, intelligent systems, and dynamical systems. This paper systematically summarizes the research results of state estimation for time-delay neural networks. Firstly, the basic model, delay types, and activation function constraints are introduced. Secondly, the core theoretical tools including Lyapunov-Krasovskii functional method, various integral inequalities, reciprocally convex combination lemmas, S-Procedure, and switched system methods are summarized. Thirdly, the research progress of H∞ state estimation, L₂-L∞ state estimation, dissipative state estimation, and event-triggered state estimation is reviewed in detail. Finally, the existing problems and challenges in this field are summarized, and the future development directions including low conservatism, intelligence, network security, and practical application are discussed.
IIR Digital Filter Implementation via Red-billed Blue Magpie Optimization Algorithm
To overcome the insufficient stop-band attenuation and poor stability of conventional IIR filter design, this paper introduces the Red-billed Blue Magpie Optimization (RBMO) algorithm for the synthesis of 8th-order infinite-impulse-response digital filters. By emulating the collective intelligence of red-billed blue magpies, RBMO integrates a tri-modal “hop-walk-fly” search strategy with an “information-sharing & food-storing” cooperation mechanism, achieving an effective balance between global exploration and local exploitation. A pole-radius constraint is embedded to guarantee BIBO stability, while a unified fitness function that simultaneously penalizes pass-band magnitude error, peak stop-band ripple, and weighted transition-band deviation converts the design task into a single-objective optimization problem. Extensive simulations on low-pass, high-pass, band-pass and band-stop specifications demonstrate that the proposed scheme significantly reduces pass-band peak ripple, stop-band amplitude and transition-band error. The optimized coefficients, visualized through magnitude–frequency curves and pole-zero maps, corroborate the feasibility and practical potential of RBMO in high-performance IIR filter design.
Red-billed Blue Magpie Optimization (RBMO)
Visible Watermark Detection Based on an Improved YOLOv8 Model
With the widespread dissemination of digital images on the internet, issues related to image copyright protection and content security have become increasingly prominent, making visible watermarks a common method of copyright marking. To address the frequent inaccuracies in visible watermark detection under complex background conditions, this paper proposes an improved deep learning-based visible watermark detection model that achieves high-precision detection and localization of watermark regions. structural optimization is conducted based on the YOLOv8 object detection framework. Considering the characteristics of visible watermarks, such as large scale variations, uneven transparency, and strong coupling with background textures, an attention mechanism module is introduced into the backbone network to enhance feature representation of critical regions. In addition, a watermark feature enhancement module is designed to strengthen multi-scale feature fusion. Furthermore, the improved YOLOv8 model is fused with the RT-DETR model, combining the local feature extraction capability of convolutional neural networks with the global modeling ability of the Transformer architecture to improve detection accuracy and localization robustness. Experimental results demonstrate that the proposed method achieves an mAP50 of 98.9% and an mAP50:95 of 97.6% on the LVW dataset, outperforming multiple mainstream detection models and effectively balancing detection accuracy with efficiency.
A Study on the Efficiency of Regional Water Resource Utilization in China: Based on the Three-Stage Data Envelopment Analysis (DEA)-Malmquist Method
To objectively evaluate the efficiency of water resource utilization in 31 provinces (municipalities and autonomous regions) of China from 2015 to 2024 and effectively eliminate the effects of environmental factors and random disturbances, this study employs a three-stage data envelopment analysis (DEA) model and the Malmquist index method to construct an evaluation system for water resource utilization efficiency from an input-output perspective. The results show that: (1) environmental factors and random disturbances exert significant effects on the measured efficiency of water resource utilization, and the traditional DEA model overestimates efficiency in the eastern region while underestimating it in the central and western regions; (2) from 2015 to 2024, the overall efficiency of water resource utilization in China exhibited a fluctuating upward trend, with marked regional disparities, the highest efficiency in the eastern region, and relatively lower efficiency in the central and western regions; (3) technological progress is the core driver of efficiency improvement, whereas managerial inefficiency is the key factor constraining efficiency gains in some provinces; and (4) per capita water resources and industrial structure exert heterogeneous effects on water resource utilization efficiency. Accordingly, it is essential to further upgrade the structure of the industry, strengthen interregional diffusion and coordinated application of technologies, and implement differentiated water resource management policies to promote efficient water use and sustainable development.
A Time-Series Motif-Based Rule Fusion Method for Interpretable State Prediction of Aluminum Electrolysis Cells
Accurate state prediction is important for improving the stability and efficiency of aluminum electrolysis cells, but practical prediction remains difficult because the production process contains strongly coupled variables, delayed process responses, and many decisions based on operator experience. To address these issues, this paper presents an interpretable motif-based rule fusion method for cell state prediction. The method first separates industrial variables into decision variables and state variables so that manual control actions and automatically monitored responses can be modeled with clear semantic roles. Multivariate motif groups are then mined from each dataset, after which temporal rules and association rules are extracted. Association rules describe delayed operation-response relationships between decision motifs and subsequent state motifs, while temporal rules describe weekday and monthly occurrence regularities of state motifs. During prediction, the method prioritizes matched association rules and uses temporal rules as a fallback when no reliable association rule is available. Experiments on large-scale data from 3,391 pot controllers show that the combined rule-fusion strategy achieves lower pattern-level prediction error than single-rule strategies and representative forecasting baselines. The method also provides explicit evidence linking interventions to later state evolution, supporting interpretable process monitoring and operator-oriented decision support.
Design and Implementation of an Automatic Vehicle License Recognition System for End-of-Life Vehicle Dismantling Scenarios
The accuracy and efficiency of vehicle license information entry directly determines whether such operations can truly implement the Standards for Motor Vehicle Recycling and Dismantling Operations and further advance the digitalization of compliant supervision in this sector. To address challenges in this scenario, such as severely damaged documents, complex shooting conditions, and strong field logic, this paper designs and implements an automatic vehicle license information recognition system for end-of-life vehicle dismantling enterprises. The system first employs a YOLOv8 model combined with a multi-frame stability criterion to achieve robust detection of the vehicle license region and capture clear images under complex shooting environments. Subsequently, PaddleOCR is utilized for text extraction. Finally, a multi-layer post-processing engine integrating regular expression matching, semantic analysis, and business rules corrects and structures the raw OCR results, outputting standardized vehicle information. Experiments demonstrate that the designed post-processing rule engine effectively corrects the majority of OCR recognition errors. Ultimately, the system is integrated into a desktop application featuring data visualization and browser automation form-filling capabilities, achieving an end-to-end automated process from physical document scanning to business system entry, thereby enhancing operational efficiency and data accuracy.
ReadFact: A Workflow Framework for Readability and Factual Consistency in Medical Text Simplification
Biomedical literature often remains inaccessible to lay readers due to technical complexity. Medical text simplification (MTS) aims to improve readability while preserving factual accuracy. We propose ReadFact, a workflow that integrates three complementary components: (i) a simplifier trained with Direct Preference Optimization (DPO), (ii) a readability reward model trained with Proximal Policy Optimization (PPO), and (iii) a PICO-based fact checker for structured factual alignment. Our system is trained on the Cochrane Database of Systematic Reviews. Intermediate simplifications are first generated using DeepSeek-V3, and each (source, mid, target) triple is expanded into preference pairs to supervise both DPO and PPO training. Factual consistency is evaluated using SciBERT-based PICO similarity, while readability is optimized through preference-driven learning. Experiments show that ReadFact improves factual consistency by more than 23% over the DPO baseline and increases readability by over 5 percent. On the NapSS benchmark, ReadFact-DPO achieves the highest BERTScore, demonstrating closer alignment with human references.
ARIMA Forecasting and Linear Programming with Simulated Annealing for Adaptive Network Traffic Scheduling
Adaptive resource scheduling in distributed network systems requires accurate forecasting of incoming traffic, principled allocation of workload across processing paths, and disciplined selection of high-impact nodes for capacity expansion. This paper develops a four-stage framework that integrates ARIMA time-series forecasting, single-objective and multi-objective linear programming, simulated annealing optimization, and entropy-weighted TOPSIS evaluation to handle the full scheduling pipeline. Stationarity is verified through ADF testing before fitting an ARIMA model that produces 31-day-ahead forecasts of per-path traffic volume with calibrated residuals. The forecasts feed a linear programming formulation that minimizes total operational cost subject to per-node utilization caps of 100%, solved through simulated annealing with adaptive cooling schedule. The multi-objective extension introduces auxiliary capacity reallocation goals and identifies one additional path 3→1 in the recommended topology. Entropy-weighted TOPSIS evaluation across multiple importance criteria selects the top three nodes 10, 14, and 4 and the top three paths 14→8, 14→9, and 36→4 for capacity expansion. Sensitivity analysis under random perturbation injection confirms that the proposed scheduling policy retains feasible utilization profiles across all perturbation scales tested, demonstrating robust performance under stochastic traffic variation typical of operational network deployments.
Cloud Platform-Enabled Collaborative Application Models of Big Data and Cloud Computing in Finance
For the first time, by integrating convergent big-data-analysis and cloud-computing technology to organise enterprises' large-scale financial management from all angles. This paper studies the Collaborative Application Models realised by Cloud Platforms for the Integration of Large-Scale Data Processing and Financial Operations, such as Real-Time Reporting, Risk Management and Resource Optimization. Based on existing research Frameworks for Information System and Financial Technology, this paper explores how service model - Infrastructure as a Service (IaaS), Platform as a Service (PaaS), And Software as a Service (SaaS) interacts with Big Data Pipelines to build coherent financial Intelligence Systems. There are also problems during implementation to implement the above countermeasures; specifically related to issues with data Governance and Regulations. Based on the results, it can be inferred from these that only through intentional integration among various aspects does an ideal system result. Practitioners' navigation of cloud adoption under regulated financial conditions is given recommendations.
Cross-Modal Alignment in Multimodal Large Language Models: Mechanisms, Challenges, and Future Directions
As a significant breakthrough in recent years for artificial intelligence research, multimodal large-scale language models have extended the ability of language models across multiple dimensions, including vision and hearing, into non-linguistic forms through technical processes such as cross-modal alignment. Examine the mechanisms, problems and future paths for cross-modal alignment in multimodal large-scale language models based on representations learned by using the theory of representation learning, transformer-based attention mechanism and information-theoretical framework as core references. Three interrelated arguments are put forward: Cross-modal alignment is no longer a one-shot technical task but rather comprises three distinct paths: contrastive learning, cross-modal attention fusion, and instruction-based fine-tuning; The main problems faced by the research on cross-modal alignment, such as modality gap, hallucination, data bias and evaluation difficulties, all stem from their intrinsic diversity among various representation modes. Therefore, some promising future directions should be explored, which includes precise semantic alignment, extension to videos or soundscapes, developing transparent interpretation systems to help people better understand the system, etc. This paper presents an empirical integration of the rapid evolution of a highly dynamic technological system for researchers studying multimodal alignment in related disciplines.
How Perceptions Variance Shapes Brand Culture: Deconstructing Consumer Cognition Through a Comparison of Generative AI and Human-generated Texts
This study investigated how differences in consumer perceptions of content origin—specifically between Gen-AI and human creation—influence brand culture, with a focus on addressing the underexplored connection between content authorship and brand cultural formation. Drawing on the Elaboration Likelihood Model (ELM), brand signal theory, and cultural consistency theory, two empirical studies were carried out to unpack the cognitive and affective mechanisms underlying this relationship. Study 1 compared consumer perceptions across four dimensions—brand authenticity, credibility, electronic word-of-mouth (EWOM) intentions, and brand attitudes. Findings indicated no significant differences in these perceptions between the two AI tools. Study 2 further explored the impact of human-generated versus DeepSeek-generated texts on brand culture. Results revealed that human-authored narratives significantly outperformed AI-generated content in enhancing perceptions of authenticity, credibility, EWOM intentions, brand attitudes, and brand culture. Brand credibility is the only factor with a significant positive effect on brand culture. Additionally, text origin (human vs. AI) played a moderating role, with human authorship strengthening the resonance between perceptual dimensions and brand culture. The AI origin functions as a negative peripheral cue under the ELM framework triggered heuristic skepticism. The study offered practical insights for marketers on integrating Gen-AI into brand communication while preserving cultural coherence.
A Review of Haplotype Assembly: Methods and Challenges
Haplotype assembly aims to partition sequencing reads according to their chromosomal origin based on variant information and to reconstruct the allelic configurations along individual chromosomes. Its core significance lies in revealing the true combinations of variants on the same chromosome, thereby enhancing the biological interpretability of genetic information. This paper presents a systematic review of haplotype assembly methods, with a focus on their underlying models and algorithmic strategies. Existing approaches are broadly categorized into reference-guided and de novo methods, and their characteristics are comparatively analyzed. Finally, future research directions are discussed, including the integration of multi-source sequencing data and the development of more robust and efficient models. Overall, this review provides a comprehensive overview of recent advances in haplotype assembly and offers valuable insights for future studies.
Research Progress and Frontier Prospects of Chromatin A/B Compartment Classification Methods
Accurate classification of chromatin A/B compartments is one of the core issues in three-dimensional genomics, which is critical for understanding how genome spatial organization affects gene regulation, cell fate determination, and disease pathogenesis. Since Hi C technology first revealed in 2009 that the genome is spatially segregated into transcriptionally active A compartments and inactive B compartments, principal component analysis (PCA) based on Hi C contact matrices has long served as the standard strategy for compartment identification. However, with the in-depth advancement of research toward single-cell resolution, cross-cell-type prediction, and the integration of multimodal epigenomic data, the inherent limitations of conventional PCA have become increasingly prominent. In recent years, the introduction of machine learning and deep learning has brought paradigm innovation to chromatin compartment classification. Methodological evolution ranges from early stacked artificial neural networks based on sequence-derived features, to convolutional neural networks utilizing raw DNA sequences, and further to advanced approaches integrating recurrent neural networks, Transformer architectures, graph-theoretic optimization, and interpretable learning, which have substantially expanded the theoretical framework and application scope of compartment analysis. This review systematically summarizes the research progress of chromatin A/B compartment classification methods and covers more than ten representative approaches, including SACSANN, ABCNet, CoRNN, TECSAS, MaxComp, DeepExDC, HiC-SCA, ABCRNet, SCI and CDACHIE. We comprehensively summarize and comment on existing advances from three dimensions: biological principles and experimental detection techniques, machine learning-based methods, and deep learning-driven strategies. Furthermore, we discuss the major challenges currently restricting this field, such as limited cross-cell-type generalization, insufficient model interpretability, the absence of unified benchmarking criteria, and widespread spatial heterogeneity in compartment annotation. Finally, we highlight future research directions, including multimodal data integration, cross-domain adaptation of foundation models, and the translational application of compartment prediction tools to clinical research scenarios such as cancer genomics and developmental biology.
Advances and Frontier Prospects of Computational Detection Methods for Chromatin Loops
The eukaryotic genome exists in a highly folded three-dimensional structure within the nucleus. As the core functional unit of the 3D genome, chromatin loops are formed via the loop extrusion mechanism mediated by CTCF proteins and cohesin complexes. They directly facilitate the spatial interaction between distal enhancers and gene promoters, and precisely regulate essential biological processes including gene transcription, DNA replication and damage repair. Structural abnormalities of chromatin loops are closely associated with the occurrence and progression of numerous diseases, such as cancers, developmental defects and neurodegenerative disorders. Chromosome conformation capture technologies represented by Hi-C, ChIA-PET and HiChIP have laid a critical foundation for the experimental identification of chromatin loops, yet they suffer from inherent limitations including high sequencing costs, limited data resolution, prominent noise interference and obstacles in single-cell detection. Computational detection methods for chromatin loops eliminate the reliance on large-scale experimental expenditures. These approaches can automatically mine characteristic patterns from DNA sequences, epigenomic profiles and 3D genomic contact matrices, enabling efficient, accurate and low-cost chromatin loop identification, and have emerged as a vital solution to compensate for the shortcomings of experimental techniques. This review systematically summarizes the molecular formation mechanisms, biological functions of chromatin loops, and technical characteristics of mainstream experimental detection methods. Following the trajectory of technological evolution, existing computational detection approaches are classified into three categories: statistical methods, conventional machine learning methods and deep learning methods. The algorithmic principles, input data types, core advantages and application limitations of each category are elaborated respectively. Furthermore, this paper integrates emerging chromatin loop detection technologies developed since 2023 based on diffusion models, multimodal data fusion, nanopore sequencing, self-supervised learning and single-cell 3D genomics, and details their technological innovations and performance breakthroughs. It also concludes major challenges in this field, including data imbalance, low-resolution adaptation, cross-cell line generalization, the absence of universal gold standards, and the dynamic analysis of single-cell chromatin loops. In addition, future research directions are prospected from the perspectives of multimodal fusion, weakly supervised learning, single-cell dynamic modeling, disease correlation analysis, and lightweight interpretable models. This review aims to provide a comprehensive reference for researchers in the field of 3D genomics regarding computational detection methods of chromatin loops, and promote the in-depth integration of algorithm development and biomedical applications.
Towards Efficient Retail Text Localization: A Lightweight Network with Saliency-Guided Sparse Attention
Retail product packaging text detection is crucial for intelligent label auditing and smart retail applications. While deep learning models, particularly the YOLO series, have demonstrated remarkable real-time inference capabilities, detecting text in complex retail scenarios presents formidable challenges due to extreme scale variations, dense distributions, and severe background interference. During the repeated downsampling processes in standard YOLO architectures, the fine-grained features of microscopic text are prone to degradation, and introducing conventional global attention mechanisms heavily inflates computational costs. To address these issues, this paper proposes an enhanced lightweight, real-time object detector optimized for retail packaging text. First, to prevent the loss of minute text details, we propose a Context Guide Downsample block that jointly aggregates local, surrounding, and global contextual descriptors during spatial resolution reduction. Second, to break the quadratic computational bottleneck of traditional self-attention, a Dynamic Saliency-guided Sparse Attention module is introduced into the encoder. DSSA adaptively filters out background redundancies and establishes efficient long-range dependencies across text regions. Finally, a Multi-scale Feature Mapping module replaces conventional feature concatenation, employing a scale-aware non-linear modulation strategy to align and fuse heterogeneous hierarchical semantics in the neck network. Extensive experiments on the Food-Product-Image dataset demonstrate that the proposed model achieves a superior trade-off between detection accuracy and inference speed, outperforming existing state-of-the-art lightweight detectors in complex text localization tasks.
A Multimodal Conversational Agent for Co-Creating Teaching Slides and Lesson Plans
This paper presents a multimodal conversational teaching agent that co-creates classroom slide decks and lesson plans with teachers through a staged review process. The prototype was motivated by a practical teaching-design scenario in which courseware preparation is fragmented across several tools and teacher intent is often captured only superficially. The proposed system combines three capabilities: multi-turn intent clarification, retrieval-augmented generation over a locally curated knowledge base, and optional reference-file grounding from uploaded PDF, DOCX, PPT, image, or video materials. Instead of exporting final artifacts at the first interaction, the agent follows a text-first pipeline. In Round 1 it produces a slide draft and a lesson-plan draft for review. In Round 2 it converts the approved slide draft into a structured slide object and then into PPTX. In Round 3 it converts the approved lesson-plan draft into Markdown and then into DOCX. This design keeps the teacher in control of pedagogical structure, terminology, and difficulty level while preserving the efficiency gains of large language models. The implementation separates chat-oriented intent handling from deterministic formatting and export steps, and it uses explicit state variables to support revision, regeneration, and artifact continuity across rounds. A case-based verification on networking topics such as VLAN, WLAN, and STP shows that the system can align courseware drafts, exported slides, and exported lesson plans under a single interaction loop. The paper contributes a reproducible low-code architecture, a multimodal fusion strategy that does not require writing uploaded references into the knowledge base, and a teacher-centered workflow for controllable educational content generation.
Environmental Modulation of Interpolation Strategies and Algorithm Adaptability in Remote Sensing Retrieval of Farmland Soil Moisture: A Theoretical Framework
Accurate retrieval of farmland soil moisture from multi-source remote sensing data requires simultaneous consideration of time-series interpolation strategies and machine learning algorithm selection. However, the theoretical basis explaining how environmental conditions modulate their joint effects remains insufficiently articulated. This paper proposes a unified conceptual framework termed the "Interpolation-Algorithm-Environment" (IAE) triad, which explains why no universally optimal retrieval pipeline exists and why optimal choices are inherently environment-contingent. We further introduce the concept of Post-Treatment Spurious Correlation (PTSC) to characterize a failure mode in which interpolation processing inadvertently introduces artificial feature structure that retrieval models learn and amplify, degrading generalization. Theoretical analysis of algorithm-environment matching, interpolation-induced information distortion, and synergistic constraint mechanisms provides a transferable decision basis for designing robust soil moisture retrieval systems under diverse agro-climatic conditions.
A Hybrid Model for Multi-step Time Series Prediction Based on Granular Computing and LSTM
Multi-step time series forecasting based on iterative strategies often suffers from severe error accumulation, which significantly degrades long-horizon prediction performance. To address this issue, this paper proposes a hybrid forecasting framework that integrates granular computing with Long Short-Term Memory (LSTM) networks. First, the original time series is transformed into a sequence of temporal information granules, each represented by a triplet consisting of cardinality, mean, and standard deviation, which respectively characterize data density, trend level, and fluctuation intensity. Second, three parallel LSTM networks are constructed to independently predict these granular components, enabling effective feature decoupling and reducing mutual interference during the learning process. Finally, a degranulation mechanism is employed to reconstruct the predicted granules into the original time series, allowing multiple future points to be forecasted within a single prediction step and thereby mitigating error propagation. Extensive experiments on diverse public benchmarks, including classical time series datasets and multi-city PM2.5 concentration data, demonstrate that the proposed framework consistently outperforms traditional statistical methods, standard LSTM models, and existing granulation-based approaches, with im-proved accuracy under long-horizon fore-casting scenarios.
Intelligent Prediction Method of Deep Coalbed Methane Content Based on Bayesian Optimization Optimized Random Forest
Coalbed methane (CBM) content is a critical indicator for CBM resource evaluation and development. Traditional prediction methods rely mainly on core experiments or empirical formulas, which suffer from high costs, long cycles, limited generalization ability, and difficulties in capturing the complex nonlinear relationships between geological/geophysical parameters and CBM content. To address these shortcomings, this paper proposes an intelligent prediction method for deep CBM content based on random forest regression and professional geological feature engineering. The method integrates well-logging data (dual-lateral resistivity LL, long-spaced gamma ray GRLS, natural gamma ray GR, spontaneous potential SP, caliper CAL) and geological parameters (coal seam thickness, bedrock thickness) to construct composite features such as pressure coefficient, resistivity gradient, and organic matter indicator. A robust standardization method based on median and interquartile range (IQR) is adopted to eliminate the interference of outliers. Bayesian optimization is used to adaptively optimize the hyperparameters of the random forest model, avoiding the subjectivity of manual parameter tuning. Experimental results on deep coal seam samples demonstrate that the proposed method achieves excellent fitting performance with a coefficient of determination (R2) exceeding 0.85, showing strong generalization ability, robustness to abnormal data, and engineering practical value. It can provide reliable technical support for the efficient exploration and development of deep CBM resources.
Research on Information Security Vulnerability Testing and Verification Methods and Protection Countermeasures for LLM Applications in Intelligent Cockpits
In recent years, the application of large language models (LLMs) in intelligent cockpits has been continuously deepened, significantly improving the natural human-vehicle interaction experience. However, the generative characteristics of models, multi-modal data interaction, and access to vehicle control privileges have also introduced a series of new information security vulnerabilities such as prompt injection, data privacy leakage, unauthorized vehicle manipulation, and model hallucinations. Traditional in-vehicle security testing systems mostly target vulnerabilities in in-vehicle operating systems, in-vehicle buses, and network communications, and are difficult to adapt to the semantic-level and generative security risks brought by LLMs. To address this issue, this paper classifies and analyzes the security vulnerabilities of LLM applications in intelligent cockpit scenarios, constructs a vulnerability testing and verification method integrating static auditing, dynamic fuzz testing, red team adversarial testing, and multi-modal boundary testing, and establishes a corresponding security evaluation index system. On this basis, a hierarchical protection strategy is proposed from five dimensions: input protection, model hardening, system isolation, data security, and compliance auditing. Verified through real-vehicle environment testing, the proposed testing method can effectively identify 92.7% of known security vulnerabilities. After deploying the protection countermeasures, the relevant attack success rate drops from 38.5% to 4.3%, which can provide technical support for the secure development, testing verification, and engineering implementation of LLMs in intelligent cockpits.
Hypergraph Neural Networks for Brain Tumor Analysis and Medical Image Understanding: A Review
Medical image analysis plays a critical role in modern clinical diagnosis and treatment planning. While deep learning, particularly convolutional neural networks, has achieved remarkable success, conventional models struggle to capture the complex high-order relationships inherent in anatomical structures and pathological patterns. Hypergraph neural networks (HGNNs), which generalize standard graphs by allowing hyperedges to connect multiple nodes, have emerged as a powerful paradigm for modeling such high-order interactions. This review provides a systematic overview of hypergraph learning techniques for medical image analysis. We first introduce the mathematical foundations of hypergraphs and categorize existing hypergraph construction methods into similarity-based, topology-based, and learning-based approaches. We then review state-of-the-art hypergraph neural network architectures, including dynamic hypergraph neural networks, hypergraph transformers, and over-smoothing mitigation strategies. Subsequently, we survey key applications in brain tumor classification, functional brain network analysis, medical image segmentation, and survival prediction. Finally, we discuss open challenges and promising future directions, including multi-modal fusion, model interpretability, computational efficiency, and domain generalization. This review aims to provide researchers with a comprehensive understanding of hypergraph learning for medical imaging and to inspire further advancements in this rapidly evolving field.
Graph Learning Against Adversaries: Bridging Structural Topology and Multimodal Semantics in Fraud Detection
The digital economy's unprecedented expansion has been accompanied by increasingly sophisticated, networked fraudulent activities that exploit systemic vulnerabilities. Traditional tabular machine learning models evaluate entities in isolation, often failing to detect the collusive, multi-step strategies employed by modern adversaries. Consequently, Graph Neural Networks (GNNs) have emerged as the foundational architecture for advanced fraud detection, leveraging complex topological relationships to uncover illicit patterns. This paper systematically traces the evolutionary trajectory from general graph representation learning to Graph Anomaly Detection (GAD), and ultimately to the highly adversarial domain of fraud detection. We deconstruct the critical mathematical and structural challenges inherent in this domain, primarily extreme class imbalance and severe feature heterophily—a phenomenon where malicious entities actively deploy relational camouflage by linking to benign nodes, thereby neutralizing the smoothing mechanisms of standard homophily-assuming GNNs. We analyze the architectural adaptations developed to counteract these adversarial tactics. Furthermore, we critically re-evaluate recent advancements in the field, analyzing empirical evidence that questions the purported absolute superiority of Graph Transformers over meticulously optimized classic GNNs. Finally, we explore the emergent frontier of Large Language Model (LLM)-enhanced graph learning. We detail how sophisticated techniques, such as dual-granularity prompting and agentic semantic vectorization, are actively resolving the catastrophic information loss associated with early-stage multimodal feature encoding. By bridging discrete topological structures with continuous multimodal semantic reasoning, this paper charts the future trajectory of robust, scalable, and explainable adversarial graph learning.