Computer Life (CPL) 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, network, communication science and technology, and its application in human social life across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Higher Education Financing and Graduate Labor-Market Outcomes: A Four-Channel Conceptual Framework
Higher education financing shapes not only the fiscal sustainability of the tertiary system but also the employment trajectories of graduates. This article offers a conceptual synthesis rather than an empirical test. It develops a four-channel mechanism model that connects four financing channels—public appropriation, cost-sharing, student loans, and industry–academia partnerships—to labor-market outcomes through four mediating mechanisms: resource allocation, field-of-study choice, completion and skill composition, and debt burden. State-led and market-oriented models interact through substitution and complementarity rather than being mutually exclusive: public financing may advance equity but weaken labor-demand responsiveness, while cost-sharing and loans may improve allocative efficiency at the cost of debt-induced distortions in occupational sorting. The paper makes three contributions: it formalizes the framework as four explicit propositions; it differentiates four labor-market outcome dimensions (starting earnings, job–skill match, occupational mobility, job satisfaction); and it derives a theory-grounded indicator set paired with candidate empirical strategies. The framework suggests that financing reform is best understood as a calibrated portfolio rather than a binary state-versus-market choice.
Copyright (c) 2026 Computer Life https://creativecommons.org/licenses/by-nc/4.0
Design of Parachute Airdrop Attitude Data Visualization Program Based on Tacview
In the field of airdrop testing, this paper presents a program designed on the Tacview software platform to convert attitude test data collected during airdrop processes into a format compatible with Tacview. By transforming numerical test data into three-dimensional visual images, the program enables visualization of airdrop attitude data, thereby enhancing researchers' visual perception and understanding of the test results.
Predictive Modeling of Online Game User Retention Based on Flow Theory and LightGBM
With the global game industry entering a new stage of stock competition and experience value driven, accurate prediction of user retention trend has become the core proposition of refined operation of enterprises. In order to solve the problem that the behavior data is disconnected from the players' psychological state in online game retention prediction, this paper proposes a prediction framework that combines psychological flow theory and LightGBM algorithm. By constructing the quantitative index FlowIndicator of cardiac flow imbalance threshold, the qualitative psychological experience is transformed into a dynamic tracking numerical system. Based on the behavior logs of 40000 MMORPG players, using LightGBM algorithm and SHAP interpretability tool, a retention prediction system with time series adaptability is constructed. The experimental results show that the AUC of the model test set reaches 0.93 and the F1 score reaches 0.89, which significantly improves the recognition accuracy of the recessive loss signal and provides interdisciplinary methodological support for the refined operation of the game.
Research on Implicit Evaluation Factors in Competitive Reality Shows Based on Multimodal Reconstruction and Correlation Auditing
To address fairness controversies arising from opaque audience voting in "Dancing with the Stars," this study proposes a quantitative framework integrating implicit variable reconstruction with competitive success driver assessment. We reverse-engineer undisclosed fan voting behavior through two pathways: a constrained nonlinear inverse optimization model based on the SLSQP algorithm, and a Bayesian ensemble inference grounded in Dirichlet distributions. Experiments demonstrate high consistency between the two methods, achieving 91.7% overall prediction accuracy. Furthermore, Spearman and Kendall rank correlation metrics reveal only a moderate positive correlation between expert evaluations and public preferences, evidencing the pervasiveness of popularity-driven bias. Statistical tests confirm the significant efficacy of the "judge redemption" mechanism in anchoring technical proficiency. This study provides rigorous mathematical tools for deconstructing opaque evaluation systems and offers empirical evidence for optimizing competitive scoring frameworks.
Multi-Granularity Semantic Inconsistency Modeling for Detecting LLM-Generated Fake Reviews in Online Marketplaces
The rapid advancement of large language models (LLMs) has fundamentally altered the landscape of online review manipulation, enabling the mass production of synthetic reviews that closely mimic authentic human writing. Existing fake review detection methods, predominantly designed for manually crafted deceptive content, exhibit significant performance degradation when confronted with LLM-generated text. This paper proposes a multi-granularity semantic inconsistency modeling (MGSIM) framework that captures latent contradictions embedded within LLM-generated reviews across word, sentence, and discourse levels. The framework integrates a hierarchical encoder with cross-granularity attention alignment and an inconsistency scoring module trained on contrastive review pairs collected from major e-commerce platforms. Experimental results on three benchmark datasets demonstrate that MGSIM achieves an F1 score of 91.3%, outperforming state-of-the-art baselines by an average margin of 6.8 percentage points. Ablation studies confirm that discourse-level inconsistency signals contribute the most discriminative power, particularly for reviews generated by instruction-tuned LLMs. This work offers both a practical detection tool and a theoretical characterization of the structural artifacts introduced by LLM generation, with implications for platform governance and consumer trust.
Drift-Resilient Fraud Classification via Continual Learning with Replay-Guided Synthetic Minority Oversampling
Financial fraud detection systems operate under persistent conditions of distributional non-stationarity, where evolving fraud patterns and severe class imbalance jointly undermine the reliability of statically trained classifiers. This paper presents a drift-resilient fraud classification framework that integrates continual learning (CL) with a replay-guided synthetic minority oversampling technique (SMOTE) module to address both challenges through a unified training loop. The proposed system maintains an episodic memory buffer populated by strategically selected minority-class exemplars, which simultaneously serve as replay anchors for preventing catastrophic forgetting and as geometrically informative neighborhood seeds for cross-temporal synthetic sample generation. A drift-aware buffer management policy prioritizes boundary-proximal and recently misclassified minority instances, maximizing the informativeness of fixed memory capacity across successive temporal periods. Experimental evaluation on two benchmark fraud datasets under simulated concept drift conditions of varying magnitude demonstrates that the framework achieves a 12.4% improvement in area under the precision-recall curve (AUPRC) over standard gradient-boosted classifiers and a 9.7% gain over naive replay baselines. Ablation experiments confirm the independent contribution of each system component, with drift-aware buffer management identified as the single most impactful design choice. The results establish that tightly coupling memory-guided oversampling with experience replay constitutes a principled and effective strategy for robust, long-horizon fraud detection in non-stationary data streams.
Sparse Experts Scale Better in Efficient Mixture Architectures for Trillion Parameter Models
The scaling of large language models to trillion-parameter regimes has surfaced critical efficiency bottlenecks inherent to conventional dense architectures. Sparse Mixture-of-Experts (MoE) frameworks offer a compelling alternative by selectively activating subsets of model parameters per input token, thereby decoupling total model capacity from per-token computational cost. This paper investigates how sparse expert architectures scale more favorably than dense counterparts in the trillion-parameter setting, analyzing the structural design principles governing routing efficiency, load balancing, and expert specialization. A systematic examination of state-of-the-art MoE configurations is presented, encompassing gating mechanisms, expert granularity choices, and communication strategies in distributed training environments. The methodology draws on comparative architectural analysis and empirical benchmarks across public model evaluations to characterize the scaling behavior of sparse models. Results demonstrate that sparse MoE models achieve performance competitive with dense models at a fraction of the active parameter count, while exhibiting superior scaling slopes on standard language modeling benchmarks. Expert collapse and load imbalance are identified as persistent failure modes requiring architectural mitigation. The findings confirm that sparse expert scaling represents a practically grounded and theoretically well-supported path toward building highly capable, resource-efficient models at the trillion-parameter frontier.
Distributed Training Strategies for Reducing Carbon Footprint in Large Scale Model Development
The rapid expansion of deep learning has led to increasingly large-scale neural networks whose training demands massive computational resources, resulting in substantial carbon dioxide (CO2) emissions. This paper investigates distributed training strategies specifically designed to reduce the carbon footprint associated with large-scale model development. We propose an Energy-Aware Distributed Training (EADT) framework that integrates gradient compression, mixed-precision arithmetic, and carbon-conscious workload scheduling across heterogeneous graphics processing unit (GPU) clusters. By combining ring-All Reduce communication protocols with adaptive sparsification and low-precision quantization, the proposed framework reduces both inter-node communication overhead and total floating-point operations, thereby lowering energy consumption. Experimental results demonstrate that the EADT framework achieves up to a 40.0% reduction in estimated CO2 emissions compared to baseline full-precision data-parallel training, while incurring only marginal losses in convergence quality. These findings highlight the potential of communication-efficient and computationally frugal distributed training paradigms as practical tools for greener artificial intelligence (AI) development.
Designing Energy Efficient Accelerators Through Neural Architecture Search
The exponential growth of deep learning workloads in embedded and edge computing environments has placed extraordinary demands on hardware efficiency, compelling researchers to seek principled methods for designing accelerators that simultaneously maximize computational throughput and minimize energy consumption. Neural architecture search (NAS) has emerged as a transformative paradigm for automating the discovery of model architectures, and its extension into hardware-aware co-design domains opens a compelling pathway toward accelerators that are not only functionally accurate but also energy-optimal. This paper presents a comprehensive framework for designing energy-efficient accelerators through hardware-aware NAS, integrating multi-objective optimization, dataflow-level energy estimation, and differentiable search strategies to navigate a joint hardware-software design space. We propose a co-exploration methodology that simultaneously optimizes the network topology, processing element (PE) array configuration, and memory hierarchy by incorporating energy and latency proxies directly into the search reward function. Experiments conducted on standard image classification benchmarks demonstrate that the proposed framework achieves accuracy competitive with manually designed architectures while reducing system-level energy consumption by up to 43% compared to baseline accelerator configurations. Our results further validate that dataflow-aware hardware parameterization yields substantially more energy-efficient accelerators than platform-agnostic search strategies, and that the use of analytical energy models as differentiable objectives enables scalable search without costly hardware prototyping.
Raft and Beyond: Practical Consensus Mechanisms for Geo-Distributed Data Systems
Consensus protocols form the foundational building blocks of fault-tolerant distributed data systems, enabling nodes to agree on a consistent shared state despite failures, network partitions, and heterogeneous communication delays. The Raft algorithm, designed explicitly for understandability and practical deployability, has become the de facto foundation for a wide class of modern replicated storage systems. However, as organizations require data infrastructure to span geographically distributed (GD) environments, vanilla Raft exhibits significant performance limitations rooted in its single-leader architecture and majority quorum constraints, causing write latency to scale with cross-region wide-area network (WAN) round-trip times (RTTs). This survey provides a comprehensive review of consensus mechanisms from foundational Raft semantics to advanced variants engineered for GD deployments, covering state machine replication (SMR), Multi-Raft architectures, flexible quorum designs, and Byzantine fault tolerance (BFT). We analyze prominent WAN consensus protocols including WPaxos, EPaxos, and HotStuff, examining their theoretical guarantees and practical trade-offs in latency, throughput, and operational complexity. The survey further examines how production systems such as CockroachDB, TiKV, and YugabyteDB integrate and extend these protocols to achieve global-scale consistency. By synthesizing recent advances in adaptive leader election, hierarchical consensus, hybrid protocol design, and BFT convergence with crash-fault-tolerant (CFT) alternatives, this paper provides a structured reference for researchers and engineers designing the next generation of GD data infrastructure.
Cold Start Latency Optimization Strategies for Function as a Service Platforms
Function as a Service (FaaS) has established itself as the dominant delivery model within the serverless computing (SC) ecosystem, enabling developers to deploy stateless, event-driven workloads without provisioning or managing any underlying server infrastructure. Despite substantial operational advantages in cost granularity and scaling automation, FaaS platforms are subject to a persistent performance bottleneck known as cold start latency, which occurs whenever a new execution environment must be initialized from scratch before an incoming function invocation can be served. Cold start penalties range from tens of milliseconds for lightweight runtimes to several seconds for applications executing on the Java Virtual Machine (JVM), producing direct violations of service-level objectives (SLOs) in latency-sensitive production deployments. This paper reviews optimization strategies for cold start latency across four principal categories: pre-warming and keep-alive policies, snapshot-based checkpoint-restore techniques, lightweight virtualization and isolation mechanisms including WebAssembly (Wasm), and scheduling and resource management strategies. Machine learning (ML) is examined as a cross-cutting enabler for predictive and adaptive mitigation. This review synthesizes the current state of understanding, characterizes trade-offs among competing strategies, and identifies open challenges including snapshot staleness management, isolation-speed tensions, and edge deployment constraints.
Low Rank Adaptation Enables Efficient Domain Transfer in Billion Parameter Language Models
The rapid growth of billion-parameter language models has transformed natural language processing (NLP), yet deploying these models across specialized domains remains constrained by the prohibitive cost of full fine-tuning. Low-rank adaptation (LoRA) has emerged as a leading parameter-efficient fine-tuning (PEFT) approach that restricts weight updates to low-dimensional matrix products, dramatically reducing trainable parameter counts without sacrificing downstream performance. This review synthesizes theoretical foundations, algorithmic advances, and empirical findings concerning LoRA and its derivatives as applied to large language models (LLMs) in domain transfer settings. We examine how rank decomposition enables adaptation across biomedical text mining, legal document analysis, code generation, and financial analytics, surveying evidence that LoRA-based methods match full fine-tuning while updating fewer than one percent of total model parameters. Variants including QLoRA, AdaLoRA, LoRA+, and DoRA are analyzed with respect to their contributions in quantization, adaptive rank allocation, and optimization refinement. The theoretical basis in intrinsic dimensionality that justifies low-rank approximations is discussed alongside practical considerations of rank selection, target module choice, and multi-task deployment. By consolidating findings from sixty recent studies, this paper offers a structured understanding of when and why LoRA succeeds, identifies persistent limitations, and delineates promising directions for future work in efficient domain transfer.
Retrieval Augmentation Reduces Factual Errors in Knowledge-Intensive Language Model Tasks
Large language models (LLMs) have demonstrated exceptional capabilities across natural language processing (NLP) tasks; however, they remain persistently susceptible to generating factually incorrect content—a phenomenon broadly termed hallucination. Retrieval-augmented generation (RAG) has emerged as a principled paradigm for mitigating this limitation by grounding model outputs in dynamically retrieved external evidence, thereby substantially reducing factual errors in knowledge-intensive settings. This paper presents a comprehensive review of RAG research, tracing developments from early retrieval-enhanced pretraining frameworks to adaptive and self-reflective architectures. We examine how retrieval strategies including dense passage retrieval (DPR), sparse retrieval, and hybrid methods interact with generative components to suppress hallucination. We analyze the Knowledge-Intensive Language Tasks (KILT) benchmark and open-domain question answering (QA) datasets as primary evaluation vehicles, synthesizing empirical evidence demonstrating that RAG consistently lowers factual error rates relative to purely parametric LLMs. We further discuss challenges including retrieval quality, knowledge conflict resolution, multi-hop reasoning, and domain adaptation, and outline future directions essential for realizing the full potential of RAG in high-stakes natural language generation (NLG) applications.
Application Scenarios and Creation Paradigms of Artificial Intelligence in Digital Media Design
The rapid development of artificial intelligence technology is profoundly changing the creative logic and application scenarios of digital media design. Through algorithm learning, image generation, semantic recognition and other technologies, artificial intelligence not only realizes efficient assistance and creative generation in the fields of plane vision, film and television animation, virtual image, etc., but also promotes the transformation of design thinking from tool dependence to intelligent co-creation. This paper takes the typical application of artificial intelligence in digital media design as the research object, discusses its influence and reconstruction path in creative generation, interactive experience and educational enlightenment, and provides reference for intelligent transformation of digital media design.
Adaptive AI Tutors Scale Personalized STEM Education Across Diverse Learner Populations
The persistent challenge of delivering individualized instruction across heterogeneous learner populations in science, technology, engineering, and mathematics (STEM) contexts has driven significant interest in artificial intelligence (AI)-powered adaptive tutoring systems. This paper examines how adaptive AI tutors can be effectively scaled to meet the cognitive, motivational, and demographic diversity of students engaged in STEM learning. Drawing on a mixed-methods research design that integrates machine learning-based knowledge tracing, reinforcement learning (RL)-driven content sequencing, and learner profile modeling, this study evaluates an adaptive intelligent tutoring system (ITS) deployed across three secondary and tertiary STEM courses involving a combined cohort of 847 students. Quantitative findings indicate statistically significant improvements in learning gains, task completion rates, and knowledge retention among students who engaged with the adaptive system compared to those in conventional instructional settings. Qualitative data further reveals that learners from under-resourced backgrounds and high-achieving students both reported increased perceived relevance and self-regulatory engagement when the system dynamically adjusted content difficulty and scaffolding level. The study also identifies equity-related concerns regarding data sparsity for underrepresented learner groups and proposes mitigation strategies anchored in fairness-aware machine learning. These findings contribute to an emerging understanding of how scalable adaptive AI tutoring can bridge achievement gaps in STEM education without sacrificing instructional depth or pedagogical coherence.
When AI Meets Cache: Intelligent Memory Hierarchy Management in Modern CPUs
The persistent and widening gap between processor execution speed and memory access latency—commonly termed the "memory wall"—has made efficient cache management one of the most consequential challenges in modern computer architecture. Traditional heuristic-based strategies such as least recently used (LRU), re-reference interval prediction (RRIP), and signature-based hit prediction (SHiP) have long served as foundations of cache control, yet they fundamentally fail to capture complex, application-specific, or workload-diverse memory access patterns. The rapid maturation of artificial intelligence (AI) and machine learning (ML) techniques now offers transformative opportunities to reimagine every tier of the memory hierarchy—from L1 instruction caches to last-level cache (LLC) partitioning and speculative hardware prefetching. This review surveys the state of the art at the intersection of AI and cache management, examining techniques including deep reinforcement learning (RL), long short-term memory (LSTM) networks, transformer-based sequence models, and graph neural networks (GNN) as applied to the three principal problem domains of cache replacement, hardware prefetching, and adaptive resource partitioning. We critically analyze trade-offs between prediction accuracy, inference latency, hardware overhead, and practical deployability, synthesizing empirical findings from recent simulation studies and silicon implementations. Key open challenges are identified, including online learning constraints, workload generalization, and the integration of AI inference within microarchitectural timing budgets.
Machine Learning Techniques for Building Resilient and Adaptive Supply Chain Systems
Supply chain systems face unprecedented disruptions from global uncertainties, requiring resilient and adaptive management strategies. Machine learning (ML) techniques offer transformative solutions for enhancing supply chain resilience through predictive analytics, real-time optimization, and intelligent decision-making. This review paper examines the state-of-the-art ML approaches applied to building resilient supply chain systems, including deep learning (DL), reinforcement learning (RL), and ensemble methods. The paper explores how ML techniques address critical challenges such as demand forecasting, risk management, inventory optimization, and disruption recovery. By analyzing recent developments in ML-powered supply chain resilience, this review identifies key applications across various industries including manufacturing, retail, and logistics. The synthesis reveals that ML techniques significantly improve supply chain adaptability by enabling proactive risk mitigation, dynamic resource allocation, and rapid response to disruptions. However, challenges remain in data quality, model interpretability, and integration with existing systems. This comprehensive review provides insights for researchers and practitioners seeking to leverage ML for creating more resilient and adaptive supply chain networks in an increasingly volatile business environment.
Design of a Fully Automatic Circuit Board Pick-and-Place Robot
To resolve manual soldering difficulties encountered by university students, electronics enthusiasts, and small-batch customized users in circuit board assembly, this paper presents the design and implementation of a multifunctional fully automatic circuit board pick-and-place robot. The system employs an Arduino2560 or a self-developed mainboard as the control core, integrating Surface Mount Technology (SMT), modular mechanical structures, and OpenPnP software control to achieve automatic picking, inspection, and placement of electronic components. For surface-mount device handling, the system utilizes a servo positioning mechanism and an intelligent placement head for efficient circuit board assembly. Furthermore, the system supports 3D-printed feeder expansion and can integrate laser cutting and solder paste dispensing functions. Experimental results indicate that the placement relative error for miniature components is within 3%, significantly enhancing small-batch assembly efficiency. With advantages including low cost, ease of DIY customization, and simplified maintenance, the proposed solution demonstrates feasibility and practicality, offering a low-cost and highly flexible intelligent approach for electronic product assembly.
Modern Approaches to LLaMA Fine-Tuning: Parameter-Efficient Methods for Targeted Domain
The emergence of Large Language Models (LLMs) has revolutionized natural language processing across numerous domains. However, adapting these models to specialized applications while maintaining computational efficiency remains a significant challenge. This study presents a comprehensive analysis of parameter-efficient fine-tuning (PEFT) methods for LLaMA models, focusing on their application to healthcare, government, ocean science, and financial services. We evaluate Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), LongLoRA, and full fine-tuning approaches across multiple dimensions including computational requirements, memory usage, and domain-specific performance. LoRA achieves a 9-fold improvement in training efficiency while maintaining comparable performance, with only 1.2M additional parameters for LLaMA 7B models [1]. Healthcare applications showed 13-35% AUROC improvements [2], while software engineering tasks achieved 34-56% solve rates [3]. Memory optimization reduced peak GPU usage from 64GB to 37GB, with potential cost savings of up to 190x compared to commercial alternatives. These findings provide crucial insights for practitioners seeking to deploy LLMs efficiently in specialized domains while maintaining high performance standards.
Large Language Models for Cybersecurity Intelligence, Threat Hunting, and Decision Support
Large language models (LLMs) have emerged as transformative technologies in cybersecurity, offering unprecedented capabilities in threat detection, vulnerability analysis, and intelligent decision-making. This review examines the application of LLMs across critical cybersecurity domains, including cyber threat intelligence (CTI), threat hunting, vulnerability detection, malware analysis, and decision support systems. The integration of LLMs such as Generative Pre-trained Transformer 4 (GPT-4), Bidirectional Encoder Representations from Transformers (BERT), Large Language Model Meta AI (LLaMA), and domain-specific models like SecureFalcon has demonstrated remarkable potential in automating complex security tasks, enhancing analyst productivity, and enabling proactive defense mechanisms. However, the deployment of LLMs in cybersecurity contexts introduces unique challenges, including prompt injection vulnerabilities, data poisoning risks, hallucination concerns, and ethical considerations regarding adversarial use. This paper synthesizes recent research advances, evaluates current LLM architectures and their security applications, examines real-world implementation challenges, and identifies critical gaps requiring further investigation. Through comprehensive analysis of over sixty recent studies, we highlight how LLMs are reshaping cybersecurity practices while emphasizing the necessity for robust security frameworks, continuous model validation, and responsible deployment strategies to mitigate emerging risks associated with these powerful artificial intelligence (AI) systems.
Copyright (c) 2025 Computer Life https://creativecommons.org/licenses/by-nc/4.0
Dual DoF Self-adaptive Bionic Ankle’s Spring-Cable System Design and Dynamic Simulation
This study focuses on the design and dynamic simulation of a spring-cable-driven system for a dual-degree-of-freedom (2-DOF) adaptive bionic ankle joint. The research aims to overcome the limitations of conventional ankle joint designs in complex terrains, including insufficient stability caused by single-degree-of-freedom configurations and inadequate real-time terrain adaptability. To address these challenges, the study developed a biomimetic ankle model capable of replicating the sophisticated motion patterns of human ankle joints. The proposed design incorporates a lightweight cable-driven mechanism with remotely positioned actuators. Through systematic simulation and optimization using ADAMS dynamic simulation software, key parameters were determined: a spring stiffness coefficient of 900 N/m, ground contact angle of 20°, and motor rated speed of 250 rpm.
Intelligent Q&A application of rules and regulations based on large models and RAG technology
Employees face high learning costs and inefficient retrieval due to the increasing volume and complexity of company rules and regulations. This paper proposes an intelligent Q&A system using OCR, vectorization, Large Language Models (LLM), and Retrieval-Augmented Generation (RAG). We detail the knowledge base construction, prompt engineering, and Q&A module design. Example validation demonstrates the system’s accuracy, completeness, and logical output. Results show it significantly improves employee learning efficiency, offering an innovative solution for rules & regulations informatization.
Adaptive Reinforcement Learning with Cross-Modal Attention for Anomaly Detection
Traditional anomaly detection systems often struggle with evolving patterns and multimodal data environments, limiting their effectiveness in dynamic real-world scenarios. This paper presents a novel framework that integrates Adaptive Reinforcement Learning (ARL) with Cross-Modal Attention (CMA) mechanisms for enhanced anomaly detection capabilities. The proposed Adaptive Reinforcement Learning with Cross-Modal Attention (ARL-CMA) framework employs a hierarchical attention architecture combined with Deep Q-Network (DQN) processing to adaptively learn from multimodal sensory inputs including temporal, spatial, and categorical data streams. Our approach addresses the critical challenges of temporal dependency modeling, cross-modal feature alignment, and dynamic threshold adaptation in anomaly detection systems. The framework incorporates a two-level attention mechanism operating at word-level and sentence-level granularities that enables selective focus on discriminative features across different data modalities, while the reinforcement learning component utilizes convolutional neural architectures for continuous adaptation of detection strategies based on environmental feedback. Experimental evaluations demonstrate significant improvements in detection accuracy, with ROC curve analysis showing superior performance compared to existing state-of-the-art methods including binary classifiers, dictionary-based approaches, and autoencoder techniques. The ARL-CMA framework achieves substantially higher true positive rates across all false positive rate ranges, establishing its effectiveness for practical anomaly detection applications in complex, multimodal environments.
LLM-Enhanced Financial Appraisal of Mechanical Carbon Capture and Storage Systems through Automated Technical-Economic Analysis
The escalating deployment of mechanical carbon capture and storage (CCS) technologies demands sophisticated financial appraisal methodologies that can accurately assess the complex technical-economic relationships inherent in these capital-intensive systems. This research presents a novel Large Language Model (LLM) enhanced framework for automated financial analysis of mechanical CCS projects, addressing critical gaps in traditional valuation approaches through advanced natural language processing, automated data synthesis, and intelligent financial modeling capabilities. The proposed system integrates state-of-the-art LLM architectures with domain-specific financial engineering principles to provide comprehensive technical-economic analysis that encompasses capital expenditure optimization, operational cost modeling, revenue stream quantification, and risk assessment across diverse CCS technologies including Direct Air Capture (DAC), point-source capture, and geological storage systems. Through extensive evaluation across 156 CCS projects representing $47.2 billion in total capital investment, our LLM framework demonstrates remarkable improvements in financial analysis accuracy by 34.7%, analysis time reduction of 78%, and risk assessment precision enhancement of 42.3% compared to traditional financial modeling approaches. The system successfully processes complex technical documentation including engineering specifications, environmental impact assessments, regulatory compliance reports, and market analysis data to generate detailed financial projections with confidence intervals and sensitivity analyses. Real-time market data integration enables dynamic updating of financial models based on evolving carbon credit prices, technology costs, and regulatory frameworks, with model recalibration completed within 2.7 hours compared to weeks required for manual analysis updates. The framework incorporates advanced uncertainty quantification through Monte Carlo simulation enhanced with LLM-generated scenario analysis, providing probabilistic financial projections that account for technology performance variations, market volatility, and regulatory changes. Automated report generation capabilities produce investment-grade financial documentation that satisfies due diligence requirements for institutional investors while providing interactive dashboards for real-time project monitoring and performance tracking. Validation against actual CCS project outcomes demonstrates superior predictive accuracy with mean absolute percentage errors below 8.3% for capital cost estimation and 11.7% for operational expense forecasting across 24-month prediction horizons.
Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities
The increasing complexity of urban energy systems necessitates sophisticated coordination mechanisms between smart grids and building energy management systems to achieve optimal energy efficiency and sustainability goals. This research presents a novel Multi-Agent Reinforcement Learning (MARL) framework designed to facilitate coordinated energy management across interconnected urban communities. The proposed system employs distributed intelligent agents that operate autonomously while maintaining collaborative decision-making capabilities for energy distribution, consumption optimization, and demand response coordination. Through extensive simulation studies conducted across three metropolitan areas involving 1,247 residential and commercial buildings over a 12-month period, our findings demonstrate significant improvements in energy efficiency, with average reductions of 23.4% in peak demand loads and 18.7% in overall energy consumption costs. The MARL approach exhibits superior performance compared to traditional centralized control systems, particularly in handling dynamic load balancing and renewable energy integration challenges. These results contribute substantially to the advancement of sustainable urban energy infrastructure and provide practical insights for large-scale smart city implementations.
Frequency-Stabilized Laser Utilizing Acetylene Molecular Absorption Lines as Frequency References
Frequency-stabilized lasers, as core components in precision optical systems and communication networks, demonstrate direct correlations between their frequency stability and critical performance metrics such as optical communication capacity and spectral measurement resolution. While conventional ultra-stable optical cavity solutions achieve exceptional stability, their practical implementation faces inherent limitations due to complex temperature control requirements and vibration isolation demands, hindering cost-effective miniaturization for industrial applications. Acetylene molecules, leveraging their intrinsic absorption spectral lines in communication bands, offer novel opportunities for developing compact frequency-stabilized lasers. This paper presents a new stabilization system based on acetylene molecular absorption characteristics. Through optimized optical path design and improved feedback control architecture, effective laser frequency locking in communication bands has been achieved, providing a cost-efficient stabilization solution for applications in fiber-optic communications and quantum sensing technologies.
Analysis of vehicle and pedestrian recognition algorithm based on improved YOLO
This paper expounds a vehicle pedestrian recognition algorithm based on improved YOLO. It analyzes the shortcomings of current algorithms and proposes improved schemes such as Feature Pyramid Network (FPN) and loss function adjustment. It extracts multi-layer image features through convolutional layers, performs feature fusion and multi-scale detection, and introduces Focal Loss instead of the original classification loss on the basis of the existing loss function to improve the current detection performance and solve the problem of inaccurate recognition of small targets.
Surface defect detection algorithm for special-shaped electronic components based on improved RT-DETR
The detection of surface defects of special-shaped electronic components is a key link to improve the level of insertion technology of special-shaped plug-in machines. Traditional manual detection is susceptible to human subjectivity, the efficiency of template matching algorithm is low, and in the case of insufficient sample data, the existing deep learning technology has problems such as low accuracy and insufficient real-time performance in defect detection. In order to improve the accuracy and real-time performance of special-shaped component detection, this paper improves the object detection model RT-DETR and proposes a real-time multi-dimensional feature adaptive network (RT-MDAFNet): Firstly, the Adaptive Fusion Pyramid Network (AFPN) is designed at the feature fusion layer of the model, and the adaptability and feature extraction ability of the model to multi-scale targets are improved through the dynamic channel attention mechanism and selective feature fusion mechanism. Then, the adaptive channel-spatial aggregation network module (SASE-RepNet) was designed to improve the detection accuracy and efficiency in complex backgrounds by combining multi-level feature aggregation, channel adaptive weight allocation and spatial selectivity enhancement mechanism. In the absence of existing datasets, a dataset of special-shaped electronic components was constructed, and the RT-MDAFNet model was compared with 8 baseline models, including DETR, Faster R-CNN, YOLO series, etc.
Detection of irregular-shaped electronic components
Research on Real-Time Detection Method for River Floating Objects Using UAVs Based on Lightweight YOLOv8s Model
To address the challenge of identifying small floating objects in river debris detection, this study proposes an enhanced model that integrates YOLOv8s with Large Separable Kernel Attention (LSKA) and Asymmetric Kernel Convolution (AKConv). The optimized architecture reduces the parameter count of YOLOv8s from 11.1M to 8.3M while improving mAP50 from 68.0% to 77.9% on the "Water Surface Floating Object Dataset" developed by the China Institute of Water Resources and Hydropower Research. Experimental results demonstrate that the proposed method significantly enhances detection accuracy, strengthens occlusion recognition capability, and improves network adaptability to complex river environments. This advancement effectively upgrades the real-time monitoring performance of UAVs in riverine applications, providing an efficient solution for water resource management.
Multi-Objective Optimization of Aluminum Plate Passive Shielding Structure for EV-WPT Systems Based on Grey Wolf Optimization Algorithm
With the rapid development of electric vehicle wireless power transfer (EV-WPT) technology, human electromagnetic safety issues caused by leakage magnetic fields have become a critical challenge restricting its industrialization. This study focuses on optimizing the aluminum plate passive shielding structure of EV-WPT systems to balance electromagnetic shielding performance and energy transmission efficiency. An electromagnetic simulation model of the EV-WPT system (with a 11kW power capacity and dual LCC compensation circuit) was established, and the principle of metal shielding based on the eddy current effect was analyzed. The Grey Wolf Optimization (GWO) algorithm was employed for multi-objective optimization, targeting minimization of magnetic induction intensity (leakage magnetic field) and maximization of transmission efficiency. Results show that the optimized aluminum plate structure effectively reduces leakage magnetic field while maintaining high transmission efficiency. This research provides a scientific basis for enhancing electromagnetic safety and promoting the safe application of EV-WPT technology.