Highlights in Science, Engineering and Technology
Highlights in Science, Engineering and Technology is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of science, engineering and technology.
Multimodal Streaming Speech Synthesis and Zero-Sample Clone Framework for Smart Education
With the deepening of the digital transformation of education, smart education has put forward higher requirements for natural and expressive real-time voice interaction technologies. However, traditional speech synthesis (TTS) systems face core challenges such as insufficient understanding of professional domain terms, monotonous emotional expression, and the lack of cross-modal collaboration in educational scenarios, making it difficult to meet the needs of immersive and interactive teaching. To overcome these limitations, this paper proposes a multimodal end-to-end streaming speech synthesis and intelligent processing framework for educational scenarios. Firstly, this framework builds a multimodal fusion network based on cross-modal attention mechanisms, which dynamically aligns text semantics, acoustic features, and speaker identity at multiple scales of phonemes, syllables, and sentences, significantly improving the naturalness and semantic consistency of the synthesized speech. Secondly, in terms of personalized speech modeling, the system introduces zero-sample speech cloning technology that integrates semantic understanding of large language models (LLMs) and progressive fine-tuning strategies, enabling high-fidelity replication of teacher-specific voice and cross-language synthesis with only a few seconds of audio samples. To meet the low latency requirements of real-time classroom interaction, the architecture integrates a streaming generation engine based on Chunk-Aware Causal Flow Matching, effectively supporting generation and transmission simultaneously, strictly controlling the system's end-to-end latency within 150 milliseconds. Experimental verification and system analysis show that this multi-task joint optimization framework can precisely handle speechization of complex subject content, adaptively adjust teaching emotional expression, and provide a solid multimodal speech technology foundation for building a highly inclusive and personalized intelligent education ecosystem.
Multimodal fusion; streaming speech synthesis; zero-sample speech cloning; large language model; smart education.
Copyright (c) 2026 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
Steel Surface Defect Detection Algorithm Based on Dual Attention Mechanism Fusion with YOLOv12
To address the strong reliance on traditional manual vision and the frequent missed detection of subtle defects against complex backgrounds in steel surface defect detection, this paper proposes an improved detection algorithm—CA-ViT-YOLOv12—based on dual attention mechanism fusion. Built upon the YOLOv12 framework, the algorithm innovatively integrates a Coordinate Attention (CA) module and a Vision Transformer (ViT) module. The CA module dynamically captures critical features across spatial and channel dimensions, enhancing precise localization of minute flaws; the ViT module leverages self-attention to establish global semantic dependencies, effectively overcoming the limited receptive field bottleneck of conventional convolutional neural networks and improving robustness under multi-texture interference and illumination variation. Experiments on a dataset comprising 5,000 real-world steel surface images from diverse scenarios show that the CA-ViT-YOLOv12 fusion model achieves a mean Average Precision (mAP@0.5) of 0.809 across all categories—significantly outperforming baseline methods—and provides reliable foundational algorithmic support for automated, high-precision quality inspection in steel production lines.
Steel defect detection, YOLOv12, Computer vision, Coordinate attention
Story Generation in Multi-Agent Systems: A DynamicCollaboration Framework Based on Reinforcement Learning
In artificial intelligence, multi-agent systems excel in complex tasks requiring coordination. This paper presents a Multi-Agent Dynamic Collaboration Framework for story generation, tackling narrative coherence, diversity, and adaptability. Using GPT-2 agents for plot, dialogue, and description, a Transformer-based coordinator dynamically adjusts contributions via multi-head attention. PPO reinforcement learning optimizes behaviors with rewards based on BLEU, ROUGE, and entropy. Trained on ROCStories, the framework achieves BLEU 0.85, ROUGE-L 0.65, and perplexity 12.5, surpassing baselines. Human assessments show improved coherence and engagement. This advances AI creative writing and enables scalable applications in education, entertainment, and marketing for immersive narratives.
Multi-Agent Systems; Story Generation; Reinforcement Learning; Transformer, Narrative AI.
Vehicle classification under real-world conditions: A comparative analysis of CNN and EfficientNet
This paper uses deep learning techniques for vehicle type classification in real world traffic situations. A dataset of 300 images of cars, trucks, and buses was created. Images were resized to 224x224 pixel dimensions. For deep learning models, images were preprocessed by way of flipping, rotating, and changing brightness to diversify the dataset. For this study, we used compared two deep learning methods: A simple convolutional neural network, and in this case, EfficientNet , which involves transfer learning. During this study, the basic CNN was able to classify images with 68 % accuracy while EfficientNet was able to classify images with 96 % accuracy. This research strongly indicates, and it is to be expected, that the selection of advanced network designs aids in the classification of vehicles. More work needs to be done in optimizing the model’s reliability and practicality, and this can be done by increasing the size of the dataset, adding complexity to the data, and testing with different network designs.
Vehicle classification; convolutional neural network; EfficientNet; deep learning.
Infrared Interference Thickness Inversion Based on Hilbert Analytic Signal and Full-Spectrum Phase Regression
This paper proposes a unified algorithmic framework that integrates physical modeling and signal processing for the stable extraction of interference fringe information and the inversion of epitaxial layer thickness in infrared reflectance spectroscopy. First, an optical interference model is established based on phase difference theory, explicitly linking the thickness parameter to the phase-wavenumber relationship, and unifying the description of model parameters through a refractive index correction term. At the signal processing level, Savitzky–Golay filtering is employed for baseline subtraction, and the Hilbert transform is utilized to construct an analytical signal, thereby separating the amplitude and phase of the interference signal and converting the original oscillatory structure into a continuous phase representation. Building on this, phase expansion and linear fitting methods are introduced to propose a full-spectrum phase regression model, transforming the thickness estimation into a global parameter solving problem and thereby reducing dependence on local features. Furthermore, a dual-path inversion mechanism is constructed by combining extreme value spacing calculation methods, and the stability of the results is enhanced through statistical averaging of multiple sets of fringes. Concurrently, a multi-beam interference expansion expression based on the Airy model is introduced, enabling the algorithm to maintain a consistent computational form under varying reflection conditions. Experimental results demonstrate that this method exhibits excellent numerical consistency and noise resistance under diverse data conditions, and the overall framework possesses strong versatility and scalability.
Hilbert Analytic Signal; Full-Spectrum Phase Regression; Savitzky–Golay Filtering.
Supply Chain Efficiency Evaluation and Informatization-Driven Mechanism Based on a Multi-Model Integrated Framework
This paper constructs an analytical framework based on multi-model fusion for the evaluation and optimization of supply chain operational efficiency. First, multidimensional input and output data undergo extreme value normalization to eliminate dimensional differences and enhance the stability of results. Building on this foundation, the Data Envelopment Analysis (DEA) model is introduced to measure the relative efficiency of each link as an independent decision-making unit, thereby enabling an overall performance evaluation under multi-input–multi-output conditions. Furthermore, the efficiency results are transformed into binary variables and combined with a logistic regression model to characterize the probability of key factors influencing high-efficiency states, thereby revealing the marginal mechanisms of these variables. To examine dynamic changes over time, a fixed-effects regression model is constructed to control for unobservable individual differences, enhancing the interpretability and robustness of the results. Empirical results indicate that IT investment exhibits a consistent positive effect across different models and demonstrates higher sensitivity in the processing stage. The proposed method possesses good structural generality and can be applied to efficiency evaluation and the identification of driving factors across multiple scenarios, providing an extensible modeling approach for the collaborative analysis of multidimensional data in complex systems.
Data Envelopment Analysis, Logistic Regression, Fixed Effects Model.
A Multi-Model Fusion and Risk Optimization Approach for Non-Invasive Prenatal Testing Based on Random Forest and K-means Clustering
This paper focuses on the complex relationship between Y-chromosome concentration and multiple factors in non-invasive prenatal testing, and constructs a methodological framework that integrates multiple models. First, to address the significant nonlinear characteristics among variables, a random forest model is introduced to assess feature importance, identify key influencing factors, and enhance model robustness. Building on this foundation, segmented polynomial regression is employed to effectively improve overall fitting performance through interval partitioning and local modeling. Furthermore, the concentration threshold is transformed into a risk minimization problem to construct an optimization model. K-means clustering is utilized to achieve joint optimization of grouping and testing timing, thereby reducing overall risk while ensuring structural stability. Additionally, to address class imbalance, SMOTE is introduced for data augmentation, and a classification model is established using Gradient Boosted Decision Trees (GBDT) to effectively identify abnormal samples. Overall, this method integrates Random Forests, piecewise regression, clustering, and ensemble learning models, demonstrating strong generalization capabilities and stability, and holds significant practical value in complex data modeling and risk control.
Random Forest, K-means Clustering, Gradient Boosting Decision Tree.
A Unified System Framework for Hybrid Allocation Optimization and Monte Carlo Reliability Simulation in the Logistics
This paper constructs a unified optimization modeling framework for Earth-Moon cargo transportation and proposes a method that integrates physical mechanism modeling with multi-source transportation coordination and allocation. By establishing a hybrid transportation model that combines elevators and rockets, and introducing an allocation algorithm based on the superposition of throughput capacities, the framework achieves the coordinated optimization of transportation time and cost. Building on this foundation, reliability modeling is incorporated, transforming equipment availability, operational efficiency, and failure probability into a utilization function, while a multi-level stochastic perturbation structure is used to characterize system fluctuations. To analyze the impact of uncertainty, discrete-time Monte Carlo simulation is employed to dynamically evaluate the transport process, enabling the quantitative characterization of delay risks. Concurrently, a capacity margin constraint model is constructed, and key influencing factors are identified using surrogate functions. At the multi-objective level, a coupled model of transportation allocation and emissions is introduced, and environmental impact assessments are conducted through stochastic simulation. Finally, a unified allocation strategy that integrates time, reliability, and environmental constraints is proposed. This strategy offers excellent scalability and general applicability, providing methodological support for resource allocation and risk control in complex transportation systems.
Hybrid Allocation Optimization, Multi-objective Optimization, Simulation-driven Decision-making.
A Multi-Objective Optimization Framework for Space Transportation Systems Based on Mixed-Integer Linear Programming and Monte Carlo Simulation
This paper establishes a multi-objective optimization analysis framework for large-scale transportation system planning. Based on a multi-stage mixed-integer linear programming (MILP) model, it models the system under constraints of transport capacity, infrastructure scale, and operational conditions, with transportation costs and project duration as primary optimization objectives. By introducing the ε-constraint method, the multi-objective problem is transformed into a single-objective solution, and a Pareto frontier between cost and construction duration is constructed to identify optimal configuration schemes under different transportation strategies. To extend the model, operational factors such as maintenance cycles, system capacity ramp-up, and learning curves are incorporated to dynamically characterize changes in transportation capacity and costs. A digital twin analysis framework is further developed by integrating discrete-event simulation with Monte Carlo simulation to evaluate random disturbances like equipment failures and transportation delays. The Conditional Value at Risk (CVaR) method is employed to measure system risks under extreme scenarios. Concurrently, rolling time-domain optimization enables dynamic adjustments to transportation decisions. Research findings demonstrate that this methodology achieves coordinated optimization and risk control for transportation systems under complex constraints and uncertainty, providing a universally applicable optimization modeling and simulation analysis approach for planning complex engineering transportation systems.
mixed-integer linear programming, Monte Carlo simulation, multi-objective optimization.
Multi-UAV Cooperative Smoke-Screen Obscuration Strategy Based on LOS Geometric Occlusion Evaluation and PSO
This paper addresses smoke screen occlusion protection tasks in multi-missile attack scenarios by establishing a universal solution framework that integrates geometric determination with intelligent optimization. First, motion models are developed for missiles, UAVs, and smoke grenades, abstracting targets as cylinders and describing missile observation paths via discretized line-of-sight (LOS) segments. Subsequently, an occlusion criterion based on the minimum distance between LOS segments and spherical smoke clouds is proposed, and the effective duration is computed via time discretization with boundary refinement. Under coordinated multiple smoke grenade conditions, the occlusion region is further modeled as the union of multiple spherical smoke clouds, and strategy optimization is achieved by maximizing cumulative occlusion time. For multi-target scaling, a Max-Min robust fairness objective is introduced to ensure stable performance of the protection strategy even under the most unfavorable attack direction. Finally, a Particle Swarm Optimization (PSO) algorithm is employed for global search of mixed continuous-discrete variables, with a feasibility repair mechanism designed to guarantee constraint satisfaction.
Particle Swarm Optimization, Multi-UAV Cooperative Optimization, Robust Fairness Optimization.
Inverse Reconstruction of Latent Voting and Rule Impact Evaluation Based on Genetic Algorithm and Monte Carlo Counterfactual Simulation
This paper addresses the issue of unobservable fan voting in competitive reality shows by constructing a comprehensive modeling framework based on reverse inference and random simulation. For scenarios where voting proportions are latent variables and elimination mechanisms exhibit nonlinear ranking characteristics, the voting reconstruction is formalized as a constrained high-dimensional optimization problem. A genetic algorithm is employed for global search to estimate the full-season voting distribution under normalization conditions, enabling robust inversion of the implicit voting structure. Building upon this foundation, a Logistic probability model integrating judge scores and estimated voting characteristics is constructed to predict and validate elimination outcomes. Bootstrap intervals and cross-validation are employed to enhance model stability. Furthermore, a Monte Carlo-based counterfactual simulation framework is proposed to propagate voting uncertainty across different scoring rules. Rule deviations and system sensitivity are quantified using fan influence indices and outcome variance rates. A dynamic simulation model incorporating multi-week state transitions and intervention mechanisms is constructed to analyze the long-term impact of rule adjustments on competitive trajectories. This methodology achieves a unified modeling approach for latent variable reconstruction, probabilistic prediction, and institutional evaluation, demonstrating strong universality and scalability.
Genetic Algorithm, Latent Variable Reconstruction, Counterfactual Simulation.
The Impact of Machine Learning and AI on Bioinformatics
The rapid growth of biological data has fundamentally transformed bioinformatics, challenging the effectiveness of traditional methods. This paper examines the evolving role of AI and ML in addressing the limitations of conventional methods such as HMMs and sequence alignment tools. Unlike rule-based approaches, ML techniques enable scalable, data-driven analysis capable of capturing complicated and nonlinear relationships within genomic and multi-omics data. Key applications, including genomic sequence classification, disease prediction, and multi-omics integration, demonstrates the significant advantage of machine-driven methods in improving predictive accuracy and expanding analytical scope. However, challenges such as model interpretability and computational demands remain critical concerns, prompting the development of AI frameworks that are explainable. This paper argues that rather than replacing traditional methods, AI and ML should serve as complementary tools that enhance the overall analytical framework. Future directions highlight the potential of generative models and decision-based machine systems to further advance biological discovery and precision medicine.
Bioinformatics, Machine Learning, Artificial Intelligence, Genomics, Multi-Omics Integration, Disease Prediction, Deep Learning, Explainable AI, Precision Medicine.
Random Walk in Graphs Analyzing and Applications
As a fundamental topic in probability theory, the random walk has a wide range of applications in real-world scenarios. It is widely employed not only in financial and economic fields such as stock price forecasting and video game design, but also in more abstract domains including graph analysis, especially network graph analysis. This study demonstrates that the random walk is effective in dealing with network graph–related problems. During the experience, MATLAB is used to test the ability of random walk in identifying the difference between two graphs, and it shows that random walk shows some advantage in identifying difference between network graphs compared to spectral matching, which has been viewed as authentic method relating to graphs for many years. What’s more, it also shows possibility that when combined with big data models, random walk can further show great potential in investigating the relationships among two or more network graphs.
Random Walk; Graph Analysis; Graph Comparison; Structural Change Detection; PageRank; Spectral Matching.
From Limitations to Future: ML/AI in Advancing BCI
Brain-computer interface (BCI) technology enables direct communication between the human brain and external devices and has broad applications in rehabilitation, assistive communication, and human-computer interaction. However, traditional EEG-based BCI methods, including spatial filtering and time-frequency analysis, are limited by low signal-to-noise ratio, non-stationary signals, artifact interference, manual feature extraction, and weak adaptability across individuals. This paper analyzes these limitations and discusses how machine learning and artificial intelligence can improve BCI systems. Convolutional neural networks can automatically extract spatial-temporal EEG features, attention mechanisms can strengthen task-relevant channels and frequency bands, lightweight convolutional models can support real-time decoding, and transfer learning with data augmentation can improve individual adaptation. These methods help enhance the accuracy, robustness, and practicality of BCI systems. With further development, ML/AI-based BCI technologies are expected to play an increasingly important role in medical rehabilitation, daily assistance, and intelligent human-computer interaction.
Brain-Computer Interface; EEG; Machine Learning; Artificial Intelligence; Signal Processing; Deep Learning.
Using AI Innovations to Improve Accuracy and Reliability in Medical Testing
While medical testing serves as a vital pillar of modern clinical diagnostics, conventional methodologies—most notably fixed-threshold models and frequentist statistics—often struggle to account for data noise and inherent biological variability. These rigid, "one-size-fits-all" standards frequently lead to diagnostic misjudgments and fail to provide a quantified measure of uncertainty in complex scenarios. This paper investigates the role of artificial intelligence (AI) in bridging these gaps to refine both diagnostic precision and reliability. We first examine how Bayesian probabilistic frameworks, rooted in Bayes’Theorem, can shift diagnostics from binary outcomes to personalized probability assessments by accounting for uncertainty. The discussion then moves to the practical application of Deep Learning, particularly Convolutional Neural Networks (CNNs), in automating the detection of pathological features in screenings for conditions such as lung cancer and diabetic retinopathy. Additionally, we explore how transfer learning mitigates the challenge of limited datasets in rare disease research, while sequence models offer insights into longitudinal patient history. By synthesizing these advancements, this paper argues that the integration of AI facilitates a necessary transition toward more robust, individualized diagnostic frameworks within precision medicine.
Clinical Diagnostics; Artificial Intelligence; Bayesian Inference; CNNs; Personalized Medicine; Diagnostic Reliability; Transfer Learning; Medical Imaging Analysis.
The application of the Maximum Likelihood Estimation principle in communication systems
This paper examines the application of Maximum Likelihood Estimation (MLE) in digital communication systems, with particular emphasis on Binary Phase Shift Keying (BPSK) modulation. The discussion opens with an overview of MLE fundamentals and its asymptotic properties—consistency and efficiency—that establish its suitability for optimal signal detection. The central contribution lies in the derivation of an MLE-based detection algorithm for BPSK transmission over an Additive White Gaussian Noise (AWGN) channel, yielding a transparent optimal decision rule. To substantiate the theoretical framework, a full-scale MATLAB R2024b simulation was developed and executed. The simulation encompasses the complete baseband BPSK transceiver: pulse shaping, carrier modulation, AWGN channel impairment, coherent demodulation, and matched filtering. Performance assessment involves comparing empirically measured Bit Error Rate (BER) against theoretical predictions across varying signal-to-noise ratios. The findings indicate that empirical BER approaches the theoretical bound as the number of simulated bits grows, confirming both the validity and practical efficacy of the derived MLE algorithm. These results underscore the continued relevance of MLE in communication signal detection and furnish a concrete reference for both instructional and engineering contexts.
Maximum Likelihood Estimation (MLE); Binary Phase Shift Keying (BPSK); Communication Systems; Bit Error Rate (BER); MATLAB Simulation; Signal Detection; Additive White Gaussian Noise (AWGN) Channel.
An AI-Driven Approach to Green and Resilient Vehicle Routing: A Comparative Study of Genetic Algorithms and Reinforcement Learning
Green logistics has emerged as a pivotal pillar of sustainable supply chain management, especially in dynamic and uncertain operational environments. However, existing research rarely integrates green objectives and supply chain resilience into a unified vehicle routing optimization framework, leaving a critical methodological gap for addressing real-world logistics uncertainties. To bridge this gap, this study aims to propose an AI-driven methodological framework for green and resilient vehicle routing, and conducts a comparative evaluation between Genetic Algorithm (GA) and reinforcement learning (RL) based on the Solomon C101 benchmark dataset. The optimization objectives are to minimize total travel distance, operational cost and carbon emissions under vehicle capacity constraints. Furthermore, dynamic disruption scenarios—including node unavailability, demand surges and vehicle breakdowns—are constructed to assess the system resilience performance. Experimental results demonstrate that RL significantly outperforms GA in static baseline scenarios, with stable and consistent solution quality across independent runs. Under dynamic disruptions, RL exhibits superior adaptive capacity with faster recovery speed and steady solution performance, whereas GA presents pronounced solution variability and noticeable performance degradation. These findings validate the effectiveness of reinforcement learning as a robust and scalable solution for realizing green and resilient logistics in dynamic and uncertain environments.
Green and Resilient Logistics; Dynamic Vehicle Routing; Carbon Emission Reduction; Deep Reinforcement Learning.
Application of Integrated UAV and TLS Point Clouds in Urban Road Renovation Design: A Case Study of Xiangjiang South Road Scenic Corridor in Changsha, China
Urban road renovation projects often face complex site conditions, incomplete elevation information, dense roadside facilities, and limited support from conventional two-dimensional topographic data. To address these issues, this study investigates the integrated use of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) for 3D modeling and renovation design of an urban road corridor. The study area is Section B of Xiangjiang South Road Scenic Corridor in Tianxin District, Changsha, China, where the existing road is narrow, partially damaged, and characterized by mixed motorized and non-motorized traffic. The redesigned corridor is 871.861 m long, classified as an urban secondary arterial road, with a design speed of 40 km/h and a standard cross-section width of 16.5 m. UAV and TLS data were acquired and processed to generate integrated point cloud products for terrain correction and design support. The results show that the integrated point cloud data effectively improved the representation of existing terrain and roadside geometric conditions, especially in areas where the original topographic map contained sparse or unreliable elevation information. The combined dataset provided both corridor-scale terrain continuity and detailed local geometric features, which supported route design, vertical profile design, cross-section definition, drainage layout, pipeline coordination, and lighting design. Compared with a single data source, the integrated use of UAV photogrammetry and TLS provided a more practical 3D basis for road renovation in a constrained urban environment. The study demonstrates that multi-source point cloud technology can serve not only as a visualization tool, but also as an effective pre-design support dataset for urban road renovation engineering.
Urban road renovation; UAV photogrammetry; TLS; point cloud integration.
Research on Credit Empowerment Based on Big Data and Digital Footprints
Under the background of the deep development of the digital economy, the traditional credit evaluation relies too much on asset mortgage and financial reports, which leads to information lag and makes it difficult to cover the "white credit" group. This limitation promotes the transformation of credit evaluation to "predictive insight" driven by big data and digital footprint. This paper systematically discusses this enabling mechanism, covering the technological evolution path from data fusion, feature engineering, to deep learning, graph neural network, and federated learning, and analyzes its wide application in financial credit, social mortgage-free leasing, and metacosmic decentralized identity. The study found that big data empowerment significantly improved the fidelity of risk identification. By giving financial value to the digital trajectory, it eliminated the mortgage bias of traditional credit and created opportunities for inclusive participation for vulnerable groups. At the same time, credit scoring is evolving from a single economic constraint to an "algorithmic contract" that promotes social transformation and digital survival. Despite the challenges of algorithmic discrimination, model black box and privacy paradox, this paper points out that the future credit system should be transformed to a "trusted AI" framework, combining regulatory Technology and privacy computing technology to achieve deep decoding and governance optimization of credit value on the premise of ensuring data sovereignty.
Big Data; Digital Footprints; Credit Empowerment; Financial Inclusion; Algorithmic Governance.
Preface: 5th International Conference on Image Processing and Intelligent Information Systems (IPIIS 2026)
The 2026 5th International Conference on Image Processing and Intelligent Information Systems (IPIIS 2026) was successfully held in Edinburgh, United Kingdom during May 16-17, 2026. In the context of the conference, scholars, and experts in the fields of image processing and intelligent information systems, involved both in experimental and theoretical research, were invited from various universities and institutions. The main goal of IPIIS 2026 was to present the related research work, to promote the cooperation of experts and scholars, to share the research achievements both on a national and international level. This collection of proceedings compiles oral and paper presentations submitted by the authors and scrutinized by the editorial committee. Due to the large number of attendees, the conference was separated into main and parallel sessions. Each plenary speaker gave an oral presentation of 30-40 minutes accompanied by his slides' material, allocated for questions and discussion. The organizing and editorial committees of IPIIS 2026 hope you can enjoy the reading of this volume. Additionally, we want to thank all authors and participants for providing their valuable contributions for these proceedings, as well as the reviewers for their constructive recommendations and feedback aiding to improve the presented articles. IPIIS Organizing Committee Edinburgh, United Kingdom
Self-supervised short-term traffic flow prediction based on diffusion autoregressive Transformer
With rapid urbanization and motorization, traffic congestion restricts urban sustainable development, yet existing short-term traffic flow prediction methods have flaws: Linear Regression fails to adapt to nonlinear features like peak tidal changes; SVM has soaring kernel cost in large-scale data and poor long-term dependency capture; LSTM lags in extreme loads and has high error fluctuation in special events. This study proposes the STD-ARformer, integrating diffusion denoising and autoregressive mechanism. It uses three core designs: dynamic receptive field (adjusts attention window for mutations), traffic flow conservation constraint (ensures physical compliance), and hierarchical denoising (enhances multi-scale robustness).Experiments show STD-ARformer outperforms Linear Regression, SVM, and LSTM in key indicators, alleviates extreme load lag, reduces special event error fluctuation, and lowers medium-flow discreteness. It provides a high-performance solution, supporting traffic management and urban sustainable development.
Short-term Traffic Flow, Diffusion Autoregressive Transformer, Dynamic Sensory Field, Denoising, Saito-temporal Coupling.
Copyright (c) 2025 Highlights in Science, Engineering and Technology https://creativecommons.org/licenses/by-nc/4.0
The establishment of ski jumping model based on particle swarm optimization algorithm and ODE solver
In ski jumping, studying to determine a reasonable run-up posture can effectively reduce air resistance during the run-up phase, thereby increasing sliding speed. However, most current research focuses on kinematic analysis from video perspectives, with fewer studies examining dynamics. Therefore, this paper proposes a maximum speed take-off model based on particle swarm optimization and ODE solver from a dynamic perspective. First, a dynamic model is established using Newtonian mechanics principles, incorporating dynamic factors for attitude control and determining relevant initial parameters. Then, the sliding process is simulated using an ODE solver, and the optimal attitude control parameters are determined using particle swarm optimization. The final model concludes that squatting during the run-up significantly reduces resistance and increases take-off speed. By further considering aerodynamic effects and refining the model by introducing real-time dynamic data, this model can become a core tool for training in winter sports research.
Particle Swarm Optimization Algorithm, ODE Solver, Ski Jumping.
Research on Sustainable Tourism Forecasting Model for Juneau based on AHP-PSO and Dynamic Strategy
Cruise ship tourists have provided significant revenue for the city of Juneau, Alaska. However, with the surge in visitor numbers and rising temperatures, issues such as overcrowding and glacier melting have emerged. Therefore, the development and implementation of sustainable tourism plans are urgent. In response, this study has developed a sustainable tourism forecasting model for Juneau, using particle swarm optimisation algorithms to simulate changes in various indicators from 2025 to 2046, in order to determine the optimal weight allocation. This study holds important theoretical and practical significance. Theoretically, it enriches the research on sustainable tourism models by incorporating dynamic constraints and long-term environmental changes. Practically, it offers a scientific reference for Juneau to achieve sustainable tourism development. The main innovations of this study include the integration of multi-dimensional factors in the model, the consideration of dynamic constraints and long-term environmental changes, and the exploration of cooperative strategies between tourism revenue distribution and infrastructure investment.
Sustainable Tourism Prediction Model, AHP, Dynamic Adjustment Strategy, PSO.
A Study of Sustainable Tourism Development Modeling Based on Multi-Objective Optimization and Machine Learning
This paper aims to promote sustainable tourism by developing a multi-objective optimization-based sustainable tourism model and a visitor flow balancing model, addressing over-tourism challenges in destinations such as Juneau, Yellowstone National Park, Venice, and the Maldives. The first model integrates ARIMA and EMA for forecasting tourist volumes and environmental indicators, employs multiple linear regression to evaluate resident satisfaction, and incorporates a feedback mechanism linking carbon footprint and tourism revenue. The paper further conducted a sensitivity analysis on tourist volume, carbon tax rate, and resident satisfaction, confirming the robustness and adaptability of the sustainable tourism model. The second model utilizes a probabilistic framework to balance visitor flow, exemplified through 12 attractions in Yellowstone. Seasonal patterns are predicted using the SARIMA model, and a variance-mean ratio is minimized to optimize distribution. The models demonstrate strong scalability and practical value for real-world tourism management.
Sustainable tourism model, Visitor flow balancing model, Tourist management, Multi - objective optimization.
A Balanced Optimization Approach to Inspection and Disassembly in Multi-Stage Manufacturing Systems
This study addresses the critical decision-making challenge of optimizing inspection (of production components and finished products) and disassembly (of defective items) strategies within manufacturing systems. Existing research primarily focuses on isolated inspection or disassembly policies, lacking an integrated computational framework. To bridge this gap, we develop a novel multi-stage optimization model capturing the interdependencies between inspection and disassembly decisions. An exhaustive enumeration algorithm is employed to systematically evaluate all feasible decision combinations across stages, with the primary objective of maximizing overall manufacturing throughput capacity. The model explicitly incorporates the impact of defects and resource constraints on system performance. Furthermore, sensitivity analysis is conducted to identify key operational parameters that significantly influence the optimal decision strategy. The results demonstrate that the proposed model and algorithmic approach effectively determine computationally efficient production control strategies, leading to enhanced system throughput and product quality robustness.
Manufacturing Optimization, Multi-stage Decision Making, Exhaustive Enumeration Algorithm, System Throughput, Quality Control.
Optimization of Crop Planting Strategies Based on Multi-Objective Linear Programming
Nowadays, rural farmland is facing the problems of how to plant a wide variety of crops according to local conditions, how to maximize economic benefits, and how to promote sustainable development of the environment. This article is based on the research of crop planting, production, and sales data in a rural area in 2023. Multiple multi-objective linear programming models are established to solve the optimal planting plan for the rural area from 2024 to 2030 under different conditions. Firstly, data preprocessing is carried out to obtain expected sales volume and sales unit price. Observing the data, it is found that the planting of grain crops and vegetable crops are independent. Therefore, two multi-objective linear programming models are established separately. Their objective functions are to maximize crop sales revenue and minimize crop planting costs. The two model constraints mainly include: bean planting frequency constraint, planting area constraint, planting range constraint, planting situation and yield relationship constraint, etc. For vegetable crops, there is a two season planting crop constraint. Four multi-objective linear programming models are used to solve the planting plans for grain crops and vegetable crops in two scenarios: when the actual production exceeds the expected sales volume and when the excess is unsold, and when the excess is sold at a 50% discount to the 2023 sales price.
Multi-objective Linear Programming, Random Parameters, Prediction Model, Big Data.
Research on the Optimal Planting Strategy of Crops Based on Nested Genetic Algorithm
The development of organic farming tailored to the actual conditions of rural areas holds significant practical importance for sustainable rural development. This paper delves into the optimal crop planting strategies under various scenarios, including different sales methods, crop functional interrelationships and uncertainties related to production and costs, market and revenue. Initially, a multi-year total profit optimization model is constructed and solved using a nested genetic algorithm. Subsequently, the Spearman correlation coefficient was used to classify replacement crops and complementary crops to explore the influence of crop functional interrelationships on planting plan and yield. Finally, synthetic aperture radar (SAR) monitoring data is incorporated to assess fluctuations in crop yield per unit area, and the Monte Carlo method is employed to simulate uncertain parameters. Statistical metrics of preselected schemes are analyzed using the TOPSIS method, with the optimal planting strategy ultimately derived by integrating growers' risk preferences.
Crop planting, Nested genetic algorithm, Monte Carlo simulation, TOPSIS, Spearman correlation coefficient.
Study on Indoor Fire Evacuation Path Planning Method Based on the Grey Wolf Optimization Algorithm
In pursuit of enhancing the efficiency of indoor fire evacuation path planning and reducing crowd density, this paper introduces an advanced evacuation path planning method based on the Grey Wolf Optimizer (GWO) algorithm. Initially, a grid-based approach is utilized to model the fire scenario. Subsequently, a path planning problem is formulated that incorporates the effects of CO concentration and crowd density. This model integrates the relationship between crowd density and evacuation speed, employing a Gaussian plume model to quantify the impact of crowd density and smoke hazards on individuals. The GWO algorithm is then introduced to solve this optimization problem, aiming to derive an optimal evacuation plan that comprehensively considers path length, smoke concentration, and crowd density. The model is validated across four distinct environmental scenarios. The experimental results demonstrate that the proposed method effectively identifies the shortest evacuation path and boasts rapid convergence rates while avoiding local optima. In non-fire scenarios, the method reduces the optimization cycle by 10%-25% and shortens path length by 10%-25% compared to other algorithms. During fire incidents, optimization cycles and path lengths are reduced by 10%-20%. These improvements enhance evacuation safety and efficiency while providing flexibility across settings, from indoors to commercial areas. This study offers valuable tools and insights for urban fire emergency management and safety planning.
Indoor Fire, Path Planning, Smoke Environment, GWO Algorithm.
Predicted Potentially Suitable Areas for Ips Typographus in the Northwest Region of China Based on the Maxent Model
As a typical secondary pest in the family Scolytidae of Coleoptera, Ips typographus poses a serious threat to coniferous forest resources in the northwest region of China. This study forecasts the spatial distribution of potential habitats for I. typographus by using the MaxEnt model and GIS technology, based on 28 distribution points of I. typographus and 12 key environmental factor variables in the study area. Through contribution rate analysis, model validation, and superposition analysis of host plants, the driving mechanism of its habitat suitability was evaluated. The outcomes revealed that the mean value of AUC was 0.989, and the MaEent model had an extremely high prediction accuracy. Annual mean temperature (Bio1), precipitation of the driest month (Bio14), temperature seasonality (Bio4), and precipitation of the coldest quarter (Bio19) were determined as the key factors governing the distribution area of I. typographus among the 12 environmental variables used in the modeling. Their respective contribution rates were 31.5%, 26.3%, 9.8%, and 10.2%. These four variables collectively accounted for 77.8% of the total contribution. The northern mountainous areas of the study area are suitable for the spread of I. typographus. For example, Altay and Tacheng regions. The findings of this research could offer theoretical foundations for local forestry authorities to develop corresponding prevention and control measures.
MaxEnt model, Ips typographus, Potential habitat, Environmental factors.
Modeling and Optimization of Crop Planting Schemes under Surplus Disposal Strategies Using Simulated Annealing
Optimized Crop Planning Model for Enhancing Agricultural Economic Benefits in a Mountainous Village of North China, to improve agricultural economic efficiency in a mountainous village in North China, this study develops an optimization model for crop planting planning from 2024 to 2030.Incorporating practical constraints including legume rotation protocols, prevention of continuous cropping, and seasonal field adaptability, the study employs a simulated annealing algorithm to solve the profit maximization problem under two distinct surplus disposal strategies(excess production with inventory loss and discounted clearance of surplus yield). Results: The discounted sales strategy generated significantly higher profits (¥62.9 million) compared to the inventory loss approach (¥22.89 million). Analysis reveals that when surplus yield remains revenue-generating, the optimization model exhibits a strong preference for high-margin crops (e.g., edible fungi), while substantially reducing cultivation area allocated to low-return crops. This study demonstrates the efficacy of simulated annealing algorithms in addressing complex, multi-constrained agricultural planning problems. The findings not only establish a viable pathway for rural sustainable development but also provide a theoretical foundation for subsequent optimization and large-scale implementation.
Crop Planting Optimization, Simulated Annealing Algorithm, Agricultural Planning, Multi-Constraint Modeling, Profit Maximization.