Cable force adjustment in cable-stayed bridges is a multivariable engineering decision problem constrained by both structural accuracy and construction efficiency. Existing studies often adopt fixed optimization paradigms, overlooking the influence of engineering objectives on the structure of optimization models. This study establishes a unified physical mapping between cable force adjustments and structural responses using an influence matrix and proposes an engineering-objective-driven optimization modeling framework. Under the same physical model, accuracy-oriented continuous optimization and efficiency-oriented discrete optimization formulations are clearly distinguished. The former minimizes overall response deviations, while the latter minimizes the number of adjusted cables within allowable error limits. Case studies demonstrate that the two formulations induce dense and sparse optimal solution structures, respectively, due to fundamental differences in decision variables, objective functions, and feasible regions driven by engineering objectives. The results indicate that cable force adjustment should be modeled as an engineering-objective-driven optimization problem, with the optimization paradigm selected at the modeling stage in accordance with engineering intent.
Hybrid Photovoltaic/Thermal (PVT) systems merge photovoltaic and thermal technologies to generate electricity and heat from solar energy at the same time. These systems boost energy efficiency by producing electricity from sunlight and using surplus heat for heating applications, which makes them an eco-friendly and multi-purpose solution for power and heat generation. This study mathematically models a nanofluid-based PVT energy system under varied environmental conditions and utilize different machine learning algorithms to discover complex relationships between system parameters. Therefore, a predictive Elastic Net model optimized through hyperparameter tuning by the Dynamic Differential Annealed Optimization Algorithm (DDAOA) is developed for accurate projection of the system's electrical and thermal energy output. This model demonstrates outstanding performance in predicting the electrical and thermal outputs of the nanofluid-based PVT system with high R 2 of 0.9999 and 0.9749, respectively. Multi-objective optimization using Non-dominated Sorting Genetic Algorithm II is employed in this work to identify the desired execution condition of a hybrid PVT system for both thermal and electrical maximum outputs, which delivers an electricity output of 135.670 W/m 2 and a heat output of 205.620 W/m 2 .
The current digital modeling and cross-platform dissemination of intangible cultural heritage elements face the challenges of low efficiency and accuracy. Existing methods exhibit significant limitations in terms of model fidelity, terminal adaptability, and the effectiveness of feedback loops. In this study, an intelligent modeling and cross-platform collaborative dissemination mechanism based on digital twins (DTs) is proposed to achieve high-fidelity reconstruction, lightweight optimization, and dynamic iteration. An initial DT model is constructed by integrating high-precision 3D scanning and computer vision technologies. A lightweight convolutional neural network, combined with a joint geometry−texture loss function, is designed to automatically inpaint model details and optimize quality. The GL transmission format encapsulation and an API gateway are used to establish data transmission channels, enabling seamless integration of mobile devices, virtual museums, and social media platforms. A personalized recommendation algorithm driven by a graph neural network is introduced, which integrates user behavior data to deliver precise content recommendations. A real-time feedback mechanism based on Kafka and Spark Streaming is constructed to transmit dissemination effect data back to the modeling and recommendation modules, forming a dynamic optimization loop. The experimental results show that this method reduces the Hausdorff distance of the paper-cutting intangible cultural heritage model from 0.858 to 0.631 mm and reduces the cross-platform synchronization delay on entry-level devices from 987 to 512 ms. The proposed method provides a scalable technical path for the intelligent inheritance and global dissemination of intangible cultural heritage.
Exposed to complex environments for extended periods, ultra-high-voltage transmission lines now face lightning strikes as the primary natural disaster responsible for unplanned outages. The ability to accurately estimate the risk of trip‒outs caused by lightning is of great importance for ensuring power grid safety. However, existing methods have problems such as poor adaptability in signal feature extraction and insufficient support for mixed variables in hyperparameter optimization. Therefore, this paper proposes a Bayesian simulation and parameter optimization method for lightning tripping risks in ultra-high-voltage power grids. First, an adaptive wavelet multi-scale feature selection mechanism is designed to achieve precise decoupling of transient impulse components and background noise. Second, a Bayesian Transformer prediction network is constructed to quantify aleatoric uncertainty and cognitive uncertainty. Finally, the hyperparameters of the prediction model are optimized based on the improved Bayesian optimization algorithm. Genetic algorithms and multi-fidelity Gaussian process surrogate models are introduced to reduce the simulation optimization cost. Experimental outcomes indicate that the prediction accuracy and F1 score of the suggested approach are at least 6.35% and 9.1% higher than those of the benchmark method, respectively, verifying its effectiveness in lightning tripping risk prediction.
Complex wind farm environments cause severe spatial aliasing and signal attenuation in blade acoustic signature monitoring. This paper presents an acoustic sensor array topology optimization method based on multi-physics simulation for high-fidelity acquisition of weak voiceprint features. A three-dimensional sound field model coupling aerodynamic noise and mechanical vibration quantifies sound propagation under varying wind speeds and yaw angles. A heuristic particle swarm algorithm discretely optimizes microphone array coordinates on tower and nacelle surfaces by maximizing the signal-to-noise ratio. A surrogate model accelerates sound field evaluation while eliminating nodes disturbed by strong wind vortices. Experiments on a public blade crack acoustic dataset show that the optimized array reduces normalized root mean square error by 44.4 percent compared to conventional spiral arrays, and the proposed attention-based multi-scale network achieves an area under the curve of 0.967 and recall of 0.913.
