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Predicting Critical Submergence in Horizontal Circular Intakes Using Meta-Heuristic Optimized Gradient Boosting Ense...
This study evaluates critical submergence depth (Sc) in horizontal circular intakes using a dataset compiled from two controlled laboratory experiments conducted under varying hydraulic and geometric configurations. A total of 324 experimental measurements were obtained across two intake-clearance scenarios: (i) zero bottom clearance (C = 0) and (ii) partial elevation (C = d_i/2...

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Nonlinear Function Fitting and Prediction Using Newton-CG Optimization and Transformer Architecture
This study proposes a nonlinear function-fitting prediction model combining the Newton-CG optimization algorithm with the Transformer architecture to address the limited accuracy and generalization of high-dimensional nonlinear data. Traditional methods often struggle with slow convergence and overfitting when dealing with complex nonlinear relationships. In this paper, the Tran...

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Perception-Edge-Cloud Architecture for Post-Stroke Rehabilitation: A Biosensor-Based Intelligent System for Movement...
To address the problem of distorted NC (Nursing Compliance) assessment for stroke patients during home rehabilitation, this paper proposes a four-level collaborative intelligent assessment and guidance system architecture: "Perception-Edge-Cloud-Feedback." First, upper limb movement and electromyography signals are synchronously collected using IMU (Inertial Measurement Unit) an...

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Knowledge-Augmented Deep Learning Framework for Chinese Literary Text Analysis Using ARO-HA-BiLSTM
Chinese literary texts spanning classical, modern, and contemporary periods present significant challenges for computational interpretation due to their deep semantic structures, metaphorical richness, and historical context. Traditional NLP approaches often fail to capture hierarchical narrative dependencies and culturally embedded meanings. This study proposes a Knowledge-Augm...

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Hybrid Early Warning System for Financial Crises in Listed Companies Using Attention Mechanism and LightGBM
Aiming to address the accuracy and interpretability needs of early warning systems for financial crises in listed companies, this paper proposes a hybrid early warning model that integrates an attention mechanism and LightGBM. When traditional financial early warning models deal with high-dimensional and nonlinear financial data, it is often difficult to capture the differential...

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