Exploration of integrating biomechanical perspective into ideological education management strategy

Molecular & cellular biomechanics, Journal Year: 2025, Volume and Issue: 22(1), P. 996 - 996

Published: Jan. 6, 2025

This paper introduced biomechanical theory into ideological education management, analyzed the impact of educational intervention and optimized strategy by establishing a dynamic prediction model behavior, aiming to improve effect, reduce resource investment, realize personalized precise management. It constructed thought behavior based on LSTM (Long Short-Term Memory), used core concepts biomechanics analogize key variables in Thought tendencies can be analogized state variables, interventions regarded as external forces, inertia corresponded internal resistance transformation, thereby revealing law change providing quantitative basis. In terms optimization, GA (Genetic Algorithm) is optimize strategy, fitness function comprehensively evaluate degree transformation costs achieve multi-objective balance. The experimental results show that proposed shows high accuracy tendency scores, with an average RMSE (Root Mean Square Error) 0.12 MAE (Mean Absolute 0.08. superior traditional strategies improving class participation rate, learning management system login frequency, reducing costs. perspective provide accurate predictions states efficient use resources optimizing design, which verifies theoretical innovation practical application value this method.

Language: Английский

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Dynamic Adaptive Artificial Hummingbird Algorithm-Enhanced Deep Learning Framework for Accurate Transmission Line Temperature Prediction

Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 403 - 403

Published: Jan. 21, 2025

As power demand increases and the scale of grids expands, accurately predicting transmission line temperatures is becoming essential for ensuring stability security systems. Traditional physical statistical models struggle with complex multivariate time series, often failing to balance short-term fluctuations long-term dependencies, their prediction accuracy adaptability remain limited. To address these challenges, this paper proposes a deep learning model architecture based on Dynamic Adaptive Artificial Hummingbird Algorithm (DA-AHA), named DA-AHA-CNN-LSTM-TPA (DA-AHA-CLT). The integrates convolutional neural networks (CNNs) local feature extraction, long memory (LSTM) temporal modeling, pattern attention mechanisms (TPA) dynamic weighting, while DA-AHA optimizes hyperparameters enhance stability. traditional artificial hummingbird algorithm (AHA) further improved by introducing step-size adjustment, greedy search, grouped parallel search global exploration exploitation. Our experimental results demonstrate that DA-AHA-CLT achieves coefficient determination (R2) 0.987, root-mean-square error (RMSE) 0.023, mean absolute (MAE) 0.018, median (MedAE) 0.011, outperforming such as CNN-LSTM LSTM-TPA. These findings confirm effectively captures characteristics temperatures, offering superior performance robustness in full-time-step tasks, highlight its potential solving challenging time-series forecasting problems

Language: Английский

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Federated Learning and Neural Circuit Policies: A Novel Framework for Anomaly Detection in Energy-Intensive Machinery

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 936 - 936

Published: Feb. 15, 2025

In the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neural Circuit Policies (NCPs) to enhance in compressors utilized leather tanning operations. Unlike traditional Long Short-Term Memory (LSTM) networks, which rely heavily on historical data patterns often struggle generalization, NCPs incorporate physical constraints system dynamics, resulting superior performance. Our comparative analysis reveals significantly outperform LSTMs accuracy interpretability within framework. innovative combination not only addresses pressing privacy concerns but also facilitates collaborative across decentralized sources. By showcasing effectiveness FL NCPs, this research paves way advanced strategies prioritize both performance integrity industries.

Language: Английский

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The Effects of Intelligent Semantic Analysis Techniques on Language Acquisition in the Improvement of English Intercultural Communication Skills

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract The study identifies the relevant semantic dependencies after establishing a corpus system database, and then searches for degree of differentiation utterances through English sentence similarity algorithm, which adopts vector space modeling criterion uses computed similarities as elements. Meanwhile, two simple efficient labeling transformation algorithms, namely, label algorithm graph-to-graph linear are proposed way to improve performance language learning in cross-cultural communication. Based on above, develops an AMR intelligent analysis using stack-LSTM analyzes its role enhancing intercultural communication skills during acquisition. accuracy annotation can be verified by applying automatic syntax Chinese languages, utterance is recognized with different components, results show that highly accurate, 92% recognition precision rate good effect. Finally, regression conducted according effect acquisition groups people, it found use has significant improvement students’ ability.

Language: Английский

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LSTM-Based Real-Time Drill String Stuck Prediction

Published: April 21, 2025

Summary Stuck pipe is one of the most serious drilling challenges, resulting in significant non-productive time (NPT), higher costs, and even well abandonment. Thus, developing a reliable real-time stuck prediction system critical. This study develops an LSTM-based model to predict pipes real during operations. Unlike previous research, which relied on non-real-time accessible features such as mud rheology survey data, this work uses Mud Logging readily available online majority wells, ensuring practical application. Furthermore, many existing studies use inappropriate metrics assess performance, accuracy, F1-score, recall, can lead misleading results highly imbalanced datasets. To address this, current employs Area Under ROC Curve (AUC) its primary evaluation metric, yielding more assessment. The systematically investigates how key parameters, temporal window size resampling techniques, affect performance. dataset was collected from that includes four pipes, with data sampled at 0.5 Hz. Expert recommendations led selection hook load, torque, standpipe pressure, surface RPM, weight bit sensor readings, all have strong correlation pipes. show LSTM 10-second undersampling strategy performs best, AUC 0.868. value than those obtained using conventional models support vector machines (SVM) or random forests (RF) techniques. proposed approach effectively captures dependencies allowing for detection early warning patterns associated incidents. broadens scope beyond operations by including examples tripping reaming processes. A case real-world field validates model's ability provide warnings, lowering operational risks downtime costs. Level 2 triggered 33 minutes before event, followed 3 alert just prior incident. These findings demonstrate LSTM's potential prediction, improving safety efficiency.

Language: Английский

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A Techno-Economic Analysis of Power Generation in Wind Power Plants Through Deep Learning: A Case Study of Türkiye

Energies, Journal Year: 2025, Volume and Issue: 18(10), P. 2632 - 2632

Published: May 20, 2025

In recent years, the utilization of renewable energy sources has significantly increased due to their environmentally friendly nature and sustainability. Among these sources, wind plays a critical role, accurately forecasting power with minimal error is essential for optimizing efficiency profitability plants. This study analyzes hourly speed data from 23 meteorological stations located in Türkiye’s Western Black Sea Region years 2020–2024, using Weibull distribution estimate annual production. Additionally, same were forecasted Long Short-Term Memory (LSTM) model. The predicted also assessed through analysis evaluate potential each station. A comparative was then conducted between results measured forecast datasets. Based on production estimates derived both datasets, revenues, costs, profits 10 MW farms at location examined. findings indicate that highest revenues unit electricity observed Zonguldak South, Sinop İnceburun, Bartın South stations. According LSTM-based forecasts 2025, investment projects considered feasible İnebolu, Cide North, Gebze Köşkburnu, Amasra

Language: Английский

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Comparative Evaluation of Hybrid LSTM-Based Models for Predicting Bioactive Compound Contents and Antioxidant Activity in Microwave-Assisted Extraction from Carrots Using Natural Deep Eutectic Solvents

LWT, Journal Year: 2025, Volume and Issue: unknown, P. 117938 - 117938

Published: May 1, 2025

Language: Английский

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Advanced Processing Technologies for Innovative Materials

Technologies, Journal Year: 2024, Volume and Issue: 12(11), P. 227 - 227

Published: Nov. 11, 2024

There is a need for further, in-depth research that explores the synthesis of newly developed materials created using advanced technologies [...]

Language: Английский

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Assessment and Prediction of Enterprise's Innovation Capability Based on Variational AutoEncoder and Long Short-Term Memory Network

Published: Dec. 20, 2024

Language: Английский

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