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461
Directional solidification of Zn-Al-Cu eutectic alloy by the vertical Bridgman method
Published 2015-01-01Get full text
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462
Analyzing the customer purchase data of an online shopping store by data mining: A real case study in Iran
Published 2025-03-01Get full text
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463
Simulation of Mechanical Heart Valve Dysfunction and the Non-Newtonian Blood Model Approach
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464
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465
A speech recognition method with enhanced transformer decoder
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466
Application of Vibration Signals for the Quantitative Analysis of the Optimal Threshold of Bearing Failure
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467
Living labs as orchestrators in the regional innovation ecosystem: a conceptual framework
Published 2024-12-01Get full text
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468
Production Scheduling considering Outsourcing Options and Carrier Costs
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469
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470
Capsule neural network and adapted golden search optimizer based forest fire and smoke detection
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471
A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection
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472
Electric Vehicles and Storage Systems Integrated within a Sustainable Urban District Fed by Solar Energy
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473
Performance Evaluation and Optimal Management of Distance-Based Registration Using a Semi-Markov Process
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474
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475
Statistical Evaluation of Indoor Air Quality Parameters in Classrooms of a University
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476
Estimating energy consumption and GHG emissions in the U.S. food supply chain for net-zero
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477
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478
Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines
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479
Social acceptance of small modular reactor (SMR): Evidence from a contingent valuation study in South Korea
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480
Reliability Evaluation of Distribution Power Systems Based on Artificial Neural Network Techniques
Published 2012-01-01“…Artificial neural network is recently established as a useful and much promising too, applied to variety of power systems engineering. This paper presents ANN version for evaluating the reliability of distribution power systems (DPSs), in the proposed algorithm, the ANN used to predicted (RPS) using historical data method constructed according to the backpropagation learning rule. …”
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