This study synthesizes the outcomes of land use changes obtained through the implementation of dynamic modeling by cellular automata across two metropolitan regions in Portugal and Brazil. The purpose is to analyze th...
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Digital twin-based machine learning (ML) techniques can improve the control of the storage conditions of dried products, strengthening the classical water sorption isotherm-based approach by including additional proce...
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Digital twin-based machine learning (ML) techniques can improve the control of the storage conditions of dried products, strengthening the classical water sorption isotherm-based approach by including additional process variables. In this study, water sorption isotherms of dried parchment and green coffee beans were experimentally determined at 25, 35, and 45 degrees C using the dynamic dew point (DDI) method. Experimental data (both coffee bean types and temperatures) were simultaneously modeled by means of three ML techniques, support vector machine (SVM), random forest (RF), and artificial neural networks (ANN), with 75% of data used for model training and 25% for validation. The hyperparameters were identified by minimizing the mean square error (MSE). The ML model's accuracy was addressed by a multiway ANOVA on the mean relative error (MRE), the coefficient of determination (R-2), and the computation time (CT). The sorption isotherms were significantly (p-value < 0.05) affected by the type of coffee and the temperature. The SVM model provided the best fit (MRE < 1% and R2 > 99%) in a reasonable CT (< 13 s). These results revealed the potential of ML models as a robust tool for the fast prediction of the equilibrium moisture content, including additional variables such as the type of coffee stage (dried parchment or green) and temperature;this paves the way for their industrial-level implementation to assist storage management.
The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of st...
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The increasing adoption of IoT devices in home environments has raised significant concerns about security and privacy. Analyzing real IoT traffic is essential for understanding these implications, yet the process pos...
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Fused Deposition modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a high...
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Fused Deposition modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses like dimensional accuracy, surface finish, and mechanical properties. The Taguchi method enabled the systematic exploration of parameters through the design of experiments (DOE). The experimental data was analyzed using Grey Relational analysis (GRA) to determine the optimal parameter settings. The GRA methodology offers a comprehensive approach to assess and prioritize various performance criteria, taking into account the inherent uncertainties in the manufacturing process. The results demonstrated the efficacy of the Taguchi-based GRA method in pinpointing the optimal parameter combinations for improving the printing quality and efficiency of TPU components. This study enhances the comprehension of Fused Deposition modeling (FDM) for Thermoplastic Polyurethane (TPU) material and provides a useful framework for optimizing the manufacturing process. Manufacturers can enhance printing productivity, quality, and reliability by utilizing Taguchi-based GRA. This, in turn, promotes the wid
The present research addresses the challenge of optimizing control in the wastewater treatment process, presenting a refined control model rooted in the particle swarm optimization (PSO) algorithm. Through a comprehen...
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Soybean protein is one of the important components of modern human diet. How to accurately and quickly determine the location of soybean protein is the main topic of research scholars. In order to understand the soybe...
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Modern complex chemical-technological systems as automation objects are distinguished by the presence of a large number of controlled and uncontrolled parameters, high productivity, technological modes intensity and o...
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Human motor learning is a neural process essential for acquiring new motor skills and adapting existing ones, which is fundamental to everyday activities. Neurological disorders such as Parkinson's Disease (PD) an...
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Human motor learning is a neural process essential for acquiring new motor skills and adapting existing ones, which is fundamental to everyday activities. Neurological disorders such as Parkinson's Disease (PD) and stroke can significantly affect human motor functions. Identifying neural biomarkers for human motor learning is essential for advancing therapeutic strategies for such disorders. However, identifying specific neural biomarkers associated with motor learning has been challenging due to the complex nature of brain activity and the limitations of traditional dataanalysis techniques. In response to these challenges, we developed a novel Spatial Graph Neural Network (SGNN) model to predict motor learning outcomes from electroencephalogram (EEG) data using the spatial-temporal dynamics of brain activity. We used it to analyse EEG data collected during a visuomotor rotation (VMR) task designed to elicit distinct types of learning: error-based and reward-based. By doing so, we establish a controlled environment that allows for precisely investigating neural signatures associated with these learning processes. To understand the features learned by the SGNN, we used a set of spatial, spectral, and temporal explainability methods to identify the brain regions and temporal dynamics crucial for learning. These approaches offer comprehensive insights into the neural biomarkers, aligning with current literature and ablation studies, and pave the way for applying this methodology to find biomarkers from various brain signals and tasks.
The research on the characteristics of power line carrier communication channel and the establishment of the model are the theoretical basis for the realization of high-speed data transmission on power line. Firstly, ...
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