The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.

Cette étude vise à évaluer la capacité des fibres (chanvre et alfa) à améliorer les propriétés mécaniques du béton, dans le but de créer un béton respectueux de l'environnement. Les bétons ont été incorporés avec des teneurs de 0.25%, 0.5%, et 1% (en volume) en fibres de chanvre, plus une dose de 0.5% (en volume) de fibres d'alfa et ainsi qu'un béton ordinaire. Les résultats montrent que l'utilisation des fibres de chanvre et d'alfa permet une meilleure augmentation de la résistance à la traction par fendage par rapport au béton ordinaire. En outre, l'augmentation de la résistance à la flexion pour le béton de fibres de chanvre est optimale avec une teneur de 0.25%. Aussi, le module d’élasticité dynamique du HFC-0.25 est proche ou égal à celui du béton ordinaire. Nous concluons que les fibres végétales de chanvre et d'alfa sont des candidats potentiels pour produire du béton vert.

Cette étude vise à évaluer la capacité des fibres (chanvre et alfa) à améliorer les propriétés mécaniques du béton, dans le but de créer un béton respectueux de l'environnement. Les bétons ont été incorporés avec des teneurs de 0.25%, 0.5%, et 1% (en volume) en fibres de chanvre, plus une dose de 0.5% (en volume) de fibres d'alfa et ainsi qu'un béton ordinaire. Les résultats montrent que l'utilisation des fibres de chanvre et d'alfa permet une meilleure augmentation de la résistance à la traction par fendage par rapport au béton ordinaire. En outre, l'augmentation de la résistance à la flexion pour le béton de fibres de chanvre est optimale avec une teneur de 0.25%. Aussi, le module d’élasticité dynamique du HFC-0.25 est proche ou égal à celui du béton ordinaire. Nous concluons que les fibres végétales de chanvre et d'alfa sont des candidats potentiels pour produire du béton vert.

Cette étude vise à évaluer la capacité des fibres (chanvre et alfa) à améliorer les propriétés mécaniques du béton, dans le but de créer un béton respectueux de l'environnement. Les bétons ont été incorporés avec des teneurs de 0.25%, 0.5%, et 1% (en volume) en fibres de chanvre, plus une dose de 0.5% (en volume) de fibres d'alfa et ainsi qu'un béton ordinaire. Les résultats montrent que l'utilisation des fibres de chanvre et d'alfa permet une meilleure augmentation de la résistance à la traction par fendage par rapport au béton ordinaire. En outre, l'augmentation de la résistance à la flexion pour le béton de fibres de chanvre est optimale avec une teneur de 0.25%. Aussi, le module d’élasticité dynamique du HFC-0.25 est proche ou égal à celui du béton ordinaire. Nous concluons que les fibres végétales de chanvre et d'alfa sont des candidats potentiels pour produire du béton vert.

Cette étude vise à évaluer la capacité des fibres (chanvre et alfa) à améliorer les propriétés mécaniques du béton, dans le but de créer un béton respectueux de l'environnement. Les bétons ont été incorporés avec des teneurs de 0.25%, 0.5%, et 1% (en volume) en fibres de chanvre, plus une dose de 0.5% (en volume) de fibres d'alfa et ainsi qu'un béton ordinaire. Les résultats montrent que l'utilisation des fibres de chanvre et d'alfa permet une meilleure augmentation de la résistance à la traction par fendage par rapport au béton ordinaire. En outre, l'augmentation de la résistance à la flexion pour le béton de fibres de chanvre est optimale avec une teneur de 0.25%. Aussi, le module d’élasticité dynamique du HFC-0.25 est proche ou égal à celui du béton ordinaire. Nous concluons que les fibres végétales de chanvre et d'alfa sont des candidats potentiels pour produire du béton vert.

Cette étude vise à évaluer la capacité des fibres (chanvre et alfa) à améliorer les propriétés mécaniques du béton, dans le but de créer un béton respectueux de l'environnement. Les bétons ont été incorporés avec des teneurs de 0.25%, 0.5%, et 1% (en volume) en fibres de chanvre, plus une dose de 0.5% (en volume) de fibres d'alfa et ainsi qu'un béton ordinaire. Les résultats montrent que l'utilisation des fibres de chanvre et d'alfa permet une meilleure augmentation de la résistance à la traction par fendage par rapport au béton ordinaire. En outre, l'augmentation de la résistance à la flexion pour le béton de fibres de chanvre est optimale avec une teneur de 0.25%. Aussi, le module d’élasticité dynamique du HFC-0.25 est proche ou égal à celui du béton ordinaire. Nous concluons que les fibres végétales de chanvre et d'alfa sont des candidats potentiels pour produire du béton vert.

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and I_ON/I_OFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting I_ON/I_OFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both I_ON/I_OFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for I_ON/I_OFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and I_ON/I_OFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting I_ON/I_OFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both I_ON/I_OFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for I_ON/I_OFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and I_ON/I_OFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting I_ON/I_OFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both I_ON/I_OFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for I_ON/I_OFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

In this paper, we introduce an innovative design approach based on combined numerical simulations and machine learning (ML) analysis to investigate the design key parameters of ultra-low scale junctionless gate-all-around (JLGAA) field-effect transistor (FET) devices. To this end, precise 3D numerical models that incorporate quantum effects and ballistic transport are employed to simulate the current–voltage (I–V) characteristics of 10 nm-scale JLGAA FET devices. The influence of design parameter variations and high-k dielectric material on the subthreshold characteristics is thoroughly examined. Various ML algorithms were employed to analyze and classify the key design parameters influencing the subthreshold figures-of-merit (FoMs), the subthreshold swing (SS) factor and I_ON/I_OFF ratio. The obtained results highlight that channel radius and channel doping design parameters are particularly important for affecting swing factor behavior. Similarly, these features also play a significant role in predicting and affecting I_ON/I_OFF current ratio values. Additionally, machine learning is used to determine the optimal design parameters for each figure of merit (FoM) output value. In this context, the models effectively predicted both I_ON/I_OFF current ratios and SS classification, with Naive Bayes achieving an accuracy of 90.8% for I_ON/I_OFF and 92.6% for SS, showcasing the model's robustness in these classification tasks.

This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set. The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model. Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies. These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services. Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.

This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set. The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model. Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies. These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services. Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.

This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set. The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model. Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies. These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services. Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.

This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set. The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model. Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies. These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services. Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.

This study devises self-stabilizing algorithms that leverage the expression distance-2 paradigm to compute (i) a maximal distance-2 independent set, wherein selected nodes maintain a separation exceeding two edges, ensuring non-adjacency; and (ii) a minimal dominating set, wherein each external node has at least one node as a neighbor in the dominating set. The efficacy and convergence of the algorithms are established through rigorous proofs within the framework of the expression model. Extensive simulation tests validate the algorithms' proficiency in selecting a minimal subset of nodes across expansive network topologies. These algorithms find practical applications in network operations, particularly in ad hoc and wireless sensor networks for the selection of cluster heads that facilitate critical services. Moreover, the self-stabilizing property of the algorithms guarantees the robust reconfiguration of cluster heads post-failure, thereby preserving network functionality amidst disruptions.

High speed machining (HSM) is an attractive process for numerous applications due to its potential to increase production rates, reduce lead times, lower costs, and enhance part quality. In this study, high-speed turning operations on AISI D2 steel using a coated carbide cutting tool under dry conditions were conducted. The cutting parameters examined in this investigation were Vc, f, and ap, while the outputs measured were surface roughness (Ra), cutting temperature (T), and flank wear (VB). To obtain reliable and accurate results, a Taguchi L27 orthogonal array for the 27 experimental runs was employed as well as analysis of variance (ANOVA), response surface methodology (RSM), and artificial neural network (ANN) to develop a constitutive relationship between prediction responses and the cutting parameters. The ANOVA results showed that Vc had a significant effect on T (36.81%) and VB (27.58%), while f had a considerable influence on Ra (24.21%). Additionally, nonlinear prediction models were created for each measured output and their accuracy was evaluated using three statistical indices: coefficient of determination (R²), mean absolute percentage error (MAPE), and root mean square error (RMSE). Finally, multi-objective optimization was successfully carried out using the desirability function (DF) approach to propose an optimal set of cutting parameters that simultaneously minimized Ra, T, and VB. The optimized cutting parameters were Vc = 477.28 m/min, f = 0.08 rev/min, and ap = 0.8 mm, resulting in Ra = 1.23 μm, T = 129.9∘C, and VB = 0.049 mm.