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.

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.

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.

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.

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.

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.

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.

Olea europaea L. 1753, is one of the oldest and most distinctive trees in the Mediterranean region. Its nutritional, social, cultural, and economic value is very important for populations in arid regions, where it is widely distributed. A sign of a sustainable environment in many agricultural regions is the existence of a wide variety and abundance of arthropod groups. The main objective of the study is to evaluate the diversity of arthropods subservientin in olive agro-systems in the arid region by using several sampling techniques, namely classic sight hunting, visual inspection, Barber pots, and yellow traps. The inventory is carried out over a period of 5 months, from February to June 2023, in three stations in M’Sila (northeastern Algeria). Three classes of arthropods were found: Insecta, Arachnida, and Malacostraca. Captures were numerically dominated by Insecta, representing 96.88% of total captures. Arachnida and Malacostraca classes represented about 2.74 and 0.38%, respectively. During this research, a total of 1861 arthropod individuals were collected and identified into 83 species, 79 genera, 53 families, and 15 orders. The most abundant orders were: Diptera (42.56%), Hymenoptera (28.11%), and Coleoptera (7.32%). However, we found a significant difference in species composition according to habitat (P < 0.01). The species were determined, and the ecological indices were calculated (Shannon Value, Evenness values and Simpson reciprocal index). The dominant functional feeding groups were phytophages (41.91 %), predators (32.94%), and polyphages (22.14%). The arthropods included several olive pests such as Euphyllura olivina (Costa) (Hemiptera: Liviidae), Bactrocera oleae (Rossi) (Diptera: Tephritidae), Prays oleae (Bernard) (Lepidoptera: Praydidae), Liothrips oleae Costa (Thysanoptera: Phlaeothripidae), and Oxycenus maxwelli (Keifer) (Arachnida: Eriophyidae).

Olea europaea L. 1753, is one of the oldest and most distinctive trees in the Mediterranean region. Its nutritional, social, cultural, and economic value is very important for populations in arid regions, where it is widely distributed. A sign of a sustainable environment in many agricultural regions is the existence of a wide variety and abundance of arthropod groups. The main objective of the study is to evaluate the diversity of arthropods subservientin in olive agro-systems in the arid region by using several sampling techniques, namely classic sight hunting, visual inspection, Barber pots, and yellow traps. The inventory is carried out over a period of 5 months, from February to June 2023, in three stations in M’Sila (northeastern Algeria). Three classes of arthropods were found: Insecta, Arachnida, and Malacostraca. Captures were numerically dominated by Insecta, representing 96.88% of total captures. Arachnida and Malacostraca classes represented about 2.74 and 0.38%, respectively. During this research, a total of 1861 arthropod individuals were collected and identified into 83 species, 79 genera, 53 families, and 15 orders. The most abundant orders were: Diptera (42.56%), Hymenoptera (28.11%), and Coleoptera (7.32%). However, we found a significant difference in species composition according to habitat (P < 0.01). The species were determined, and the ecological indices were calculated (Shannon Value, Evenness values and Simpson reciprocal index). The dominant functional feeding groups were phytophages (41.91 %), predators (32.94%), and polyphages (22.14%). The arthropods included several olive pests such as Euphyllura olivina (Costa) (Hemiptera: Liviidae), Bactrocera oleae (Rossi) (Diptera: Tephritidae), Prays oleae (Bernard) (Lepidoptera: Praydidae), Liothrips oleae Costa (Thysanoptera: Phlaeothripidae), and Oxycenus maxwelli (Keifer) (Arachnida: Eriophyidae).

Olea europaea L. 1753, is one of the oldest and most distinctive trees in the Mediterranean region. Its nutritional, social, cultural, and economic value is very important for populations in arid regions, where it is widely distributed. A sign of a sustainable environment in many agricultural regions is the existence of a wide variety and abundance of arthropod groups. The main objective of the study is to evaluate the diversity of arthropods subservientin in olive agro-systems in the arid region by using several sampling techniques, namely classic sight hunting, visual inspection, Barber pots, and yellow traps. The inventory is carried out over a period of 5 months, from February to June 2023, in three stations in M’Sila (northeastern Algeria). Three classes of arthropods were found: Insecta, Arachnida, and Malacostraca. Captures were numerically dominated by Insecta, representing 96.88% of total captures. Arachnida and Malacostraca classes represented about 2.74 and 0.38%, respectively. During this research, a total of 1861 arthropod individuals were collected and identified into 83 species, 79 genera, 53 families, and 15 orders. The most abundant orders were: Diptera (42.56%), Hymenoptera (28.11%), and Coleoptera (7.32%). However, we found a significant difference in species composition according to habitat (P < 0.01). The species were determined, and the ecological indices were calculated (Shannon Value, Evenness values and Simpson reciprocal index). The dominant functional feeding groups were phytophages (41.91 %), predators (32.94%), and polyphages (22.14%). The arthropods included several olive pests such as Euphyllura olivina (Costa) (Hemiptera: Liviidae), Bactrocera oleae (Rossi) (Diptera: Tephritidae), Prays oleae (Bernard) (Lepidoptera: Praydidae), Liothrips oleae Costa (Thysanoptera: Phlaeothripidae), and Oxycenus maxwelli (Keifer) (Arachnida: Eriophyidae).

Our study aims to analyse two communicative schemata: online interaction (IL) and radio interaction (IR). The objective is to understand the functioning of phatic expression and its characteristics in the opening and closing rituals in IL & IR. A descriptive analytical approach was adopted to conduct a qualitative analysis of a corpus consisting of IR on Alger Chaine 3 and IL in a Messenger group (Facebook). This analysis revealed the presence of phatic elements in the openings and closures of exchanges, both in IR and online interactions. It allowed contrasting these two types of interactions, highlighting the specificity of IL, especially as users shape their linguistic and interactive behaviour in the digital environment.