This paper compares two approaches for detecting and analyzing acoustic microwaves in piezoelectric materials, specifically in Lithium Niobate (LiNbO3) substrates. The first method focuses on modeling the propagation of acoustic microwaves in piezoelectric structures, utilizing an interdigital transducer (IDT) to excite the electroelastic waves. This method investigates various wave types, such as secondary surface waves, leaky waves, bulk waves, and skimming bulk waves, and applies wavelet transform for efficient detection. Two wavelet functions—Mexican-hat and Morlet—are compared based on their ability to detect acoustic wave singularities, with an emphasis on their efficiency in processing microwave signals. The second method introduces a machine learning approach using support vector machines (SVM) to detect ultrasonic pulses and identify previously undetectable waves. By classifying real and imaginary parts of the coefficient attenuation and acoustic velocity, this method provides more accurate values and facilitates the modeling of ultrasonic pulse propagation. While the wavelet-based approach focuses on signal processing for wave detection, the SVM-based method excels in detecting complex wave patterns that traditional methods may overlook, offering higher precision in ultrasonic pulse modeling and the realization of acoustic microwave devices.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

The present work focuses on the experimental characterisation and numerical validation of the in-plane mechanical properties of IM2-12K/Epocast 50-A1 composite for structural damage prediction. Consequently, a series of tensile, compressive, shear, and flexural tests were systematically conducted on specimens prepared with specific lay-up configurations, while the fibre volume fraction was measured using the ashing method. The experimental results demonstrated that the composite under investigation exhibited high tensile strength and stiffness along the fibre direction, moderate compressive properties, and lower shear strength. This behaviour is indicative of anisotropic properties. Moreover, a three-dimensional finite element simulation of the tensile and three-point flexural tests was subsequently conducted, employing a Hashin-based failure initiation criterion. In order to achieve this objective, the key material properties were incorporated into a user-defined material subroutine (VUMAT), thereby enabling the modelling of progressive damage mechanisms, encompassing both fibre and matrix failures. The numerical predictions exhibited excellent agreement with the experimental data, thereby validating both the measured properties and the robustness of the modelling strategy. The present study establishes a validated mechanical dataset and a predictive model, providing a reliable foundation for the design and simulation of the performance of IM2-12K/Epocast 50-A1 in advanced engineering applications.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areasfor urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city.36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.