Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Closed greenhouse systems optimize internal climatic conditions for both reducing energy loss and high-quality yields. Nevertheless, careful monitoring of the parameters of the microclimate requires a better understanding of the thermal phenomena that coexist at the same time inside the greenhouses. In the present study, the surface radiation effect on the natural convection in the greenhouse was investigated numerically based a turbulent unsteady model. The k-ε model was adopted for the turbulent flow and the discrete ordinate (DO) method for the radiation heat transfer. Assuming a no isotherm conditions at the floor and roof faces of the greenhouse and for a Rayleigh number ranges from 0.6×10¹⁰ to 2.3×10¹⁰. The results showed a strong radiation effect on the thermal behavior near the walls and considerably reduces the flow dynamics within the greenhouse. The contribution of the radiation heat transfer on the total Nusselt number at least 50% greater than that without. The results obtained for the selected values of Rayleigh numbers are in good agreement with the experimental data of the literature.

Closed greenhouse systems optimize internal climatic conditions for both reducing energy loss and high-quality yields. Nevertheless, careful monitoring of the parameters of the microclimate requires a better understanding of the thermal phenomena that coexist at the same time inside the greenhouses. In the present study, the surface radiation effect on the natural convection in the greenhouse was investigated numerically based a turbulent unsteady model. The k-ε model was adopted for the turbulent flow and the discrete ordinate (DO) method for the radiation heat transfer. Assuming a no isotherm conditions at the floor and roof faces of the greenhouse and for a Rayleigh number ranges from 0.6×10¹⁰ to 2.3×10¹⁰. The results showed a strong radiation effect on the thermal behavior near the walls and considerably reduces the flow dynamics within the greenhouse. The contribution of the radiation heat transfer on the total Nusselt number at least 50% greater than that without. The results obtained for the selected values of Rayleigh numbers are in good agreement with the experimental data of the literature.

Closed greenhouse systems optimize internal climatic conditions for both reducing energy loss and high-quality yields. Nevertheless, careful monitoring of the parameters of the microclimate requires a better understanding of the thermal phenomena that coexist at the same time inside the greenhouses. In the present study, the surface radiation effect on the natural convection in the greenhouse was investigated numerically based a turbulent unsteady model. The k-ε model was adopted for the turbulent flow and the discrete ordinate (DO) method for the radiation heat transfer. Assuming a no isotherm conditions at the floor and roof faces of the greenhouse and for a Rayleigh number ranges from 0.6×10¹⁰ to 2.3×10¹⁰. The results showed a strong radiation effect on the thermal behavior near the walls and considerably reduces the flow dynamics within the greenhouse. The contribution of the radiation heat transfer on the total Nusselt number at least 50% greater than that without. The results obtained for the selected values of Rayleigh numbers are in good agreement with the experimental data of the literature.

In this paper, we propose a full-wave analysis for characterizing the resonant frequencies and bandwidths of high-temperature superconductor inverted microstrip printed on anisotropic substrates. Our proposed approach is based on Galerkin procedure in the Fourier transform domain (FTD) combining with the complex resistive boundary condition. With the use of suitable Green's functions in the FTD, the analysis is performed for the case where the superconducting rectangular patches printed on anisotropic substrate. The numerical results obtained using the proposed approach are compared with previously published numerical results computed by means of the electromagnetic simulator “IE3D software”. These comparisons were very good, which prove the correctness and the validity of the proposed method. It is found that the optical properties combined with optimally chosen structural parameters of anisotropic materials can be maintaining control of the resonant frequency and exhibiting wider bandwidth characteristics.

In this paper, we propose a full-wave analysis for characterizing the resonant frequencies and bandwidths of high-temperature superconductor inverted microstrip printed on anisotropic substrates. Our proposed approach is based on Galerkin procedure in the Fourier transform domain (FTD) combining with the complex resistive boundary condition. With the use of suitable Green's functions in the FTD, the analysis is performed for the case where the superconducting rectangular patches printed on anisotropic substrate. The numerical results obtained using the proposed approach are compared with previously published numerical results computed by means of the electromagnetic simulator “IE3D software”. These comparisons were very good, which prove the correctness and the validity of the proposed method. It is found that the optical properties combined with optimally chosen structural parameters of anisotropic materials can be maintaining control of the resonant frequency and exhibiting wider bandwidth characteristics.

In this paper, we propose a full-wave analysis for characterizing the resonant frequencies and bandwidths of high-temperature superconductor inverted microstrip printed on anisotropic substrates. Our proposed approach is based on Galerkin procedure in the Fourier transform domain (FTD) combining with the complex resistive boundary condition. With the use of suitable Green's functions in the FTD, the analysis is performed for the case where the superconducting rectangular patches printed on anisotropic substrate. The numerical results obtained using the proposed approach are compared with previously published numerical results computed by means of the electromagnetic simulator “IE3D software”. These comparisons were very good, which prove the correctness and the validity of the proposed method. It is found that the optical properties combined with optimally chosen structural parameters of anisotropic materials can be maintaining control of the resonant frequency and exhibiting wider bandwidth characteristics.

In this paper, we propose a full-wave analysis for characterizing the resonant frequencies and bandwidths of high-temperature superconductor inverted microstrip printed on anisotropic substrates. Our proposed approach is based on Galerkin procedure in the Fourier transform domain (FTD) combining with the complex resistive boundary condition. With the use of suitable Green's functions in the FTD, the analysis is performed for the case where the superconducting rectangular patches printed on anisotropic substrate. The numerical results obtained using the proposed approach are compared with previously published numerical results computed by means of the electromagnetic simulator “IE3D software”. These comparisons were very good, which prove the correctness and the validity of the proposed method. It is found that the optical properties combined with optimally chosen structural parameters of anisotropic materials can be maintaining control of the resonant frequency and exhibiting wider bandwidth characteristics.

We establish global-posedness in time for the viscous Boussinesq equations in two dimensions of space with temperature-dependent diffusivity in the framework of a smooth vortex patch. We also provide the inviscid limit for velocity, temperature, and associated flow toward the system studied very recently in \cite{Paicu-Zhu} as soon as the viscosity goes to zero, and quantify the rate of convergence.

We establish global-posedness in time for the viscous Boussinesq equations in two dimensions of space with temperature-dependent diffusivity in the framework of a smooth vortex patch. We also provide the inviscid limit for velocity, temperature, and associated flow toward the system studied very recently in \cite{Paicu-Zhu} as soon as the viscosity goes to zero, and quantify the rate of convergence.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.