In this paper, we investigate the connections between certain spec-tra arising from Fredholm theory of a generalized Drazin invertible bounded linear operator and those of its generalized Drazin inverse. Furthermore, we analyze the transfer of Browder’s theorem and its generalized form from such an operator to its corresponding generalized Drazin inverse. Applications to left, right, and multiplication operators are also presented.

This article presents a linguistic analysis of language dysfluencies produced in exolingual situations. The study is based on a collection of real interactions recorded between Algerian doctors in a university hospital in the country. Following a corpus linguistics approach, the oral data were transcribed according to conventions adapted to spoken language. The description will focus on communication strategies that help manage the production difficulties encountered. The objective of the article is to identify the most frequent markers of disfluencies in the corpus, such as lexical repetitions, interrupted sentences, and terminological confusions. Additionally, it aims to explore whether there is a functional link between these disfluencies and language cooperation strategies, in order to ensure the dynamic of exolingual communication in a linguistic and professional context.

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.

Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.

Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.

The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.

Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.

Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.

The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.

Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.

Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.

The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.

Introduction. A new global maximum power point tracking (GMPPT) control strategy for a solar photovoltaic (PV) system, based on the combination of Takagi-Sugeno (T-S) fuzzy models and an ANFIS, is presented. The novelty of this paper lies in the integration of T-S fuzzy models and the ANFIS approach to develop an efficient GMPPT controller for a PV system operating under partial shading conditions.

Purpose. The new GMPPT control strategy aims to extract maximum power from the PV system under varying weather conditions or partial shading.

Methods. An ANFIS algorithm is used to determine the maximum voltage, which corresponds to the actual maximum power point, based on PV voltage and current. Next, the nonlinear model of the PV system is employed to design the T-S fuzzy controller. A reference model is then derived based on the maximum voltage. Finally, a tracking controller is developed using the reference model and the T-S fuzzy controller. The stability of the overall system is evaluated using Lyapunov's method and is represented through linear matrix inequalities expressions.

The results clearly demonstrate the advantages of the proposed GMPPT-based fuzzy control strategy, showcasing its high performance in effectively reducing oscillations in various steady states of the PV system, ensuring minimal overshoot and a faster response time. In addition, a comparative analysis of the proposed GMPPT controller against conventional algorithms, such as Incremental Conductance, Perturb & Observe and Particle Swarm Optimization, shows that it offers a fast dynamic response in finding the maximum power with significantly less oscillation around the maximum power point.

A human skills framework is a human resource management tool that identifies and describes the set of skills required to fill different positions within an organization. This article helps to identify the knowledge, know-how, and interpersonal skills needed for each role, thereby facilitating talent management and the professional development of employees. By clearly identifying the required skills and adequately training personnel, the framework helps reduce costs related to unexpected breakdowns and emergency interventions, while maximizing equipment availability. A human skills framework is a strategic tool that contributes to optimizing an organization’s human capital, enabling better alignment between available and re

A human skills framework is a human resource management tool that identifies and describes the set of skills required to fill different positions within an organization. This article helps to identify the knowledge, know-how, and interpersonal skills needed for each role, thereby facilitating talent management and the professional development of employees. By clearly identifying the required skills and adequately training personnel, the framework helps reduce costs related to unexpected breakdowns and emergency interventions, while maximizing equipment availability. A human skills framework is a strategic tool that contributes to optimizing an organization’s human capital, enabling better alignment between available and re

A human skills framework is a human resource management tool that identifies and describes the set of skills required to fill different positions within an organization. This article helps to identify the knowledge, know-how, and interpersonal skills needed for each role, thereby facilitating talent management and the professional development of employees. By clearly identifying the required skills and adequately training personnel, the framework helps reduce costs related to unexpected breakdowns and emergency interventions, while maximizing equipment availability. A human skills framework is a strategic tool that contributes to optimizing an organization’s human capital, enabling better alignment between available and re

A human skills framework is a human resource management tool that identifies and describes the set of skills required to fill different positions within an organization. This article helps to identify the knowledge, know-how, and interpersonal skills needed for each role, thereby facilitating talent management and the professional development of employees. By clearly identifying the required skills and adequately training personnel, the framework helps reduce costs related to unexpected breakdowns and emergency interventions, while maximizing equipment availability. A human skills framework is a strategic tool that contributes to optimizing an organization’s human capital, enabling better alignment between available and re

The phenomenon of swelling-shrinkage has gained widespread attention in practice owing to the generation of eroded clayey layers of soil that amplify with global climate change and the seasonal water content. This provokes several serious disorders affecting the stability of nearby constructions and consequently generating human loss. The expansive clayey soils show the phenomena of wetting and drying cycles in their natural state (undisturbed soil). Hence, classical oedometric tests are found to be unable to take into account the thermal behavior of naturally swelling soils; this is proven by the resulting asymptotic volumetric behavior, as well as the steady values of the potential of swelling and shrinkage. The main aim of this experimental analysis is to derive a test that considers the significant effect of temperature. Experimental results of an oedometric approach are represented herein for the purpose of investigating the volumetric and hydric behavior of naturally swelling soil in the region of N’Gaous (Eastern Batna province, Algeria) through drying-wetting paths. Innovative expressions are derived for the direct computations of the swelling-shrinkage potential in terms of water content, appearance time and applied loads. It is of interest to mention that those expressions are applicable to other regions in the world with similar soil geotechnical and chemical characteristics and conditions. The cyclic outputs show that the swelling pressure variation with the appearance time is mainly related to the first cycle of swelling shrinkage; as it exhibits a noticeable increase in the swelling potential with the amplification of applied loads until reaching a state of steadiness. The experimental results demonstrate a high degree of reliability and correlation with the soil behavior. Therefore, the swelling shrinkage potentials are expressed innovatively in equations that help predict the soil behavior in expansive regions in order to enhance the safety of nearby foundations.

The phenomenon of swelling-shrinkage has gained widespread attention in practice owing to the generation of eroded clayey layers of soil that amplify with global climate change and the seasonal water content. This provokes several serious disorders affecting the stability of nearby constructions and consequently generating human loss. The expansive clayey soils show the phenomena of wetting and drying cycles in their natural state (undisturbed soil). Hence, classical oedometric tests are found to be unable to take into account the thermal behavior of naturally swelling soils; this is proven by the resulting asymptotic volumetric behavior, as well as the steady values of the potential of swelling and shrinkage. The main aim of this experimental analysis is to derive a test that considers the significant effect of temperature. Experimental results of an oedometric approach are represented herein for the purpose of investigating the volumetric and hydric behavior of naturally swelling soil in the region of N’Gaous (Eastern Batna province, Algeria) through drying-wetting paths. Innovative expressions are derived for the direct computations of the swelling-shrinkage potential in terms of water content, appearance time and applied loads. It is of interest to mention that those expressions are applicable to other regions in the world with similar soil geotechnical and chemical characteristics and conditions. The cyclic outputs show that the swelling pressure variation with the appearance time is mainly related to the first cycle of swelling shrinkage; as it exhibits a noticeable increase in the swelling potential with the amplification of applied loads until reaching a state of steadiness. The experimental results demonstrate a high degree of reliability and correlation with the soil behavior. Therefore, the swelling shrinkage potentials are expressed innovatively in equations that help predict the soil behavior in expansive regions in order to enhance the safety of nearby foundations.

The phenomenon of swelling-shrinkage has gained widespread attention in practice owing to the generation of eroded clayey layers of soil that amplify with global climate change and the seasonal water content. This provokes several serious disorders affecting the stability of nearby constructions and consequently generating human loss. The expansive clayey soils show the phenomena of wetting and drying cycles in their natural state (undisturbed soil). Hence, classical oedometric tests are found to be unable to take into account the thermal behavior of naturally swelling soils; this is proven by the resulting asymptotic volumetric behavior, as well as the steady values of the potential of swelling and shrinkage. The main aim of this experimental analysis is to derive a test that considers the significant effect of temperature. Experimental results of an oedometric approach are represented herein for the purpose of investigating the volumetric and hydric behavior of naturally swelling soil in the region of N’Gaous (Eastern Batna province, Algeria) through drying-wetting paths. Innovative expressions are derived for the direct computations of the swelling-shrinkage potential in terms of water content, appearance time and applied loads. It is of interest to mention that those expressions are applicable to other regions in the world with similar soil geotechnical and chemical characteristics and conditions. The cyclic outputs show that the swelling pressure variation with the appearance time is mainly related to the first cycle of swelling shrinkage; as it exhibits a noticeable increase in the swelling potential with the amplification of applied loads until reaching a state of steadiness. The experimental results demonstrate a high degree of reliability and correlation with the soil behavior. Therefore, the swelling shrinkage potentials are expressed innovatively in equations that help predict the soil behavior in expansive regions in order to enhance the safety of nearby foundations.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

The growing need for clean energy has made solar panels an essential solution. However, the nonlinear behavior of photovoltaic (PV) systems under varying weather conditions necessitates advanced control strategies to ensure optimal energy harvesting. This paper presents an enhanced Maximum Power Point Tracking (MPPT) approach that integrates the conventional Perturb and Observe (P&O) method with an Indirect Adaptive Fuzzy Controller (IAFC). While P&O is known for its simplicity, it suffers from steady-state oscillations and slow response during environmental changes. To address these issues, the IAFC adaptively adjusts the perturbation step using a Lyapunov-based rule to improve convergence and minimize power fluctuations. The proposed method achieves Maximum Power Point tracking within less than 0.025 s, compared to 0.05 s for the conventional P&O algorithm. This enhances the credibility of our dynamic performance claim. Specifically, unlike prior fuzzy-P&O hybrids with fixed rule sets, our method leverages Lyapunov-based adaptation to dynamically adjust the control action, improving convergence and robustness under changing conditions. We also included a quantitative metric showing a 75% reduction in power fluctuations compared to conventional P&O. Simulation results under varying sunlight conditions demonstrate fast convergence and improved power stability. The proposed IAFC method clearly outperforms classical P&O in tracking accuracy, responsiveness, and overall energy yield.

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.

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.