This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

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

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

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

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

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

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

La présente étude s’intéresse à la littérature scientifique qui s’est faite autour du penseur algérien Malek Bennabi (1905-1973) en ce début du 21e siècle. Son objectif est de vérifier l’authenticité du prétendu retour de la pensée bennabienne sur la scène académique et intellectuelle, ainsi que de mesurer le degré d’intérêt que les chercheurs lui portent à travers le monde, notamment en Algérie avec l’apparition de plusieurs inédits pendant cette même période. Pour cela, un état des lieux a été entrepris, en posant comme cadre chronologique la période qui va de l’an 2000 jusqu’à 2022. Le recensement des données bibliographiques, illustré par des tableaux et des représentations graphiques, s’est fait selon plusieurs plans : le nombre ainsi que les nationalités des chercheurs, les pays depuis lesquels les travaux ont été publiés, les types de documents (livres, thèses, conférences, etc.), les catégories de ces travaux (sociologie, économie, religion, etc.) ainsi que les langues dans lesquelles ils ont été exprimés. Les résultats de cette étude révèlent un phénomène de pluralité, voire de cosmopolitisme, et un taux de publication croissant au cours des années.

La présente étude s’intéresse à la littérature scientifique qui s’est faite autour du penseur algérien Malek Bennabi (1905-1973) en ce début du 21e siècle. Son objectif est de vérifier l’authenticité du prétendu retour de la pensée bennabienne sur la scène académique et intellectuelle, ainsi que de mesurer le degré d’intérêt que les chercheurs lui portent à travers le monde, notamment en Algérie avec l’apparition de plusieurs inédits pendant cette même période. Pour cela, un état des lieux a été entrepris, en posant comme cadre chronologique la période qui va de l’an 2000 jusqu’à 2022. Le recensement des données bibliographiques, illustré par des tableaux et des représentations graphiques, s’est fait selon plusieurs plans : le nombre ainsi que les nationalités des chercheurs, les pays depuis lesquels les travaux ont été publiés, les types de documents (livres, thèses, conférences, etc.), les catégories de ces travaux (sociologie, économie, religion, etc.) ainsi que les langues dans lesquelles ils ont été exprimés. Les résultats de cette étude révèlent un phénomène de pluralité, voire de cosmopolitisme, et un taux de publication croissant au cours des années.

The targeted resorption of socio-economic deficits at the local (municipal) level requires the use of a cartography of development in the multidimensional sense of the term, combined with that of the causes structural factors of its possible delay. This article provides for this purpose a municipal cartography of the development of M’sila province, and its sources assimilated to education, standard of living, economic activity, housing and social services. To ensure a certain objectivity in our research, we have chosen an automatic technique following the essential steps (choice of variables, normalization, weighting and aggregation) in the hierarchical classification of municipalities. This mapping of multidimensional local development clearly shows the development deficits at the municipal level, due to the delays accumulated for years, despite the generalization of basic education and attempt the local authorities to improve the economic and social conditions of the population. The results obtained show that on the whole, the urban municipalities have a local development index higher than 0.7 and rank in the first places, this index goes from a minimum (0.310) observed in the rural municipalities Tamsa, Zerzour, Sidi M’hammed and the ones located south of the province where the climate is dry and hot, the maximum index (0.860) recorded in two urban municipalities at namely M’sila and Bousaada, The high variability of development deficits shows that any development strategy would benefit from being rethought in order to provide more effective to the different situations experienced by rural municipalities in particular.

The targeted resorption of socio-economic deficits at the local (municipal) level requires the use of a cartography of development in the multidimensional sense of the term, combined with that of the causes structural factors of its possible delay. This article provides for this purpose a municipal cartography of the development of M’sila province, and its sources assimilated to education, standard of living, economic activity, housing and social services. To ensure a certain objectivity in our research, we have chosen an automatic technique following the essential steps (choice of variables, normalization, weighting and aggregation) in the hierarchical classification of municipalities. This mapping of multidimensional local development clearly shows the development deficits at the municipal level, due to the delays accumulated for years, despite the generalization of basic education and attempt the local authorities to improve the economic and social conditions of the population. The results obtained show that on the whole, the urban municipalities have a local development index higher than 0.7 and rank in the first places, this index goes from a minimum (0.310) observed in the rural municipalities Tamsa, Zerzour, Sidi M’hammed and the ones located south of the province where the climate is dry and hot, the maximum index (0.860) recorded in two urban municipalities at namely M’sila and Bousaada, The high variability of development deficits shows that any development strategy would benefit from being rethought in order to provide more effective to the different situations experienced by rural municipalities in particular.

The targeted resorption of socio-economic deficits at the local (municipal) level requires the use of a cartography of development in the multidimensional sense of the term, combined with that of the causes structural factors of its possible delay. This article provides for this purpose a municipal cartography of the development of M’sila province, and its sources assimilated to education, standard of living, economic activity, housing and social services. To ensure a certain objectivity in our research, we have chosen an automatic technique following the essential steps (choice of variables, normalization, weighting and aggregation) in the hierarchical classification of municipalities. This mapping of multidimensional local development clearly shows the development deficits at the municipal level, due to the delays accumulated for years, despite the generalization of basic education and attempt the local authorities to improve the economic and social conditions of the population. The results obtained show that on the whole, the urban municipalities have a local development index higher than 0.7 and rank in the first places, this index goes from a minimum (0.310) observed in the rural municipalities Tamsa, Zerzour, Sidi M’hammed and the ones located south of the province where the climate is dry and hot, the maximum index (0.860) recorded in two urban municipalities at namely M’sila and Bousaada, The high variability of development deficits shows that any development strategy would benefit from being rethought in order to provide more effective to the different situations experienced by rural municipalities in particular.

Recent climatic shifts and the growth of agricultural land have escalated the incidence of wheat field fires, presenting severe risks to both food security and local economies. This study aims to develop advanced predictive models to effectively forecast significant wheat fires in Barika, Algeria. We utilized a comprehensive dataset spanning from 2015 to 2023, which includes information on fire incidents and meteorological factors like temperature, humidity, precipitation, and wind speed. A sophisticated ensemble machine learning model was crafted, combining Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Random Forest (RF) in a stacked configuration to predict wheat fire events. Our analysis indicates that the ensemble model significantly outperforms traditional single-model approaches in terms of both accuracy and reliability. Employing these cutting-edge predictive techniques significantly bolsters firefighting measures, enhances resource management, and reduces the adverse effects of fires in agricultural zones. The employment of ensemble learning highlights its utility as a formidable tool in environmental management and crisis response. With more precise forecasts, this model facilitates improved emergency preparedness and strategic intervention plans, aiming to safeguard essential agricultural assets and support rural communities against the backdrop of mounting environmental pressures.

Recent climatic shifts and the growth of agricultural land have escalated the incidence of wheat field fires, presenting severe risks to both food security and local economies. This study aims to develop advanced predictive models to effectively forecast significant wheat fires in Barika, Algeria. We utilized a comprehensive dataset spanning from 2015 to 2023, which includes information on fire incidents and meteorological factors like temperature, humidity, precipitation, and wind speed. A sophisticated ensemble machine learning model was crafted, combining Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Random Forest (RF) in a stacked configuration to predict wheat fire events. Our analysis indicates that the ensemble model significantly outperforms traditional single-model approaches in terms of both accuracy and reliability. Employing these cutting-edge predictive techniques significantly bolsters firefighting measures, enhances resource management, and reduces the adverse effects of fires in agricultural zones. The employment of ensemble learning highlights its utility as a formidable tool in environmental management and crisis response. With more precise forecasts, this model facilitates improved emergency preparedness and strategic intervention plans, aiming to safeguard essential agricultural assets and support rural communities against the backdrop of mounting environmental pressures.

Recent climatic shifts and the growth of agricultural land have escalated the incidence of wheat field fires, presenting severe risks to both food security and local economies. This study aims to develop advanced predictive models to effectively forecast significant wheat fires in Barika, Algeria. We utilized a comprehensive dataset spanning from 2015 to 2023, which includes information on fire incidents and meteorological factors like temperature, humidity, precipitation, and wind speed. A sophisticated ensemble machine learning model was crafted, combining Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Random Forest (RF) in a stacked configuration to predict wheat fire events. Our analysis indicates that the ensemble model significantly outperforms traditional single-model approaches in terms of both accuracy and reliability. Employing these cutting-edge predictive techniques significantly bolsters firefighting measures, enhances resource management, and reduces the adverse effects of fires in agricultural zones. The employment of ensemble learning highlights its utility as a formidable tool in environmental management and crisis response. With more precise forecasts, this model facilitates improved emergency preparedness and strategic intervention plans, aiming to safeguard essential agricultural assets and support rural communities against the backdrop of mounting environmental pressures.

The article focuses on the generalization of Fuglede's theorem and the solvability of operator equations. Topics include extending Fuglede's theorem to non-normal operators, deriving criteria for solving operator equations such as AX − XB = C, and using inner inverses to establish necessary and sufficient conditions for operator equation solutions.

The article focuses on the generalization of Fuglede's theorem and the solvability of operator equations. Topics include extending Fuglede's theorem to non-normal operators, deriving criteria for solving operator equations such as AX − XB = C, and using inner inverses to establish necessary and sufficient conditions for operator equation solutions.

A Vertical Axis Wind Turbine (VAWT) comprises multiple parts constructed from different materials. This complexity presents challenges in designing the blade structure. In this study, we investigated a structural optimization of a small-scale blade for a VAWT, with Finite Element Analysis (FEA) model. The purpose is to minimize the blade mass while adhering to a suite of critical wind conditions according to the IEC 61400-2 Standard. The structure made from Aluminum material simulates structure’s global behavior to determine maximum stress and deflection levels. The same structure is modeled using Glass/Epoxy composite for optimizing its design. Twenty combinations of Glass/Epoxy layers, varying in ply thickness and orientation, are simulated to find the most suitable combination. Results demonstrated that the optimization case [45°/90°/0°/−45°] obtained the minimum values of stress and deflection, is 59% lighter than Aluminum blade (initial design). The designed Glass/Epoxy composite blade is acceptable and recommended for structural safety.