Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

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.

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.

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.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose’s challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator’s ability to faithfully reproduce genuine solar panel characteristics.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose’s challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator’s ability to faithfully reproduce genuine solar panel characteristics.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose’s challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator’s ability to faithfully reproduce genuine solar panel characteristics.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose’s challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator’s ability to faithfully reproduce genuine solar panel characteristics.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose’s challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator’s ability to faithfully reproduce genuine solar panel characteristics.