Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.

Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.

Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.

Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.

In this study, a numerical investigation was conducted on the seismic behavior of low-strength reinforced concrete columns, strengthened with steel bars and wrapped with fiberglass tapes and fabrics, using finite element software. The columns were subjected to both monotonic and cyclic loading, and the analysis focused on fracture patterns, failure mechanisms, lateral hysteresis loops, ductility degradation, and stiffness degradation. The results showed that the reference column exhibited brittle shear failure and insufficient ductility. In contrast, the second column, reinforced with steel bars and partially wrapped with fiberglass tapes, demonstrated 30% higher tensile strength compared to the reference column, achieving stable hysteresis loops, improved energy dissipation, and 25% less cracking. The third column, fully wrapped with fiberglass fabric in addition to the steel bars, exhibited 50% higher tensile strength and 75% reduced probability of cracking in the plastic hinge area. These findings underscore the effectiveness of advanced reinforcement techniques in improving the seismic performance of reinforced concrete columns.

In this study, a numerical investigation was conducted on the seismic behavior of low-strength reinforced concrete columns, strengthened with steel bars and wrapped with fiberglass tapes and fabrics, using finite element software. The columns were subjected to both monotonic and cyclic loading, and the analysis focused on fracture patterns, failure mechanisms, lateral hysteresis loops, ductility degradation, and stiffness degradation. The results showed that the reference column exhibited brittle shear failure and insufficient ductility. In contrast, the second column, reinforced with steel bars and partially wrapped with fiberglass tapes, demonstrated 30% higher tensile strength compared to the reference column, achieving stable hysteresis loops, improved energy dissipation, and 25% less cracking. The third column, fully wrapped with fiberglass fabric in addition to the steel bars, exhibited 50% higher tensile strength and 75% reduced probability of cracking in the plastic hinge area. These findings underscore the effectiveness of advanced reinforcement techniques in improving the seismic performance of reinforced concrete columns.

In this study, a numerical investigation was conducted on the seismic behavior of low-strength reinforced concrete columns, strengthened with steel bars and wrapped with fiberglass tapes and fabrics, using finite element software. The columns were subjected to both monotonic and cyclic loading, and the analysis focused on fracture patterns, failure mechanisms, lateral hysteresis loops, ductility degradation, and stiffness degradation. The results showed that the reference column exhibited brittle shear failure and insufficient ductility. In contrast, the second column, reinforced with steel bars and partially wrapped with fiberglass tapes, demonstrated 30% higher tensile strength compared to the reference column, achieving stable hysteresis loops, improved energy dissipation, and 25% less cracking. The third column, fully wrapped with fiberglass fabric in addition to the steel bars, exhibited 50% higher tensile strength and 75% reduced probability of cracking in the plastic hinge area. These findings underscore the effectiveness of advanced reinforcement techniques in improving the seismic performance of reinforced concrete columns.

In this study, a numerical investigation was conducted on the seismic behavior of low-strength reinforced concrete columns, strengthened with steel bars and wrapped with fiberglass tapes and fabrics, using finite element software. The columns were subjected to both monotonic and cyclic loading, and the analysis focused on fracture patterns, failure mechanisms, lateral hysteresis loops, ductility degradation, and stiffness degradation. The results showed that the reference column exhibited brittle shear failure and insufficient ductility. In contrast, the second column, reinforced with steel bars and partially wrapped with fiberglass tapes, demonstrated 30% higher tensile strength compared to the reference column, achieving stable hysteresis loops, improved energy dissipation, and 25% less cracking. The third column, fully wrapped with fiberglass fabric in addition to the steel bars, exhibited 50% higher tensile strength and 75% reduced probability of cracking in the plastic hinge area. These findings underscore the effectiveness of advanced reinforcement techniques in improving the seismic performance of reinforced concrete columns.

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.

The automated inspection of complex freeform components, such as helical gears, is a persistent challenge in advanced manufacturing due to their intricate geometries and strict precision requirements. Conventional inspection methods, such as those using coordinate measuring machines or optical techniques, are often time-consuming and lack adaptability to subtle deviations. Recent deep learning approaches show promise but are typically limited to point-based or scan-to-scan comparisons, which remain sensitive to noise and alignment errors. We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE). CAD-derived gear models are systematically perturbed with controlled Gaussian deformations to emulate tolerances, defects, and sensor noise, then voxelized for autoencoder training. This enables robust learning of nominal gear geometry distributions. Extensive experiments conducted against PointNet++, a Variational Autoencoder, and a GAN-based reconstruction model demonstrate that our method consistently achieves superior performance across various metrics, including PSNR, SSIM, accuracy, precision, recall, and F1-score. The results highlight the potential of voxel-based learning with data-driven perturbation for scalable and high-accuracy inspection in industrial applications.

Oil well drilling is an expensive process that needs a particular focus. For this reason, Rate Of Penetration (ROP) has been widely approved as a measure of drilling efficiency and adequate configuration parameters. Our aim in this work consists in the elaboration of a smart system using Hoeffding trees for predicting the Rate of Penetration (ROP) in oilfield drilling. The choice of Hoeffding trees to build our model is motivated by their adaptive learning capability and drift detection. They offer continuous, fast, and efficient learning both online on data streams and offline on batch data. To validate our approach, we used real drilling data from the “Hassi-Terfa” oilfield located in Southeast Algeria. The obtained results show in comparison to the eXtreme Gradient Boosting (XGBoost) algorithm that Hoeffding trees maintain their learning capacity and produce more accurate predictions even in the presence of drifts. This is thanks to the combination of the Adaptive Windowing (ADWIN) algorithm to manage drifts and least mean squares (LMS) filters to reduce noise. This observation highlights the effectiveness of our approach to predict the ROP while oil-well drilling. The proposed smart system offers more efficient solution to predict the ROP, whether in real-time or offline. By leveraging its adaptability to changes in data distribution, our approach ensures more accurate and adaptive predictions, facilitating drilling operations optimization and boosting the overall efficiency of the process.

Oil well drilling is an expensive process that needs a particular focus. For this reason, Rate Of Penetration (ROP) has been widely approved as a measure of drilling efficiency and adequate configuration parameters. Our aim in this work consists in the elaboration of a smart system using Hoeffding trees for predicting the Rate of Penetration (ROP) in oilfield drilling. The choice of Hoeffding trees to build our model is motivated by their adaptive learning capability and drift detection. They offer continuous, fast, and efficient learning both online on data streams and offline on batch data. To validate our approach, we used real drilling data from the “Hassi-Terfa” oilfield located in Southeast Algeria. The obtained results show in comparison to the eXtreme Gradient Boosting (XGBoost) algorithm that Hoeffding trees maintain their learning capacity and produce more accurate predictions even in the presence of drifts. This is thanks to the combination of the Adaptive Windowing (ADWIN) algorithm to manage drifts and least mean squares (LMS) filters to reduce noise. This observation highlights the effectiveness of our approach to predict the ROP while oil-well drilling. The proposed smart system offers more efficient solution to predict the ROP, whether in real-time or offline. By leveraging its adaptability to changes in data distribution, our approach ensures more accurate and adaptive predictions, facilitating drilling operations optimization and boosting the overall efficiency of the process.

Oil well drilling is an expensive process that needs a particular focus. For this reason, Rate Of Penetration (ROP) has been widely approved as a measure of drilling efficiency and adequate configuration parameters. Our aim in this work consists in the elaboration of a smart system using Hoeffding trees for predicting the Rate of Penetration (ROP) in oilfield drilling. The choice of Hoeffding trees to build our model is motivated by their adaptive learning capability and drift detection. They offer continuous, fast, and efficient learning both online on data streams and offline on batch data. To validate our approach, we used real drilling data from the “Hassi-Terfa” oilfield located in Southeast Algeria. The obtained results show in comparison to the eXtreme Gradient Boosting (XGBoost) algorithm that Hoeffding trees maintain their learning capacity and produce more accurate predictions even in the presence of drifts. This is thanks to the combination of the Adaptive Windowing (ADWIN) algorithm to manage drifts and least mean squares (LMS) filters to reduce noise. This observation highlights the effectiveness of our approach to predict the ROP while oil-well drilling. The proposed smart system offers more efficient solution to predict the ROP, whether in real-time or offline. By leveraging its adaptability to changes in data distribution, our approach ensures more accurate and adaptive predictions, facilitating drilling operations optimization and boosting the overall efficiency of the process.

Oil well drilling is an expensive process that needs a particular focus. For this reason, Rate Of Penetration (ROP) has been widely approved as a measure of drilling efficiency and adequate configuration parameters. Our aim in this work consists in the elaboration of a smart system using Hoeffding trees for predicting the Rate of Penetration (ROP) in oilfield drilling. The choice of Hoeffding trees to build our model is motivated by their adaptive learning capability and drift detection. They offer continuous, fast, and efficient learning both online on data streams and offline on batch data. To validate our approach, we used real drilling data from the “Hassi-Terfa” oilfield located in Southeast Algeria. The obtained results show in comparison to the eXtreme Gradient Boosting (XGBoost) algorithm that Hoeffding trees maintain their learning capacity and produce more accurate predictions even in the presence of drifts. This is thanks to the combination of the Adaptive Windowing (ADWIN) algorithm to manage drifts and least mean squares (LMS) filters to reduce noise. This observation highlights the effectiveness of our approach to predict the ROP while oil-well drilling. The proposed smart system offers more efficient solution to predict the ROP, whether in real-time or offline. By leveraging its adaptability to changes in data distribution, our approach ensures more accurate and adaptive predictions, facilitating drilling operations optimization and boosting the overall efficiency of the process.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.

Wild chicory, or Hyoseris radiata L., is indigenous to the Mediterranean region, is a plant used in traditional medicine as a diuretic, blood depurative, and against kidney stones. The present study aimed to assess for the first time the acute and subacute toxicity, to quantify the total amount of polyphenols and flavonoids, and to assess the antioxidant activity of H. radiata collected from Setif, Algeria. The overall amount of flavonoids and polyphenols was quantified spectrophotometrically. The antioxidant activity of the extract was evaluated according to two methods, DPPH and FRAP. The acute toxicity of H. radiata was carried out according to the OECD guideline 423 to determine the median lethal dose LD50 and the subacute toxicity was evaluated according to OECD guideline 407 to assess the possible pathological effects of the extract administered for 28 days by oral route. The results show that the total amount of polyphenols and flavonoids was 132.53 ± 2 µg of GAE·1 mg-1 and 96.11 ± 3.65 µg of QE·1 mg-1 of extract, respectively. The extract shows a good antioxidant potential in both tests. The administered dose (2 g·kg-1 of BW) didn’t produce any changes in general behaviors or mortality, so the LD50 is greater than 2 g·kg-1 of BW. Moreover, the daily administration of the extract with 2 doses, 100 mg·kg-1 and 200 mg·kg-1 didn’t cause any changes in body weight, behavior test, hematological parameters, and organ relative weight. A significant decrease in triglyceride was recorded in both concentrations. Based on the present findings, the extract of H. radiata has no significant toxicity. These findings offer valuable information about the toxicity profile of the traditional medicine plant Hyoseris radiata L.