Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.

Introduction/purpose: Algerian natural zeolite (denoted NZ) was modified using hydrochloric acid (HZ) and sodium hydroxide solution (NaZ). This study investigated the impact of acid and alkaline modifications on the adsorption of two cationic textile dyes (BR46 and BY13) from aqueous solutions. Methods: The XRF analysis confirmed that SiO2 is the predominant mineral in all three zeolites. The XRD results revealed that NZ is primarily composed of mordenite, with chabazite and minor quartz content. The MEB-EDX analysis showed slight variations in the Si and Al content for HZ and NaZ, without significantly altering the zeolite’s structure.. The effects of initial dye concentration, contact time and pH were examined in a batch system.

Results: The adsorption on NZ, NaZ and HZ increased with longer contact times, higher initial dye concentrations, and elevated temperatures. Equilibrium was rapidly attained best described using the pseudo-second order kinetic model. Both the Langmuir and the Freundlich isotherm models fit for the adsorption data. Conclusion: The highest dye removal efficiency was observed for NaZ, with 97.62% for BR46 and 98.97% for BY13. The lowest removal rates occurred at pH= 8 for HZ and pH=10 for NZ and NaZ. Adsorption was spontaneous and endothermic.

Introduction purpose: As population growth and industrial expansion continue, surface freshwater reservoirs such as dams have become increasingly vital due to their accessibility and ease of treatment. However, the quality of these water sources has significantly deteriorated, primarily due to the discharge of domestic and industrial wastewater. The proliferation of extensive algal blooms has led to significant challenges in maintaining drinking water quality and raised concerns about public health. This study investigates the impact of various water sources on the physicochemical quality of an Algerian dam over four seasons (December 2020 – October 2021) and explores the factors influencing the occurrence of cyanobacterial blooms to better understand and manage this excessive growth.

Methods: Physicochemical properties and algal composition of the dam water were analyzed monthly to determine nutrient sources and environmental factors affecting cyanobacterial proliferation. Results: The analysis revealed that the Timgad stream and Reboua valley are notable sources of nutrient enrichment. Elevated temperatures and high nutrient loads, particularly total phosphorus (TP), in Timgad dam water facilitate the proliferation of blue-green algae. Additionally, limited nitrogen content favors the dominance of nitrogen-fixing cyanobacteria such as Aphanizomenon and Oscillatoria. The study also highlights that the low flow rate and high nutrient load of the Timgad stream create favorable conditions for cyanobacterial growth. Conclusions: Nutrient inputs, temperature, and hydrological conditions significantly influence cyanobacterial blooms. Understanding these factors is crucial for implementing effective water management strategies to reduce algal proliferation and protect freshwater quality.

Introduction purpose: As population growth and industrial expansion continue, surface freshwater reservoirs such as dams have become increasingly vital due to their accessibility and ease of treatment. However, the quality of these water sources has significantly deteriorated, primarily due to the discharge of domestic and industrial wastewater. The proliferation of extensive algal blooms has led to significant challenges in maintaining drinking water quality and raised concerns about public health. This study investigates the impact of various water sources on the physicochemical quality of an Algerian dam over four seasons (December 2020 – October 2021) and explores the factors influencing the occurrence of cyanobacterial blooms to better understand and manage this excessive growth.

Methods: Physicochemical properties and algal composition of the dam water were analyzed monthly to determine nutrient sources and environmental factors affecting cyanobacterial proliferation. Results: The analysis revealed that the Timgad stream and Reboua valley are notable sources of nutrient enrichment. Elevated temperatures and high nutrient loads, particularly total phosphorus (TP), in Timgad dam water facilitate the proliferation of blue-green algae. Additionally, limited nitrogen content favors the dominance of nitrogen-fixing cyanobacteria such as Aphanizomenon and Oscillatoria. The study also highlights that the low flow rate and high nutrient load of the Timgad stream create favorable conditions for cyanobacterial growth. Conclusions: Nutrient inputs, temperature, and hydrological conditions significantly influence cyanobacterial blooms. Understanding these factors is crucial for implementing effective water management strategies to reduce algal proliferation and protect freshwater quality.

Introduction purpose: As population growth and industrial expansion continue, surface freshwater reservoirs such as dams have become increasingly vital due to their accessibility and ease of treatment. However, the quality of these water sources has significantly deteriorated, primarily due to the discharge of domestic and industrial wastewater. The proliferation of extensive algal blooms has led to significant challenges in maintaining drinking water quality and raised concerns about public health. This study investigates the impact of various water sources on the physicochemical quality of an Algerian dam over four seasons (December 2020 – October 2021) and explores the factors influencing the occurrence of cyanobacterial blooms to better understand and manage this excessive growth.

Methods: Physicochemical properties and algal composition of the dam water were analyzed monthly to determine nutrient sources and environmental factors affecting cyanobacterial proliferation. Results: The analysis revealed that the Timgad stream and Reboua valley are notable sources of nutrient enrichment. Elevated temperatures and high nutrient loads, particularly total phosphorus (TP), in Timgad dam water facilitate the proliferation of blue-green algae. Additionally, limited nitrogen content favors the dominance of nitrogen-fixing cyanobacteria such as Aphanizomenon and Oscillatoria. The study also highlights that the low flow rate and high nutrient load of the Timgad stream create favorable conditions for cyanobacterial growth. Conclusions: Nutrient inputs, temperature, and hydrological conditions significantly influence cyanobacterial blooms. Understanding these factors is crucial for implementing effective water management strategies to reduce algal proliferation and protect freshwater quality.

This article presents a quantitative and qualitative analysis of causal markers in scientific articles published in France and Algeria. Based on two corpora drawn from Synergie France and Synergie Algérie, the study examines the frequency, distribution, and functions of connectors such as car, donc, puisque, and parce que. The results reveal a common core of markers but distinct preferences:  French authors favor a structured and diversified argumentative style, while Algerian writers adopt a more explicit and pedagogical approach. These differences reflect contrasting academic traditions and highlight the didactic importance of causal markers in the teaching of scientific writing.

The aim of the present study is to examine the way in which The Daily Telegraph portrays migrants as ‘Others’ by employing a discourse and power dynamics perspective. It attempts to identify and analyse the predominant discursive strategies, social context implications and power dynamics that the newspaper employs to represent this group of individuals. The study uses a descriptive qualitative research approach, along with critical discourse analysis, adopting Fairclough’s three-dimensional framework as a research instrument for analysis. This framework allows for a thorough analysis of the text, and its social context. Consequently, the results gained from the examination, revealed that the Daily Telegraph used various discursive strategies to construct migrants as others in a negative way, employing metaphor, hyperbole, and othering strategies. As regards the discursive practices, social context implications and power dynamics at play, the study showed that migrants are believed to be an uncontrollable "other" that necessitates border control. The marginalisation and exclusion of migrants from the holding society were frequently the result of the recurrent use of negative stereotypes by the daily Telegraph. It is possible that this will lead to unfair policies and the maintenance of power relationships by making these migrants seem different or dangerous.

The aim of the present study is to examine the way in which The Daily Telegraph portrays migrants as ‘Others’ by employing a discourse and power dynamics perspective. It attempts to identify and analyse the predominant discursive strategies, social context implications and power dynamics that the newspaper employs to represent this group of individuals. The study uses a descriptive qualitative research approach, along with critical discourse analysis, adopting Fairclough’s three-dimensional framework as a research instrument for analysis. This framework allows for a thorough analysis of the text, and its social context. Consequently, the results gained from the examination, revealed that the Daily Telegraph used various discursive strategies to construct migrants as others in a negative way, employing metaphor, hyperbole, and othering strategies. As regards the discursive practices, social context implications and power dynamics at play, the study showed that migrants are believed to be an uncontrollable "other" that necessitates border control. The marginalisation and exclusion of migrants from the holding society were frequently the result of the recurrent use of negative stereotypes by the daily Telegraph. It is possible that this will lead to unfair policies and the maintenance of power relationships by making these migrants seem different or dangerous.

 Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO₂) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

 Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO₂) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

 Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO₂) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

 Ensuring the safety and health of assembly line workers is critical to increasing productivity and preventing accidents. This research presents a real-time monitoring system that combines computer vision (AI), wearable Internet of Things (IoT) devices, and cloud-based technologies to detect worker fatigue and health risks. The system calculates eye aspect ratio (EAR) and mouth aspect ratio (MAR) to identify fatigue symptoms such as eye closure and yawning, while wearable IoT devices monitor physiological parameters such as heart rate (HR) and blood oxygen saturation (SpO₂) to detect potential health issues. Alerts are automatically triggered based on pre-defined thresholds, allowing for immediate intervention. All data is processed in real-time with input from wearables and computer vision, and transmitted to a cloud platform for analysis, reporting and storage. This integration of AI-powered computer vision, wearable IoT and cloud connectivity ensures continuous monitoring and provides actionable insights to supervisors, improving workplace safety and operational efficiency. The results of the study demonstrate the effectiveness of this innovative system in identifying fatigue and health issues, reducing accidents and promoting a safer working environment. By using the latest technology, the proposed solution addresses the urgent need for advanced safety measures in demanding work environments.

This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This study investigates the fatigue degradation and crack propagation in the locking mechanism of PET bottle blow molding machines, focusing on the impact of elliptical cracks on the mechanism’s performance and longevity. The locking mechanism, which plays a vital role in securing the mold during the blow molding process, is subjected to repeated loading, making it susceptible to fatigue damage. Using a combination of finite element analysis (FEA) and experimental methodologies, we examine the stress distribution, deformation, and displacement in the mechanism under operational loads. The study identifies the most stressed component and models the behavior of an elliptical crack located at the center of this component. A stress intensity factor (K) of 3.7553 MPa.mm-0.5 is found, indicating significant risk in the crack region. Fatigue analysis using Goodman’s law predicts a service life of one million cycles with a safety factor of 2.08. These findings highlight the need for targeted design and maintenance strategies to enhance the reliability and durability of PET blow molding machines.

This paper investigates the relationship between the extended spectrum of a bounded linear operator and its group inverse. We also establish a connection between the extended spectrum of the bounded linear operator and that of its Drazin inverse. As part of our analysis, we prove the following equality: σext((BA)D) = σext((AB)D), where (BA)D and (AB)D represent the Drazin inverses of BA and AB, respectively. 2020 Mathematics Subject Classification. 35K15; 35K55; 35K65; 35B40. Key words and phrases. extended spectrum; operator group inverse; Drazin inverse.