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.

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.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.

III-V-based materials are widely used for multi-junction solar cell applications due to their large band gap, allowing them to absorb a significant amount of light and increase the output power. Among the III-V materials, AlGaAs is a promising candidate for the top cell due to its tunable band gap. However, the growth of AlGaAs often leads to the formation of DX-centers, resulting in low material quality and limiting the reported efficiencies of AlGaAs cells to mostly below 18.7%. Research in this field has primarily focused on single and multi-variable parameter sweep methods to optimize the conversion efficiency of solar cells. While effective, these techniques can be time-consuming, especially when only the final result matters and their accuracy diminishes as the number of layers in the cell increases. To address these challenges, we proposed a metaheuristic method based on Real Coded Genetic Algorithm (RCGA) to optimize the solar cell. By hybridizing MATLAB and Atlas SILVACO, we developed an efficient code. The effectiveness of the utilized modeling framework is evaluated by comparing its predictions to experimental results, revealing a strong correspondence between the two. The obtained results were compared to those achieved using conventional parameter sweep methods. Our optimized solar cell achieved an efficiency of 26.08% under the AM1.5 spectrum. The findings demonstrate that a multi-dimensional optimization using the RCGA approach, combined with the Atlas SILVACO simulator, can be effectively employed to optimize semiconductor devices, offering a more robust alternative to existing methods.