Quantum Key Agreement (QKA) is a cornerstone of quantum cryptography, facilitating secure key distribution among multiple participants. Existing QKA protocols often suffer from scalability issues and increased computational complexity as the number of participants grows. This paper proposes an efficient Circle Multiparty Quantum Key Agreement (CMQKA) protocol based on the BB84 protocol. This protocol enhances quantum resource efficiency and ensures equal participation in a circular topology. The key feature lies in the optimized use of quantum resources, minimizing the qubit overhead while ensuring high security standards. By achieving a qubit efficiency of 1/2n, it significantly improves the multiparty quantum communications. A thorough security analysis is conducted to demonstrate the protocol’s resilience against common quantum threats.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

This study investigates the integrated planning of production, maintenance, and quality control in a hybrid manufacturing-remanufacturing system, accounting for deterioration, variability in the quality of returned products, carbon emissions, and outsourcing opportunities. The network consists of a manufacturer collaborating with an outsourcing remanufacturing provider. The manufacturer operates a single failure-prone machine to produce new products and to remanufacture returned ones. Recovered products that the manufacturer cannot process are sent to the outsourcing provider for remanufacturing. The system generates harmful emissions, potentially leading to environmental taxes and sanctions. We formulate a mixed-integer nonlinear programming model to determine the optimal integrated manufacturing, remanufacturing, outsourcing, and preventive maintenance plan. Eventually, the proposed strategy minimizes total economic costs and defects and ultimately reduces carbon emissions. We use a global solver for solving small instances, while a genetic algorithm metaheuristic is developed for larger ones. Extensive computational experiments reveal that the developed genetic algorithm is highly efficient, achieving gaps of less than 0.95% within shorter execution times for small instances and significantly outperforming the solver in larger ones. The results show that the integrated outsourcing strategy, combined with accounting for carbon emissions from both new and remanufactured products, significantly reduces the reliance on new products, leading to notable cost savings and environmental benefits. These savings become more pronounced as the number of returns increases.

The sabkha soils of the Aïn M’lila region present major geotechnical challenges, including high salinity, significant gypsum and soluble salt contents, and low mechanical strength. To improve their geotechnical performance, this study evaluated the effectiveness of cement–lime stabilization at various mixing ratios. Mechanical tests, including compressibility, California bearing ratio (CBR), and unconfined compressive strength (UCS), were conducted to identify the optimal dosage. The 6/4% cement–lime mixture exhibited the best performance, showing a significant increase in bearing capacity and soil cohesion. Based on these results, an X-ray diffraction (XRD) analysis was carried out exclusively on this optimal mixture to examine the associated mineralogical transformations. The XRD results revealed a pronounced structural reorganization characterized by the formation of secondary carbonates and pozzolanic products (C-S-H and C-A-H), which contributed to matrix densification and improved mechanical resistance. Despite the persistence of certain evaporitic phases such as gypsum and halite, the 6/4% cement–lime mixture proved to be the most effective, confirming the suitability of this approach for the sustainable stabilization of sabkha soils.

The sabkha soils of the Aïn M’lila region present major geotechnical challenges, including high salinity, significant gypsum and soluble salt contents, and low mechanical strength. To improve their geotechnical performance, this study evaluated the effectiveness of cement–lime stabilization at various mixing ratios. Mechanical tests, including compressibility, California bearing ratio (CBR), and unconfined compressive strength (UCS), were conducted to identify the optimal dosage. The 6/4% cement–lime mixture exhibited the best performance, showing a significant increase in bearing capacity and soil cohesion. Based on these results, an X-ray diffraction (XRD) analysis was carried out exclusively on this optimal mixture to examine the associated mineralogical transformations. The XRD results revealed a pronounced structural reorganization characterized by the formation of secondary carbonates and pozzolanic products (C-S-H and C-A-H), which contributed to matrix densification and improved mechanical resistance. Despite the persistence of certain evaporitic phases such as gypsum and halite, the 6/4% cement–lime mixture proved to be the most effective, confirming the suitability of this approach for the sustainable stabilization of sabkha soils.

The sabkha soils of the Aïn M’lila region present major geotechnical challenges, including high salinity, significant gypsum and soluble salt contents, and low mechanical strength. To improve their geotechnical performance, this study evaluated the effectiveness of cement–lime stabilization at various mixing ratios. Mechanical tests, including compressibility, California bearing ratio (CBR), and unconfined compressive strength (UCS), were conducted to identify the optimal dosage. The 6/4% cement–lime mixture exhibited the best performance, showing a significant increase in bearing capacity and soil cohesion. Based on these results, an X-ray diffraction (XRD) analysis was carried out exclusively on this optimal mixture to examine the associated mineralogical transformations. The XRD results revealed a pronounced structural reorganization characterized by the formation of secondary carbonates and pozzolanic products (C-S-H and C-A-H), which contributed to matrix densification and improved mechanical resistance. Despite the persistence of certain evaporitic phases such as gypsum and halite, the 6/4% cement–lime mixture proved to be the most effective, confirming the suitability of this approach for the sustainable stabilization of sabkha soils.

The sabkha soils of the Aïn M’lila region present major geotechnical challenges, including high salinity, significant gypsum and soluble salt contents, and low mechanical strength. To improve their geotechnical performance, this study evaluated the effectiveness of cement–lime stabilization at various mixing ratios. Mechanical tests, including compressibility, California bearing ratio (CBR), and unconfined compressive strength (UCS), were conducted to identify the optimal dosage. The 6/4% cement–lime mixture exhibited the best performance, showing a significant increase in bearing capacity and soil cohesion. Based on these results, an X-ray diffraction (XRD) analysis was carried out exclusively on this optimal mixture to examine the associated mineralogical transformations. The XRD results revealed a pronounced structural reorganization characterized by the formation of secondary carbonates and pozzolanic products (C-S-H and C-A-H), which contributed to matrix densification and improved mechanical resistance. Despite the persistence of certain evaporitic phases such as gypsum and halite, the 6/4% cement–lime mixture proved to be the most effective, confirming the suitability of this approach for the sustainable stabilization of sabkha soils.

For modern enterprises to guarantee the quality and accuracy of their fundamental business data, master data management (MDM) is essential. However, a lack of connectivity between data governance and data quality throughout deployment is the reason why many MDM programs fail. This paper introduces a six-phase MDM framework that specifically integrates stakeholder-driven governance and data quality dimensions into each phase of the MDM lifecycle. Based on the PRAXEME methodology and designed using Business Process Model and Notation (BPMN), the framework emphasizes real-time monitoring and facilitates departmental collaboration. A healthcare case study illustrates its efficacy in enhancing data comprehensiveness, minimizing redundancy, and boosting operational productivity. The proposed framework offers a comprehensive approach that aligns business and IT objectives and reinforces process transparency.

For modern enterprises to guarantee the quality and accuracy of their fundamental business data, master data management (MDM) is essential. However, a lack of connectivity between data governance and data quality throughout deployment is the reason why many MDM programs fail. This paper introduces a six-phase MDM framework that specifically integrates stakeholder-driven governance and data quality dimensions into each phase of the MDM lifecycle. Based on the PRAXEME methodology and designed using Business Process Model and Notation (BPMN), the framework emphasizes real-time monitoring and facilitates departmental collaboration. A healthcare case study illustrates its efficacy in enhancing data comprehensiveness, minimizing redundancy, and boosting operational productivity. The proposed framework offers a comprehensive approach that aligns business and IT objectives and reinforces process transparency.

The selection of a fourth-generation fighter aircraft for the Algerian Air Force is a complex multicriteria decision-making (MCDM) process, marked by ambiguity and subjectivity. In the face of such challenges, the hybrid framework has applied the fuzzy analytic hierarchy process (fuzzy AHP), Shannon entropy, and fuzzy technique for order of preference by similarity to ideal solution. This approach utilizes linguistic variables represented by triangular fuzzy numbers to enhance adaptability and transparency in the decision-making process. Fuzzy AHP and entropy methods calculate the weights of selection criteria, while fuzzy TOPSIS ranks alternatives. The study is applied on a set of aircrafts, including SU-34, MIG-29, Rafale, and F-16, using technical and operational criteria such as range, autonomy, and climb rate. Results indicate that SU-34 is the optimal choice when using fuzzy AHP, while the F-16 ranks highest with the entropy method, with minimal score differences underscoring the precision and reliability of the methodology. This study overcomes traditional limitations, such as subjective bias and inflexibility, by leveraging fuzzy logic, enabling systematic assessments aligned with real-world constraints. The findings refine procurement decisions and provide a framework for balanced military evaluations. Future work may extend to advanced aircraft or unmanned systems.

The selection of a fourth-generation fighter aircraft for the Algerian Air Force is a complex multicriteria decision-making (MCDM) process, marked by ambiguity and subjectivity. In the face of such challenges, the hybrid framework has applied the fuzzy analytic hierarchy process (fuzzy AHP), Shannon entropy, and fuzzy technique for order of preference by similarity to ideal solution. This approach utilizes linguistic variables represented by triangular fuzzy numbers to enhance adaptability and transparency in the decision-making process. Fuzzy AHP and entropy methods calculate the weights of selection criteria, while fuzzy TOPSIS ranks alternatives. The study is applied on a set of aircrafts, including SU-34, MIG-29, Rafale, and F-16, using technical and operational criteria such as range, autonomy, and climb rate. Results indicate that SU-34 is the optimal choice when using fuzzy AHP, while the F-16 ranks highest with the entropy method, with minimal score differences underscoring the precision and reliability of the methodology. This study overcomes traditional limitations, such as subjective bias and inflexibility, by leveraging fuzzy logic, enabling systematic assessments aligned with real-world constraints. The findings refine procurement decisions and provide a framework for balanced military evaluations. Future work may extend to advanced aircraft or unmanned systems.

The selection of a fourth-generation fighter aircraft for the Algerian Air Force is a complex multicriteria decision-making (MCDM) process, marked by ambiguity and subjectivity. In the face of such challenges, the hybrid framework has applied the fuzzy analytic hierarchy process (fuzzy AHP), Shannon entropy, and fuzzy technique for order of preference by similarity to ideal solution. This approach utilizes linguistic variables represented by triangular fuzzy numbers to enhance adaptability and transparency in the decision-making process. Fuzzy AHP and entropy methods calculate the weights of selection criteria, while fuzzy TOPSIS ranks alternatives. The study is applied on a set of aircrafts, including SU-34, MIG-29, Rafale, and F-16, using technical and operational criteria such as range, autonomy, and climb rate. Results indicate that SU-34 is the optimal choice when using fuzzy AHP, while the F-16 ranks highest with the entropy method, with minimal score differences underscoring the precision and reliability of the methodology. This study overcomes traditional limitations, such as subjective bias and inflexibility, by leveraging fuzzy logic, enabling systematic assessments aligned with real-world constraints. The findings refine procurement decisions and provide a framework for balanced military evaluations. Future work may extend to advanced aircraft or unmanned systems.

Nowadays, the increasing tendency towards the exploitation of solar energy has yielded many technological advancements. Hybrid approaches are attracting attention worldwide to ensure the comprehensive assessment of photovoltaic modules reliability becoming a crucial issue. The present study is dedicated to the investigation of an innovative approach integrating Bond graph theory, Gaussian mixture models and Similarity-based method for fault detection and remaining useful life prediction. In this context, Bond graphs are exploited first to create a dataset covering diverse operational modes of the system. The identification and evaluation of critical sensors for fault observability is also considered, where the dataset is optimized based on the variance analysis. The Gaussian mixture model with its semi-supervised initialization is then utilized for clustering and fault diagnosis, while remaining useful life estimation is performed using a pairwise similarity technique. Validation results on a photovoltaic panel model demonstrate that the Gaussian mixture model consistently outperforms the classical k-Nearest Neighbors model across all key metrics (accuracy of 0.9396 vs. 0.7577, precision of 0.9192 vs. 0.5570, recall of 0.7849 vs. 0.5628, and F1-score of 0.8666 vs. 0.6707), highlighting its superior performance. The remaining useful lifetime model also achieves high accuracy, with Root Mean Square Error values ranging from 0.0282 to 0.0300, indicating minimal prediction error. Additionally, the R-Squared value of ~0.92 shows that the model explains approximately 92% of the variance in remaining useful lifetime predictions, underscoring its strong predictive capability. The results demonstrate the practical effectiveness of the proposed framework for both single and multiple faults. However, some limitations are noted, such as the exclusion of the transition phase in training data and the reliance on controlled conditions. The outcomes of this work are expected to provide valuable insights into the implementation of efficient hybrid frameworks, contributing to the sustainable development of solar energy.

Nowadays, the increasing tendency towards the exploitation of solar energy has yielded many technological advancements. Hybrid approaches are attracting attention worldwide to ensure the comprehensive assessment of photovoltaic modules reliability becoming a crucial issue. The present study is dedicated to the investigation of an innovative approach integrating Bond graph theory, Gaussian mixture models and Similarity-based method for fault detection and remaining useful life prediction. In this context, Bond graphs are exploited first to create a dataset covering diverse operational modes of the system. The identification and evaluation of critical sensors for fault observability is also considered, where the dataset is optimized based on the variance analysis. The Gaussian mixture model with its semi-supervised initialization is then utilized for clustering and fault diagnosis, while remaining useful life estimation is performed using a pairwise similarity technique. Validation results on a photovoltaic panel model demonstrate that the Gaussian mixture model consistently outperforms the classical k-Nearest Neighbors model across all key metrics (accuracy of 0.9396 vs. 0.7577, precision of 0.9192 vs. 0.5570, recall of 0.7849 vs. 0.5628, and F1-score of 0.8666 vs. 0.6707), highlighting its superior performance. The remaining useful lifetime model also achieves high accuracy, with Root Mean Square Error values ranging from 0.0282 to 0.0300, indicating minimal prediction error. Additionally, the R-Squared value of ~0.92 shows that the model explains approximately 92% of the variance in remaining useful lifetime predictions, underscoring its strong predictive capability. The results demonstrate the practical effectiveness of the proposed framework for both single and multiple faults. However, some limitations are noted, such as the exclusion of the transition phase in training data and the reliance on controlled conditions. The outcomes of this work are expected to provide valuable insights into the implementation of efficient hybrid frameworks, contributing to the sustainable development of solar energy.

Nowadays, the increasing tendency towards the exploitation of solar energy has yielded many technological advancements. Hybrid approaches are attracting attention worldwide to ensure the comprehensive assessment of photovoltaic modules reliability becoming a crucial issue. The present study is dedicated to the investigation of an innovative approach integrating Bond graph theory, Gaussian mixture models and Similarity-based method for fault detection and remaining useful life prediction. In this context, Bond graphs are exploited first to create a dataset covering diverse operational modes of the system. The identification and evaluation of critical sensors for fault observability is also considered, where the dataset is optimized based on the variance analysis. The Gaussian mixture model with its semi-supervised initialization is then utilized for clustering and fault diagnosis, while remaining useful life estimation is performed using a pairwise similarity technique. Validation results on a photovoltaic panel model demonstrate that the Gaussian mixture model consistently outperforms the classical k-Nearest Neighbors model across all key metrics (accuracy of 0.9396 vs. 0.7577, precision of 0.9192 vs. 0.5570, recall of 0.7849 vs. 0.5628, and F1-score of 0.8666 vs. 0.6707), highlighting its superior performance. The remaining useful lifetime model also achieves high accuracy, with Root Mean Square Error values ranging from 0.0282 to 0.0300, indicating minimal prediction error. Additionally, the R-Squared value of ~0.92 shows that the model explains approximately 92% of the variance in remaining useful lifetime predictions, underscoring its strong predictive capability. The results demonstrate the practical effectiveness of the proposed framework for both single and multiple faults. However, some limitations are noted, such as the exclusion of the transition phase in training data and the reliance on controlled conditions. The outcomes of this work are expected to provide valuable insights into the implementation of efficient hybrid frameworks, contributing to the sustainable development of solar energy.

Nowadays, the increasing tendency towards the exploitation of solar energy has yielded many technological advancements. Hybrid approaches are attracting attention worldwide to ensure the comprehensive assessment of photovoltaic modules reliability becoming a crucial issue. The present study is dedicated to the investigation of an innovative approach integrating Bond graph theory, Gaussian mixture models and Similarity-based method for fault detection and remaining useful life prediction. In this context, Bond graphs are exploited first to create a dataset covering diverse operational modes of the system. The identification and evaluation of critical sensors for fault observability is also considered, where the dataset is optimized based on the variance analysis. The Gaussian mixture model with its semi-supervised initialization is then utilized for clustering and fault diagnosis, while remaining useful life estimation is performed using a pairwise similarity technique. Validation results on a photovoltaic panel model demonstrate that the Gaussian mixture model consistently outperforms the classical k-Nearest Neighbors model across all key metrics (accuracy of 0.9396 vs. 0.7577, precision of 0.9192 vs. 0.5570, recall of 0.7849 vs. 0.5628, and F1-score of 0.8666 vs. 0.6707), highlighting its superior performance. The remaining useful lifetime model also achieves high accuracy, with Root Mean Square Error values ranging from 0.0282 to 0.0300, indicating minimal prediction error. Additionally, the R-Squared value of ~0.92 shows that the model explains approximately 92% of the variance in remaining useful lifetime predictions, underscoring its strong predictive capability. The results demonstrate the practical effectiveness of the proposed framework for both single and multiple faults. However, some limitations are noted, such as the exclusion of the transition phase in training data and the reliance on controlled conditions. The outcomes of this work are expected to provide valuable insights into the implementation of efficient hybrid frameworks, contributing to the sustainable development of solar energy.

The purpose of this article was to optimise the cost of the energy consumption of an industrial complex that was using three energy sources: industrial electricity, natural gas, and solar energy. Before undertaking any optimisation action, we carried out an in-depth examination of the company’s energy system. This action was positioned as an essential pivot for collecting energy data according to: Source (gas, electricity); Equipment or production tools; Usage (heating, cooling, lighting, ventilation, etc.). The in-depth examination of the company’s energy system made it possible to identify the energy consumption of the equipment and the costs associated with it for a year of operation. With an objective of energy and economic performance, the simplex algorithm was implemented to resolve the energy mix model and machine hours, according to the two-phase technique applied first to the energy problem and second to resolvingthe time problem.