The turbine blades are subjected to high operating temperatures and high centrifugal tensile stress due to rotational speeds. The maximum temperature at the inlet of the turbine is currently limited by the resistance of the materials used for the blades. The present paper is focused on the thermo-mechanical behavior of the blade in composite materials with reinforced mast under two different types of loading. The material studied in this work is a composite material, the selected matrix is a technical ceramic which is alumina (aluminum oxide Al₂O₃) and the reinforcement is carried out by short fibers of high modulus carbon to optimize a percentage of 40% carbon and 60% of ceramics. The simulation was performed numerically by Ansys (Workbench 16.0) software. The comparative analysis was conducted to determine displacements, strains and Von Mises stress of composite material and then compared to other materials such as Titanium Alloy, Stainless Steel Alloy, and Aluminum 2024 Alloy. The results were compared in order to select the material with the best performance in terms of rigidity under thermo-mechanical stresses. While comparing these materials, it is found that composite material is better suited for high temperature applications. On evaluating the graphs drawn for, strains and displacements, the blade in composite materials reinforced with mast is considered as optimum.

The turbine blades are subjected to high operating temperatures and high centrifugal tensile stress due to rotational speeds. The maximum temperature at the inlet of the turbine is currently limited by the resistance of the materials used for the blades. The present paper is focused on the thermo-mechanical behavior of the blade in composite materials with reinforced mast under two different types of loading. The material studied in this work is a composite material, the selected matrix is a technical ceramic which is alumina (aluminum oxide Al₂O₃) and the reinforcement is carried out by short fibers of high modulus carbon to optimize a percentage of 40% carbon and 60% of ceramics. The simulation was performed numerically by Ansys (Workbench 16.0) software. The comparative analysis was conducted to determine displacements, strains and Von Mises stress of composite material and then compared to other materials such as Titanium Alloy, Stainless Steel Alloy, and Aluminum 2024 Alloy. The results were compared in order to select the material with the best performance in terms of rigidity under thermo-mechanical stresses. While comparing these materials, it is found that composite material is better suited for high temperature applications. On evaluating the graphs drawn for, strains and displacements, the blade in composite materials reinforced with mast is considered as optimum.

Volunteer computing (VC) has become a relatively mature technique of distributed computing. It is based on exploiting the idle time of ordinary online machines with the consent of their owners. Target applications are generally scientific projects requiring a huge amount of computational resources. Existing VC platforms raise several challenges. This work attempts to bring solutions for two defeats. The first one is the involvement of volunteers; the decreasing of participants affects the global performances. To cope with this, a new social volunteer computing environment is proposed in order to involve more volunteers. The second addressed problem is the task scheduling, which aims to optimize the use of resources. The proposed algorithm generates for each resource's class, a number of tasks whose cost of execution reflects the momentary capacity of the resources. The new solutions are validated through a theory of number's project, called “Collatz Conjecture.”

Volunteer computing (VC) has become a relatively mature technique of distributed computing. It is based on exploiting the idle time of ordinary online machines with the consent of their owners. Target applications are generally scientific projects requiring a huge amount of computational resources. Existing VC platforms raise several challenges. This work attempts to bring solutions for two defeats. The first one is the involvement of volunteers; the decreasing of participants affects the global performances. To cope with this, a new social volunteer computing environment is proposed in order to involve more volunteers. The second addressed problem is the task scheduling, which aims to optimize the use of resources. The proposed algorithm generates for each resource's class, a number of tasks whose cost of execution reflects the momentary capacity of the resources. The new solutions are validated through a theory of number's project, called “Collatz Conjecture.”

Introduction

The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal.

Methods

The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal.

Results

Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets.

Conclusion

The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

Introduction

The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal.

Methods

The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal.

Results

Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets.

Conclusion

The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

Introduction

The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal.

Methods

The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal.

Results

Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets.

Conclusion

The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to achieve the national strategic agenda’s criteria that aim for accomplishing sustainable buildings by estimating the effects of energy efficiency measures in order to reduce energy consumption and CO2 emission. Design/methodology/approach A design approach has been developed based on simulation software and a modeled building. Therefore, a typical office building is considered for testing five efficiency measures in three climatic conditions in Algeria. This approach is conducted in two phases: first, the analysis of each measure’s effect is independently carried out in terms of cooling energy and heating energy intensities. Then, a combination of optimal measures for each climate zone is measured in terms of three sustainable indicators: final energy consumption, energy cost saving and CO2 emission. Findings The results reveal that a combination of optimal measures has a substantial impact on building energy saving and CO2 emission. This saving can rise to 41 and 31 percent in a hot and cold climate, respectively. Furthermore, it is concluded that obtaining higher building performance, different design alternatives should be adapted to the climate proprieties and the local construction materials must be applied. Originality/value This study is considered as an opportunity for achieving the national strategy, as it may contribute in improving office building performance and demonstrating a suitable tool to assist stakeholders in the decision making of most important parameters in the design stage for new or retrofit buildings.

Purpose The purpose of this paper is to achieve the national strategic agenda’s criteria that aim for accomplishing sustainable buildings by estimating the effects of energy efficiency measures in order to reduce energy consumption and CO2 emission. Design/methodology/approach A design approach has been developed based on simulation software and a modeled building. Therefore, a typical office building is considered for testing five efficiency measures in three climatic conditions in Algeria. This approach is conducted in two phases: first, the analysis of each measure’s effect is independently carried out in terms of cooling energy and heating energy intensities. Then, a combination of optimal measures for each climate zone is measured in terms of three sustainable indicators: final energy consumption, energy cost saving and CO2 emission. Findings The results reveal that a combination of optimal measures has a substantial impact on building energy saving and CO2 emission. This saving can rise to 41 and 31 percent in a hot and cold climate, respectively. Furthermore, it is concluded that obtaining higher building performance, different design alternatives should be adapted to the climate proprieties and the local construction materials must be applied. Originality/value This study is considered as an opportunity for achieving the national strategy, as it may contribute in improving office building performance and demonstrating a suitable tool to assist stakeholders in the decision making of most important parameters in the design stage for new or retrofit buildings.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.