Purpose The assessment of patient safety culture (PSC) is a major priority for healthcare providers. It is often realized using quantitative approaches (questionnaires) separately from qualitative ones (patient safety culture maturity model (PSCMM)). These approaches suffer from certain major limits. Therefore, the aim of the present study is to overcome these limits and to propose a novel approach to PSC assessment. Design/methodology/approach The proposed approach consists of evaluating PSC in a set of healthcare establishments (HEs) using the HSOPSC questionnaire. After that, principal component analysis (PCA) and K-means algorithm were applied on PSC dimensional scores in order to aggregate them into macro dimensions. The latter were used to overcome the limits of PSC dimensional assessment and to propose a quantitative PSCMM. Findings PSC dimensions are grouped into three macro dimensions. Their capitalization permits their association with safety actors related to PSC promotion. Consequently, a quantitative PSC maturity matrix was proposed. Problematic PSC dimensions for the studied HEs are “Non-punitive response to error”, “Staffing”, “Communication openness”. Their PSC maturity level was found underdeveloped due to a managerial style that favors a “blame culture”. Originality/value A combined quali-quantitative assessment framework for PSC was proposed in the present study as recommended by a number of researchers but, to the best of our knowledge, few or no studies were devoted to it. The results can be projected for improvement and accreditation purposes, where different PSC stakeholders can be implicated as suggested by international standards.

Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

This paper presents a decision-making support system for situation risk assessment associated with critical alarms conditions in a gas facility. The system provides a human operator with advice on the confirmation and classification of occurred alarm. The input of the system comprises uncertain and incomplete information. In the light of uncertain and incomplete information, different uncertainties laws have been associated with the probabilistic assessment of the system loops which combine data of several sources to reach the ultimate classification. The implemented model used Observe-Orient-Decide-Act loop (OODA) combined with Bayesian networks. Results show that the system can classify the alarms system.

This paper presents a decision-making support system for situation risk assessment associated with critical alarms conditions in a gas facility. The system provides a human operator with advice on the confirmation and classification of occurred alarm. The input of the system comprises uncertain and incomplete information. In the light of uncertain and incomplete information, different uncertainties laws have been associated with the probabilistic assessment of the system loops which combine data of several sources to reach the ultimate classification. The implemented model used Observe-Orient-Decide-Act loop (OODA) combined with Bayesian networks. Results show that the system can classify the alarms system.

Purpose The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs. Design/methodology/approach The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system. Findings The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system. Originality/value The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Purpose The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs. Design/methodology/approach The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system. Findings The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system. Originality/value The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Purpose The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs. Design/methodology/approach The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system. Findings The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system. Originality/value The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Purpose The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs. Design/methodology/approach The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system. Findings The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system. Originality/value The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Purpose The objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria. Design/methodology/approach The SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient. Findings The study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation. Originality/value The study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

Various facilities are used in mineral processing to prepare raw material. Practically, two types of balls are used, cast balls and forged balls. They are respectively made from high chromium cast iron and forged steel and are supplied in different sizes and chemical compositions. The cast and forged balls have different microstructures and consequently display dissimilar wear behavior. The target aimed in this work is to achieve a comparative study taking into account the type of microstructure, mechanical properties, and wear behavior of these two kinds of materials. Specimens have undergone chemical, metallographic and XRD characterizations. Subsequently, these samples were subjected to hardness measurements, abrasion and friction tests in order to evaluate their wear behaviour. Tribological tests, under unlubricated environment, are carried out on both types of grinding balls in order to study the wear system. Corrosion tests are also performed on forged steel and high chromium cast iron ball samples. The obtained results reveal a large difference in terms of chemical composition and microstructural components. Chromium cast iron balls are more resistant to friction, whereas forged balls are more resistant to abrasion. Additionally, the corrosion tests reveal a narrow discrepancy in corrosion behaviour between the studied materials.

The exploitation of fossil fuels is causing global warming whose negative effects have recently been felt all over the world. Therefore, the search for new sources of energy, renewable and respectful of the environment is crucial for manufacturers. The concept of Best Available Techniques (BAT) presents an adequate solution for manufacturers, for the control, elimination or reduction of the harmful impacts of their activities on the environment. This concept, known as Integrated Pollution Prevention and Control (IPPC), was introduced and imposed from 1996 in Europe. This paper aims to introduce the possibility of transferring the IPPC approach and BAT concepts to Algeria. Therefore, the main objective is to propose some recommendations to optimize the technical and environmental performance of hybrid solar-gas systems, by treating as a case study the first hybrid solar-gas power plant SPPI (Solar Power Plant One) near Hassi R’mel in the south of Algeria. A gap analysis of the Algerian environmental policy compared to the IPPC system, and an assessment of technical and environmental performance of the “SPPI” plant in terms of regulation and BAT are developed in our study.