β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the bla_ESBL genes alone, fifteen E. coli isolates were positive for the bla_OXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of bla_NDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.

β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the bla_ESBL genes alone, fifteen E. coli isolates were positive for the bla_OXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of bla_NDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.

β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the bla_ESBL genes alone, fifteen E. coli isolates were positive for the bla_OXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of bla_NDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.

β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the bla_ESBL genes alone, fifteen E. coli isolates were positive for the bla_OXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of bla_NDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.

β-lactams and colistin resistance in Enterobacterales is a global public health issue. In this study we aimed to investigate the occurrence and genetic determinants of Extended-Spectrum β-lactamases, carbapenemases and mcr-encoding-genes in Enterobacterales isolates recovered from the migratory bird species Ciconia ciconia in an Algerian city. A total of 62 faecal samples from white storks were collected. Samples were then subjected to selective isolation of β-lactams and colistin-resistant-Enterobacterales. The representative colonies were identified using Matrix-Assisted Laser Desorption-Ionisation Time-of-Flight Mass Spectrometry. Susceptibility testing was performed using the disk-diffusion method. ESBL, carbapenemases, and colistin resistance determinants were searched for by PCR and sequencing. The clonality relationships of the obtained isolates were investigated by multilocus sequence typing assays. Mating experiments were carried out to evaluate the transferability of the carbapenemase and mcr-genes. Forty-two isolates were identified as follows: Escherichia coli (n = 33), Klebsiella pneumoniae (n = 4), Proteus mirabilis (n = 4) and Citrobacter freundii (n = 1). Molecular analysis showed that twelve isolates carried the bla_ESBL genes alone, fifteen E. coli isolates were positive for the bla_OXA-48 gene, six isolates were NDM-5-carriers (two P. mirabilis, two K. pneumoniae and two E. coli) and eight E. coli strains were positive for the mcr-1 gene. MLST results showed a high clonal diversity, where NDM-5-producing strains were assigned to two sequence types (ST167 for E. coli and ST198 for K. pneumoniae), whereas the mcr-1 positive E. coli isolates belonged to ST58, ST224, ST453, ST1286, ST2973, ST5542, ST9815 and the international high-risk resistant lineage ST101. To the best of our knowledge, this is the first report of bla_NDM-5 gene in white storks and also the first describing the mcr-1 gene in white storks in Algeria. This study underlines the important role of migratory white storks as carriers of high-level drug-resistant bacteria, allowing their possible implication as indicators and sentinels for antimicrobial resistance surveillance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

This paper presents the development of a multi-coil eddy current (EC) sensor that uses an axial rotating magnetic field for the measurement of electrical resistance to determine the electrical conductivity tensor of stratified carbon fiber reinforced polymer (CFRP) materials. The sensor consists of an identical planar racetrack multi-coil, excited by two-phase sinusoidal current sources that are 90° apart in phase to generate an axial rotating magnetic field and eliminate the need for mechanical rotation. Each sensor's coil's resistance variation is measured using a developed experimental prototype unit and computed using a 3D finite element method (FEM) based on the (A, V–A) formulation. The inverse problem technique that minimizes the difference between the calculated and measured resistances is then used to identify the electrical conductivity tensor components using the particle swarm optimization (PSO) algorithm. The comparison between the computed resistances and the measured ones shows an excellent concordance.

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.

Sebkha soils are defined as problem soils located in arid, semi-arid, and coastal areas. Generally, they are fine soil, composed of silt, sand, and clay, which are cemented by different salts (e.g., halite, gypsum, and calcite). In nature, sebkha saline soils are exposed to different drying and wetting (D-W) cycles. However, these cycles have a significant effect on the mechanical behavior of these soils. This study aims to characterize the chemical, mineralogical, and geotechnical properties of sebkha soil using an experimental approach. We focus on the effects of D-W cycles on the unconfined compressive strength (UCS) and salinity of sebkha soils from Ain M'Lila, Algeria. In addition, these D-W cycles were applied to the samples dried in the open air to achieve the targeted water content (water content values of 7%, 11.4%, and 13%). The results obtained show that the UCS increases with decrease in water content and decreases with an increase in the number of D-W cycles. In addition, these cycles affect the salinity of the sebkha soil. Indeed, a significant decrease in soil salinity was recorded with an increase in the number of D-W cycles. Finally, a relationship was found between the salinity of the soil and UCS. The latter decreases with a decrease in soil salinity; this relationship becomes very significant for low water content values of 7% or less.