This paper deals with multi-agent energy management and fault tolerant control of the micro-grid powered by wind farm based on two doubly fed induction generators. The stator flux orientation has used to eliminate the active and reactive power coupling. The proposed control scheme is based on two cascades closed loops. The inner controllers concern the rotor currents. The outer controllers have a parallel configuration with the stator voltage or the stator power control. Switching between these two controllers is realized by the synchronization mechanism. All controllers are designed with Lyapunov approach associated with sliding-mode control, this solution shows good robustness against parameter variations, measurement errors and faults. The global asymptotic stability of the overall system is proven. After that, a Multi-agent energy management was proposed and tested in order to satisfy some objectives and overcome some constraints. The advantages of the wind energy integration associated with multi-agent energy management are: production cost minimization, reduction of the carbon emissions, increasing the energy autonomy and he robustness against weather conditions and faults that may occur during operation. The results confirm the effectiveness of the proposed control.

This paper deals with multi-agent energy management and fault tolerant control of the micro-grid powered by wind farm based on two doubly fed induction generators. The stator flux orientation has used to eliminate the active and reactive power coupling. The proposed control scheme is based on two cascades closed loops. The inner controllers concern the rotor currents. The outer controllers have a parallel configuration with the stator voltage or the stator power control. Switching between these two controllers is realized by the synchronization mechanism. All controllers are designed with Lyapunov approach associated with sliding-mode control, this solution shows good robustness against parameter variations, measurement errors and faults. The global asymptotic stability of the overall system is proven. After that, a Multi-agent energy management was proposed and tested in order to satisfy some objectives and overcome some constraints. The advantages of the wind energy integration associated with multi-agent energy management are: production cost minimization, reduction of the carbon emissions, increasing the energy autonomy and he robustness against weather conditions and faults that may occur during operation. The results confirm the effectiveness of the proposed control.

Amniotic band syndrome (ABS) was first described by Montgomery in Montgomery (1832). It is a poorly known congenital malformation due to strangulation of the organs by an amniotic fibrous band. Several parts of the body can be affected: for instance, skull, face, neck, trunk and musculoskeletal system. It generally associates three types of anomalies namely, amputations, deformities, and malformations. There are two genuine theories covering this syndrome; the Intrinsic Theory associating the syndrome to a germline defect and the Purely Mechanical Extrinsic Theory related to the amniotic band. These theories have thoroughly tried to explain the disease and the organ involvement (Goldfarb et al., 2009). In the current study, we report a rare case of an open fracture of both leg bones with amniotic disease in a 10-day-old neonate who underwent surgical treatment. In our case, it is a surgical emergency where we try to explain its physiopathology and show how to operate it. We discuss likewise the appropriateness of using the expressions “leg fracture” and “congenital pseudarthrosis of the leg”. Finally, we describe a revised classification by Hall (1982) and Weinzweig (1994) of ABS incorporating a stage with bone involvement.

Amniotic band syndrome (ABS) was first described by Montgomery in Montgomery (1832). It is a poorly known congenital malformation due to strangulation of the organs by an amniotic fibrous band. Several parts of the body can be affected: for instance, skull, face, neck, trunk and musculoskeletal system. It generally associates three types of anomalies namely, amputations, deformities, and malformations. There are two genuine theories covering this syndrome; the Intrinsic Theory associating the syndrome to a germline defect and the Purely Mechanical Extrinsic Theory related to the amniotic band. These theories have thoroughly tried to explain the disease and the organ involvement (Goldfarb et al., 2009). In the current study, we report a rare case of an open fracture of both leg bones with amniotic disease in a 10-day-old neonate who underwent surgical treatment. In our case, it is a surgical emergency where we try to explain its physiopathology and show how to operate it. We discuss likewise the appropriateness of using the expressions “leg fracture” and “congenital pseudarthrosis of the leg”. Finally, we describe a revised classification by Hall (1982) and Weinzweig (1994) of ABS incorporating a stage with bone involvement.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

Hexavalent chromium (CrVI) compounds are potent toxicants commonly used in numerous industries. Thus, potential toxic effects and health hazards are of high relevance. Selenium (Se) and zinc (Zn) are known for their antioxidant and chemoprotective properties. However, little is known about their protective effects against CrVI-induced renal damage during pregnancy. In this context, the present study aimed to investigate the protective efficacy of these two essential elements against potassium dichromate-induced nephrotoxicity in pregnant Wistar Albino rats. Female rats were divided into control and four treated groups of six each receiving subcutaneously on the 3rd day of pregnancy, K₂Cr₂O₇ (10 mg/kg, s.c. single dose) alone, or in association with Se (0.3 mg/kg, s.c. single dose), ZnCl₂ (20 mg/kg, s.c. single dose) or both of them simultaneously. The nephrotoxic effects were monitored by the evaluation of plasma renal parameters, oxidative stress biomarkers, DNA damage, and renal Cr content. The obtained results showed that K₂Cr₂O₇ disturbed renal biochemical markers, induced oxidative stress and DNA fragmentation in kidney tissues, and altered renal histoarchitecture. The co-administration of Se and/or ZnCl₂ has exhibited pronounced chelative, antioxidant, and genoprotective effects against K₂Cr₂O₇-induced renal damage and attenuated partially the histopathological alterations. These results suggest that Se and Zn can be used as efficient nephroprotective agents against K₂Cr₂O₇-induced toxicity in pregnant Wistar Albino rats.

The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.

The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.

The aim of this study was to test the influence of nest site characteristics and food supplementation from rubbish dumps on reproductive parameters of white storks breeding in semi-arid habitats. A total of 148 nests were monitored in two colonies of white storks (control colony vs. colony that benefited from high food supply in rubbish dumps) in eastern Algeria over a six-year period (2011–2016) to measure nest characteristics and reproductive parameters (clutch size, number of hatchings, number of fledglings, breeding success). Results showed that pairs breeding at proximity from rubbish dumps had larger clutch sizes (5.1 ± 0.6 vs. 4.6 ± 0.6), hatched more chicks (4.7 ± 0.7 vs. 4.3 ± 0.7) and raised more fledglings (3.0 ± 0.9 vs. 2.6 ± 1.0) than pairs breeding far from rubbish dumps. Results also showed that clutch size was positively related to nest surface area, and that pairs nesting on electricity poles had a lower breeding success than those nesting in trees (48.9 ± 20.4% vs. 64.6 ± 17.6%). Our findings suggest that breeding outputs are strongly related to selective behavior in nest placement and food availability surrounding the nesting site.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

This paper proposes a new approach for remaining useful life prediction that combines a bond graph, the Gaussian Mixture Model and similarity techniques to allow the use of both physical knowledge and the data available. The proposed method is based on the identification of relevant variables that carry information on degradation. To this end, the causal properties of the bond graph (BG) are first used to identify the relevant sensors through the fault observability. Then, a second stage of analysis based on statistical metrics is performed to reduce the number of sensors to only the ones carrying useful information for failure prognosis, thus, optimizing the data to be used in the prognosis phase. To generate data in the different system state, a simulator based on the developed BG is used. A Gaussian Mixture Model is then applied on the generated data for fault diagnosis and clustering. The Remaining Useful Life is estimated using a similarity technique. An application on a mechatronic system is considered for highlighting the effectiveness of the proposed approach.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.

In the last recent years, a large community of researchers and industrial practitioners has been attracted by combining different prognostics models as such strategy results in boosted accuracy and robust performance compared to the exploitation of single models. The present work is devoted to the investigation of three new fusion schemes for the remaining useful life forecast. These integrated frameworks are based on aggregating a set of Gaussian process regression models thanks to the Induced Ordered Weighted Averaging Operators. The combination procedure is built upon three proposed analytical weighting schemes including exponential, logarithmic and inverse functions. In addition, the uncertainty aspect is supported in this work, where the proposed functions are used to weighted average the variances released from competitive Gaussian process regression models. The training data are transformed into gradient values, which are adopted as new training data instead of the original observations. A lithium-ion battery data set is used as a benchmark to prove the efficiency of the proposed weighting schemes. The obtained results are promising and may provide some guidelines for future advances in performing robust fusion options to accurately estimate the remaining useful life.