In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.

In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.

In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.

Background

Miscarriage is defined as an adverse and unexpected termination of pregnancy; it is the most frequent pregnancy complication. Here, we aimed to identify the factors predisposing to miscarriage in pregnant women in Eastern Algeria and the effect of the combination of several factors, including maternal Body Mass Index (BMI), maternal age, concomitant pathologies, and nutrients, and to predict the occurrence of miscarriage.

Methods

A total of 786 pregnant women from Eastern Algeria were interviewed between 2011 and 2015. Association between miscarriage exposure and identified risk factors was assessed using a Generalized Linear Model (GLM), ANOVA test, Multiple Correspondence Analysis (MCA), and Hierarchical Clustering Analysis (HCA). Throughout this study, we sought to find answers, discuss this association, and predict the occurrence of miscarriage.

Results

We developed a predictive model for miscarriage, and we found that miscarriage was significantly higher for pregnant women aged over 35 years (1.75; 95% CI: 0.75–4.37; p = 0.208), with a high BMI (> 25 kg/m²), (1.88; 95% CI:1.28–2.78; p = 0.001). We have highlighted that miscarriage is strongly associated with hypertension (1.67; 95% CI: 1.16–2.39; p = 0.006), diet rich in meat (0.60; 95% CI: 0.33–1.04; p = 0.075), and moderate in fish (2.32; 95% CI: 1.18–4.58; p = 0.015).

Conclusion

Our study proved that knowing these risk factors helps to establish predictive models and strategies to prevent tragic pregnancy outcomes and highlights the link between miscarriage and several risk factors; and thus, will allow protecting mother and fetus health.

Background

Miscarriage is defined as an adverse and unexpected termination of pregnancy; it is the most frequent pregnancy complication. Here, we aimed to identify the factors predisposing to miscarriage in pregnant women in Eastern Algeria and the effect of the combination of several factors, including maternal Body Mass Index (BMI), maternal age, concomitant pathologies, and nutrients, and to predict the occurrence of miscarriage.

Methods

A total of 786 pregnant women from Eastern Algeria were interviewed between 2011 and 2015. Association between miscarriage exposure and identified risk factors was assessed using a Generalized Linear Model (GLM), ANOVA test, Multiple Correspondence Analysis (MCA), and Hierarchical Clustering Analysis (HCA). Throughout this study, we sought to find answers, discuss this association, and predict the occurrence of miscarriage.

Results

We developed a predictive model for miscarriage, and we found that miscarriage was significantly higher for pregnant women aged over 35 years (1.75; 95% CI: 0.75–4.37; p = 0.208), with a high BMI (> 25 kg/m²), (1.88; 95% CI:1.28–2.78; p = 0.001). We have highlighted that miscarriage is strongly associated with hypertension (1.67; 95% CI: 1.16–2.39; p = 0.006), diet rich in meat (0.60; 95% CI: 0.33–1.04; p = 0.075), and moderate in fish (2.32; 95% CI: 1.18–4.58; p = 0.015).

Conclusion

Our study proved that knowing these risk factors helps to establish predictive models and strategies to prevent tragic pregnancy outcomes and highlights the link between miscarriage and several risk factors; and thus, will allow protecting mother and fetus health.

Background

Miscarriage is defined as an adverse and unexpected termination of pregnancy; it is the most frequent pregnancy complication. Here, we aimed to identify the factors predisposing to miscarriage in pregnant women in Eastern Algeria and the effect of the combination of several factors, including maternal Body Mass Index (BMI), maternal age, concomitant pathologies, and nutrients, and to predict the occurrence of miscarriage.

Methods

A total of 786 pregnant women from Eastern Algeria were interviewed between 2011 and 2015. Association between miscarriage exposure and identified risk factors was assessed using a Generalized Linear Model (GLM), ANOVA test, Multiple Correspondence Analysis (MCA), and Hierarchical Clustering Analysis (HCA). Throughout this study, we sought to find answers, discuss this association, and predict the occurrence of miscarriage.

Results

We developed a predictive model for miscarriage, and we found that miscarriage was significantly higher for pregnant women aged over 35 years (1.75; 95% CI: 0.75–4.37; p = 0.208), with a high BMI (> 25 kg/m²), (1.88; 95% CI:1.28–2.78; p = 0.001). We have highlighted that miscarriage is strongly associated with hypertension (1.67; 95% CI: 1.16–2.39; p = 0.006), diet rich in meat (0.60; 95% CI: 0.33–1.04; p = 0.075), and moderate in fish (2.32; 95% CI: 1.18–4.58; p = 0.015).

Conclusion

Our study proved that knowing these risk factors helps to establish predictive models and strategies to prevent tragic pregnancy outcomes and highlights the link between miscarriage and several risk factors; and thus, will allow protecting mother and fetus health.

As compared to a fixed Photovoltaic (PV) system, a two axis solar tracker system can increase electrical energy production from 35% to 45% in a year. Vibration characteristic is an essential factor in evaluating the reliability and stability of solar tracker structure during operating course. In this paper, the free vibration behaviour (modal analysis) of 12 kW two axis PV solar tracker structure is investigated numerically. The modal analysis by using commercial finite element package (SOLIDWORKS SIMULATION) to identify the modal parameters of the tracker structure (natural frequencies and corresponding modal shapes). The simulation results obtained for tracker structure at maximum elevation angle (60deg) indicate that no resonant problem (according to ASHRAE Standard) during solar tracker operation under wind load (from 0 to 36 m/s).

As compared to a fixed Photovoltaic (PV) system, a two axis solar tracker system can increase electrical energy production from 35% to 45% in a year. Vibration characteristic is an essential factor in evaluating the reliability and stability of solar tracker structure during operating course. In this paper, the free vibration behaviour (modal analysis) of 12 kW two axis PV solar tracker structure is investigated numerically. The modal analysis by using commercial finite element package (SOLIDWORKS SIMULATION) to identify the modal parameters of the tracker structure (natural frequencies and corresponding modal shapes). The simulation results obtained for tracker structure at maximum elevation angle (60deg) indicate that no resonant problem (according to ASHRAE Standard) during solar tracker operation under wind load (from 0 to 36 m/s).

As compared to a fixed Photovoltaic (PV) system, a two axis solar tracker system can increase electrical energy production from 35% to 45% in a year. Vibration characteristic is an essential factor in evaluating the reliability and stability of solar tracker structure during operating course. In this paper, the free vibration behaviour (modal analysis) of 12 kW two axis PV solar tracker structure is investigated numerically. The modal analysis by using commercial finite element package (SOLIDWORKS SIMULATION) to identify the modal parameters of the tracker structure (natural frequencies and corresponding modal shapes). The simulation results obtained for tracker structure at maximum elevation angle (60deg) indicate that no resonant problem (according to ASHRAE Standard) during solar tracker operation under wind load (from 0 to 36 m/s).

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

The behaviour of shallow foundations on slopes is an important topic of interest in geotechnical engineering. This paper presents the results of laboratory model tests of an eccentrically loaded strip footing on a slope. Experiments were conducted with an eccentrically loaded model footing under various eccentricity ratios (±e/B) and normalized footing distances (d/B) and the results were compared with previous literature. The results confirm that the load eccentricity and normalized footing distance have considerable effects on the drained bearing capacity. The ultimate bearing capacity of a negative eccentric load is greater than that of a positive eccentric load up to a relative distance of d/B = 3. At this point, the bearing capacity becomes almost the same regardless of the eccentricity location relative to the slope face. Furthermore, the failure mechanism is not symmetrical; a greater failure surface length can develop on the slope side and this length decreases with increasing eccentricity.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.

Seismic deformation assessments are an ongoing issue in the design, monitoring and construction of earth dams. The need for new advanced methods to model their seismic behavior and to evaluate the resulting deformations is justified by the uncertainties surrounding conventional methods, mainly, with liquefaction phenomena. In this respect, the present study focuses on the prediction of relative crest settlement of embankment dams under variant earthquake loading (Δh_EQ/H). For this purpose, Back-Propagation Neural Network (BPNN) and Multivariate Adaptive Regression Splines (MARS) models were developed to predict (Δh_EQ/H). Two different databases of historically documented earthquake cases are collected for model development and comparative performance between model predictions. The first contains 151 observations of liquefied and non-liquefied cases, while the second contains only 109 non-liquefied cases. The obtained results indicated that both technics could be used as reliable tools to predict the earthquake-related crest settlement in embankment dams. Also, MARS was selected as the most successful prediction tool.