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Abstract Background Peripartum cardiomyopathy (PPCM) is a rare but potentially life-threatening condition. Due to the scarcity of epidemiological data in North Africa, we conducted this study to assess the outcomes of PPCM in our region. Materials and methods This monocentric retrospective cohort study involving all patients diagnosed with PPCM was conducted between January 2010 and December 2022. Results Twenty-seven PPCM patients, with a median age of 33 years (Interquartile range (IQR) = 9), were included. 52% of patients were diagnosed during the postpartum period. Dyspnea New York Heart Association III/IV(NYHA III/IV) was the most common functional symptom (85%). The median left ventricle ejection fraction (LVEF) was 30% (IQR = 11%). Atrial fibrillation occurred in 11.1% of patients, thromboembolic complications occurred in 18.5%, Pulmonary edema occurred in 85% of patients and cardiogenic shock occurred in 14.8% of patients, with two patients requiring Extracorporeal Membrane Oxygenation (ECMO) support. The predominant mode of delivery was a cesarean Sect. (82% of patients), and the indication for a cesarean delivery was obstetrical in 59% of patients. Prematurity occurred in 36% of newborns, and intrauterine fetal death occurred in one pregnancy. The median follow-up was 24 months [6–144 months]. LVEF recovery was noted in 67% of patients. Bromocriptine was administered to six patients (22%), and none of these patients died and out of them, five patients recovered their LVEF (83%). The overall mortality rate during the follow-up period was 15%, comprising three in-hospital cardiac deaths and one occurring two years later due to refractory advanced heart failure. No significant differences were observed between LVEF recovery and LVEF non-recovery groups. Factors significantly associated with mortality were multiparity, poor antenatal care (ANC) attendance, thromboembolic events, cardiogenic shock, and Pulmonary edema. Five patients with LV recovery subsequently became pregnant, none experienced a relapse of PPCM. However, one of the descendants of a patient was diagnosed with dilated cardiomyopathy. Conclusion This study revealed that the diagnosis of PPCM in our hospital is often delayed until symptoms become more advanced, resulting in high morbi-mortality.

Endometriosis is the presence of endometrial tissue in abnormal locations outside the uterine cavity. These locations are usually the ovaries, the peritoneum, and the uterine ligaments. Less frequently, the endometrial tissue can affect the perineum especially after surgical procedures or obstetric lesions. In this case report, we describe the case of a patient suffering from pain and swelling, with a sizeable nodule in an old episiotomy scar. Clinical examination, ultrasonography (USG) and magnetic resonance imaging (MRI) helped evoke the diagnosis of perineal endometriosis, and there were no signs of other endometriosis locations. Hormonal treatment was provided at first, but there was no clinical improvement after three months, so the treatment had to be surgical. Histopathological examination of the endometriotic mass confirmed the diagnosis. There were no immediate postoperative complications, and no clinical symptoms or recurrence signs six months and one year after.

Objective: To describe for the first time the epidemiologic and clinico-pathologic characteristics of vulvar cancer in Tunisia. Design: Two parts are distinguished in this study: Part1: Multicentric retrospective study about the characteristics of all cancer cases diagnosed during a 17-years period (January 1998–December 2014) in three departments of Gynecology and Obstetrics: one in south Tunisia and two in the capital. Part 2: To determine the Incidence trend of invasive vulvar cancer in North Tunisia 1994–2009, on the basis of North Cancer Registry of Tunisia. Results: A total of 76 cases of vulvar cancer were recorded. The median age at diagnosis was 65.4 years and 86.9% of patients were more than 55 years old. The symptomatology was dominated by vulvar pruritus in 48.7%. The average size of the tumor was 3.96 cm. Stage III was the most frequent (53.7%) followed by stage II (28.3%). Only 10.4% of tumors were at stage I. The most common histologic type of vulvar malignancy was squamous cell carcinoma (SCC) (94.7%). Standardized incidence varied from 1.2/100 000 (1994) to 0.5/100 000 (2009). There was significant decrease of Standardized incidence (APC of −8.8% per year, 95% CI: −5.5%, −9.0%–p

Abstract Conventional sliding mode controls (SMC) have always shown high robustness when applied to non‐linear systems. However, their applications are still limited due to their high‐frequency switching which could be a harmful phenomenon for different systems. As a solution, this paper proposes a high order SMC based drift algorithm. First, the algorithm is designed theoretically for a proton exchange membrane fuel cell system, where the stability study is proved with Lyapunov theory. Then, it was implemented in a commercial Heliocentris FC50 fuel cell system together with a power converter, a BK Precision 8500 programmable load, and a MicroLabBox dSpace DS1202. Comparative outcomes with the conventional SMC have shown the pros and cons of each scheme in terms of steady‐state oscillations, convergence speed, and robustness.

Photovoltaic (PV) panels are devices capable of transforming solar energy into electricity without emissions. They are still a trending technology in the market not only because of the renewable features but also due to the avoidance of movable parts, which makes them an option with low maintenance. If the output voltage is insufficient or needs to be regulated, a boost converter is commonly connected to a PV panel. In this article, a commercial PV with a boost converter is controlled through a dSPACE platform for a maximum power point tracking (MPPT) task. Due to previous related experience, a fuzzy logic technique is designed and tested in real-time. The results are compared with an incremental conductance (IncCond) algorithm because it is a feasible and reliable tool for MPPT purposes. The outcomes show enhancement (in comparison with IncCond) in the steady-state oscillation, response time and overshoot values, which are 73.2%, 81.5% and 52.9%, respectively.

This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

Renewable sources of energy play a key role in the process of decarbonizing modern electric power systems. However, some renewable sources of energy operate in an intermittent, non-dispatchable way, which may affect the balance of the electrical grid. In this scenario, wind turbine generators must participate in the system frequency control to avoid jeopardizing the transmission and distribution systems. For that reason, additional control strategies are needed to ensure the frequency response of variable-speed wind turbines. This review article analyzes diverse control strategies at different levels which are aimed at contributing to power balancing and system frequency control, including energy storage systems.

In applications where high precision in micro- and nanopositioning is required, piezoelectric actuators (PEA) are an optimal micromechatronic choice. However, the accuracy of these devices is affected by a natural phenomenon called “hysteresis” that even increases the instability of the system. This anomaly can be counteracted through a material re-shape or by the design of a control strategy. Through this research, a novel control design has been developed; the structure contemplates an artificial neural network (ANN) feedforward to contract the non-linearities and a robust close-loop compensator to reduce the unmodelled dynamics, uncertainties and perturbations. The proposed scheme was embedded in a dSpace control platform with a Thorlabs PEA; the parameters were tuned online through specific metrics. The outcomes were compared with a conventional proportional-integral-derivative (PID) controller in terms of control signal and tracking performance. The experimental gathered results showed that the advanced proposed strategy had a superior accuracy and chattering reduction.

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.

Oscillating water column (OWC) systems are water power generation plants that transform wave kinetic energy into electrical energy by a surrounded air column in a chamber that changes its pressure through the waves motion. The chamber pressure output spins a Wells turbine that is linked to a doubly fed induction generator (DFIG), flexible devices that adjust the turbine speed to increase the efficiency. However, there are different nonlinearities associated with these systems such as weather conditions, uncertainties, and turbine stalling phenomenon. In this research, a fuzzy logic controller (FLC) combined with an airflow reference generator (ARG) was designed and validated in a simulation environment to display the efficiency enhancement of an OWC system by the regulation of the turbine speed. Results show that the proposed framework not only increased the system output power, but the stalling is also avoided under different pressure profiles.

Photovoltaic (PV) panels are devices capable of converting solar energy to electrical without emissions generation, and can last for several years as there are no moving parts involved. The best performance can be achieved through maximum power point tracking (MPPT), which is challenging because it requires a sophisticated design, since the solar energy fluctuates throughout the day. The PV used in this research provided a low output voltage and, therefore, a boost-converter with a non-linear control law was implemented to reach a suitable end-used voltage. The main contribution of this research is a novel MPPT method based on a voltage reference estimator (VRE) combined with a fuzzy logic controller (FLC) in order to obtain the maximum power from the PV panel. This structure was implemented in a dSpace 1104 board for a commercial PV panel, PEIMAR SG340P. The scheme was compared with a conventional perturbation and observation (P&O) and with a sliding mode controller (SMC), where the outcomes demonstrated the superiority of the proposed advanced method.

Proton exchange membrane (PEM) fuel cell has recently attracted broad attention from many researchers due to its cleanliness, high efficiency and soundless operation. The obtention of high-performance output characteristics is required to overcome the market restrictions of the PEMFC technologies. Therefore, the main aim of this work is to maintain the system operating point at an adequate and efficient power stage with high-performance tracking. To this end, a model predictive control (MPC) based on a global minimum cost function for a two-step horizon was designed and implemented in a boost converter integrated with a fuel cell system. An experimental comparative study has been investigated between the MPC and a PI controller to reveal the merits of the proposed technique. Comparative results have indicated that a reduction of 15.65% and 86.9%, respectively, in the overshoot and response time could be achieved using the suggested control structure.

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA.

Polymer electrolyte membrane (PEM) fuel cells demonstrate potential as a comprehensive and general alternative to fossil fuel. They are also considered to be the energy source of the twenty-first century. However, fuel cell systems have non-linear output characteristics because of their input variations, which causes a significant loss in the overall system output. Thus, aiming to optimize their outputs, fuel cells are usually coupled with a controlled electronic actuator (DC-DC boost converter) that offers highly regulated output voltage. High-order sliding mode (HOSM) control has been effectively used for power electronic converters due to its high tracking accuracy, design simplicity, and robustness. Therefore, this paper proposes a novel maximum power point tracking (MPPT) method based on a combination of reference current estimator (RCE) and high-order prescribed convergence law (HO-PCL) for a PEM fuel cell power system. The proposed MPPT method is implemented practically on a hardware 360W FC-42/HLC evaluation kit. The obtained experimental results demonstrate the success of the proposed method in extracting the maximum power from the fuel cell with high tracking performance.

Fuel cells are considered as one of the most promising methods to produce electrical energy due to its high-efficiency level that reaches up to 50%, as well as high reliability with no polluting effects. However, scientists and researchers are interested more in proton exchange membrane fuel cells (PEMFCs). Thus, it has been considered as an ideal solution to many engineering applications. The main aim of this work is to keep the PEMFC operating at an adequate power point. To this end, conventional first-order sliding mode control (SMC) is used. However, the chattering phenomenon, which is caused by the SMC leads to a low control accuracy and heat loss in the energy circuits. In order to overcome these drawbacks, quasi-continuous high order sliding mode control (QC-HOSM) is proposed so as to improve the power quality and performance. The control stability is proven via the Lyapunov theory. The closed-loop system consists of a PEM fuel cell, a step-up converter, a DSPACE DS1104, SMC and QC-HOSM algorithms and a variable load resistance. In order to demonstrate the effectiveness of the proposed control scheme, experimental results are compared with the conventional SMC. The obtained results show that a chattering reduction of 84% could be achieved using the proposed method.

Proton exchange membrane fuel cell (PEMFC) topology is becoming one of the most reliable and promising alternative resource of energy for a wide range of applications. However, efficiency improvement and lifespan extension are needed to overcome the limited market of fuel cell technologies. In this paper, an efficient approach based on a super-twising algorithm (STA) is proposed for the PEMFC system. The control objective is to lengthen the fuel cell lifetime by improving its power quality, as well as to keep the system operating at an optimal and efficient power point. The algorithm adjusts the PEMFC operating point to the optimum power by tuning the duty cycle of the boost converter. The closed-loop system includes the Heliocentris hy-ExpertTM PEMFC, DC−DC boost converter, DSPACE DS1104, dedicated PC, and a programmable electronic load. The practical implementation of the proposed STA on a hardware setup is performed using a dSPACE real-time digital control platform. The data acquisition and the control system are conducted together with the dSPACE 1104 controller board. To demonstrate the performance of the proposed algorithm, experimental results are compared with 1-order sliding mode control (SMC) under different load resistance. The obtained results demonstrate the validity of the proposed control scheme by ensuring at least 72% of the maximum power produced by PEMFC. In addition, it is proven that the STA ensures all the fundamental properties of the 1-order SMC, as well as providing chattering reduction of 91%, which will ameliorate as a consequence the fuel cell lifetime.

Taking into account the limited capability of proton exchange membrane fuel cells (PEMFCs) to produce energy, it is mandatory to provide solutions, in which an efficient power produced by PEMFCs can be attained. The maximum power point tracker (MPPT) plays a considerable role in the performance improvement of the PEMFCs. Conventional MPPT algorithms showed good performances due to their simplicity and easy implementation. However, oscillations around the maximum power point and inefficiency in the case of rapid change in operating conditions are their main drawbacks. To this end, a new MPPT scheme based on a current reference estimator is presented. The main goal of this work is to keep the PEMFCs functioning at an efficient power point. This goal is achieved using the backstepping technique, which drives the DC⁻DC boost converter inserted between the PEMFC and the load. The stability of the proposed algorithm is demonstrated by means of Lyapunov analysis. To verify the ability of the proposed method, an extensive simulation test is executed in a Matlab⁻Simulink TM environment. Compared with the well-known proportional⁻integral (PI) controller, results indicate that the proposed backstepping technique offers rapid and adequate converging to the operating power point.