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The loggerhead sea turtle (Caretta caretta), has the widest distribution among sea turtle species in the Mediterranean, requiring regional and international collaborations in addition to local efforts to better inform conservation actions. Molecular techniques are powerful tools for the assessment of population dynamics at large scales, especially in determining the connectivity among different nesting and foraging sites, and genetic diversity. In this study, a large sample was collected synchronously in the nesting areas located in the north, south and eastern Mediterranean. Recently confirmed nesting sites from Albania and other nesting sites represented by lower sample sizes were also included in order to better assess the genetic composition of the region's rookeries. Samples from 698 individuals were collected and the longer (815 bp) mtDNA D-loop fragment of these samples was sequenced. We recorded 15 haplotypes, three of which were novel. In addition, our results show that some haplotypes considered of Atlantic origin, have a wider dispersal in the Mediterranean than previously thought, albeit with low levels of representation. Our results contribute to determining the likely origin of haplotypes that were previously only recorded from foraging sites. They highlight the utility of broad-scale sampling, with increased sample sizes and the longer mtDNA sequence to determine genetic diversity and connectivity. This study also demonstrates the importance of continued monitoring for the contribution of Atlantic-origin haplotypes to the Mediterranean population and the effects of climate change on the resident Mediterranean population, which is expected to expand its geographical range for reproduction. This work is important for, mixed stock analyses (MSA) that seek to determine the natal regions of stranded or accidentally caught sea turtles and those purposefully obtained from foraging sites. In doing so biogeographic linkages between areas of the Mediterranean can be elucidated for conservation purposes.

Sexual selection on fitness-determining traits should theoretically erode genetic variance and lead to low heritability. However, many sexually selected traits maintain significant phenotypic and additive genetic variance, with explanations for this “lek paradox” including genic capture due to condition-dependence, and breaks on directional selection due to environmental sources of variance including maternal effects. Here we investigate genetic and environmental sources of variance in the intrasexually selected green badge of the sand lizard (Lacerta agilis). The badge functions as a cue to male fighting ability in this species, and male–male interactions determine mate acquisition. Using animal models on a pedigree including three generations of males measured over an extensive 9-year field study, we partition phenotypic variance in both badge size and body condition into additive genetic, maternal, and permanent environmental effects experienced by an individual over its lifespan. Heritability of badge size was 0.33 with a significant estimate of underlying additive genetic variance. Body condition was strongly environmentally determined in this species and did not show either significant additive genetic variance or heritability. Neither badge size nor body condition was responsive to maternal effects. We propose that the lack of additive genetic variance and heritability of body condition makes it unlikely that genic capture mechanisms maintain additive genetic variance for badge size. That said, genic capture was originally proposed for male traits under female choice, not agonistic selection. If developmental pathways generating variance in body condition, and/or the covarying secondary sex trait, differ between inter- and intrasexual selection, or the rate at which their additive genetic variance or covariance is depleted, future work may show whether genic capture is largely restricted to intersexual selection processes.

Prolonged drought periods cause a cascade of biochemical alterations in plants and lead to reduction in plant growth and crop productivity. Seaweed extracts are a category of plant biostimulants that are effective in alleviating drought stress on plants. However, the effect of seaweed extracts on attenuating the negative impact of drought on Faba bean (Vicia faba L.) under arid and semi-arid conditions has received little attention. This study was conducted to evaluate the performances of extracts made from Fucus spiralis (FSE), Ulva lactuca (ULE), Laminaria ochroleuca (LOE), and Ascophyllum nodosum (ANE) in mitigating drought stress in Faba bean. The biochemical profile of the extracts was characterized, and key physiological and biochemical parameters of Faba bean plants were assessed during both drought and recovery phases. All investigated extracts positively affected plant biomass under drought stress conditions. Plants that received LOE had a higher relative water content and lower malondialdehyde concentration in comparison with stressed control plants. The positive effect of ANE and FSE was mainly attributed to proline accumulation in plant tissues under stress. FSE and ULE application resulted in a higher concentration of soluble sugars in treated plants in comparison to the control plants. It was concluded that seaweed extracts originating from different sources and extracted using different protocols act differentially in altering plant-related stress traits. Overall, seaweed extracts are potentially an effective solution to manage the negative impact of water scarcity on Faba bean.

Background Despite the generally accepted World Health Organization guidelines on semen analysis, an individual’s results can display significant variation when performed across time or in different laboratories. Semen parameters are in fact highly variable measures that can differ significantly between various analyses. Numerous researchers have discovered a wide range of semen parameters within each individual male, but only a few studies included the analysis of semen parameters variability in patients with infertility. The aim of this study was to evaluate the inter- and intra-individual variability of semen parameters in men of reproductive age with normozoospermia and those with oligozoospermia. Methods Five hundred and thirteen who provided ≥ 2 semen samples (798 samples in total) using an at-home mail-in kit over a period of about 2 years were enrolled in the study. Semen samples collection using Give Legacy at-home mail-in semen collection kit; semen analysis at a CLIA-certified laboratory. Results The degree of intra-subject variation across all semen parameters was lower in men with normozoospermia compared to men with oligozoospermia. Men with normozoospermia furthermore demonstrated a level of intra-subject variation that was lower than inter-subject variation across all measured parameters. No association was observed between intra-subject coefficients of variation in any of the semen parameters, including sperm concentration, sperm count, motile sperm count, total motility, progressive motility, the percentage of sperm with normal morphology, and the age, duration of abstinence, and BMI of the men. Conclusion The results of this observational study confirm the significant variability in semen parameters in men with normozoospermia and oligozoospermia, as measured from at-home semen collection kit samples. This further underscore the importance of securing multiple samples for analysis to provide a robust assessment of male fertility.

Because population growth, agricultural and industrial development during last decades, the study area was known a seriously water resources’ degradation. Therefore, cartography of the vulnerability seems to be an efficient tool for water resources management. To evaluate the vulnerability of the aquifer to pollution, two methods were used: DRASTIC, and EPIK based on the geographical information system (GIS) tools. These methods use different parameters which explain the different results in the vulnerability degrees in the great transboundry karstic basin in the Tebessa–Kasserine region between Algeria and Tunisia. Risk maps are established by overlaying the vulnerability and hazard maps, and represent the intensity of a potential contamination. The vulnerability maps show the existence of two classes: moderate and high depending on the intrinsic properties. The high vulnerability zone covers 80% of the study area and is found in Morsott, Bakaria and Foussana. On the other hand, moderate vulnerability zone covers about 20% of the total study zones and correspond to areas with deep water level. These maps could serve as a scientific basis for sustainable land use planning and groundwater management in the study region. These results can be validated, either occasionally by tracing tests, or globally for the basin by the characteristics of karst springs. Finally, the integration of the sensitivity of this carbonate system allows us to deduce the total risk of a basin.

Self-assembled InAs/GaAs quantum dots (QD) are extensively studied for their potential application in optoelectronic devices. In this paper, we report the influence of QD growth rate on the optical properties of InAs/GaAs QDs under their exposure to a Nitrogen plasma. The samples under study are grown by molecular beam epitaxy and characterized by photoluminescence spectroscopy (PL). For capped unnitrided QDs, the increase in InAs growth rate is accompanied by an increase in PL emission energy and PL full width at half maximum (FWHM). Under nitridation, it was found that the incorporation of Nitrogen (N) strongly affected the QD emission and showed a significant redshift of the photoluminescence peak emission. In addition, PL FWHM decreases as the growth rate of InAs increases. The temperature dependence of the PL peak energy of the unnitrided and nitrided QDs exhibits a V shape associated to the carrier localization phenomenon induced by the spatially fluctuating potential. This behavior is confirmed by using the localized state ensemble (LSE) theoretical model. In addition, the results suggest that nitridation aims to reduce the size distribution of QDs and leads to their homogenization compared to the case of inhomogeneous distribution in the absence of Nitrogen. These results are a particularly promising method for realizing QD lasers, which require the use of low QD size and high density with minimal size dispersion.

In this paper, a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller is proposed to extract the maximum power from available solar energy for a three-phase shunt active power filter (APF) grid connected photovoltaic (PV) system supplying an arc welding machine. Firstly, in order to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current, and the output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-ACO MPPT controller is based on an ant colony optimization (ACO) algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Secondly, aiming to meet the various grid requirements such as power quality improvement, distortion free signals etc., a three-phase shunt APF is utilized, and a direct power control algorithm is designed for distributing the solar energy between the DC-link capacitor, arc welding machine and the AC grid. Finally, the performance of proposed control system is confirmed by simulation tests on a 12.2 kW PV system. Both simulation and experimental results have demonstrated that the deigned ANN-ACO MPPT controller can provide a better MPP tracking with a faster speed and a high robustness with a minimal steady-state oscillation than those obtained with the conventional INC method. Also, with the use of a three-phase shunt APF, all the power fluctuations from the arc welding machine disturbances are damped out and the output active and reactive power become controllable.

In this study, an improved Adaptive Fast Terminal Synergetic Controller (AFTSC) using Dual Radial Basis Function (RBF) Neural Networks (NNs) for output voltage control of an uncertain DC/DC step-down converters is proposed. Using the considered AFTSC, the with new manifold proposed here enables the DC/DC step-down converter’s state variables to track the preferred reference voltage in presence of disturbances from any initial condition with proper precision and limited time. To rendering the design more robust, a sort of dual RBFNNs are utilized to approximate in real-time unknown converter non-linear dynamics and reduce the modeling error without calling upon usual model linearization and simplifications. The stability of the closed-loop system is assured by means of the Lyapunov method. Considering the PWM DC–DC step-down converter as an example, the considered adaptive RBFNN-FTSC law is studied in detail and implemented on a dSPACE ds1103 card. All the simulation and experimental results illustrate the efficiency and feasibility of the suggested controller.

The present paper presents a comprehensive study based on numerical and experimental investigations testing the use of NC lathe machine in SPIF process of a dome part made of AA1060-H14 aluminum alloy. The main idea is to check the viability of NC turning machine in SPIF process of axisymmetric parts usually manufactured using 3-axis milling machines. For this purpose, aluminum thin sheet is firstly characterized and the right material behavior is identified. A VUMAT subroutine is used to implement this material behavior in ABAQUS/explicit FE used to simulate the SPIF-Turning operation. A comparison between experimental and numerical results showed the efficiency of this FE model to simulate a 2-axis SPIF strategy. Results, covering forming effort, thinning, the strain state distribution, fracture occurrence, and surface finish proved that NC turning machines could manufacture rapidly and with more accuracy, revolved components usually formed by a 3-axis NC milling machine.

Our work on this paper aims on developing the strategies of fractional order controller (FOC) design based on use time-domain specifications (Percentage of overshoot (PO) and Settling time (ts)) to control non integer-order models with plus time delay (NIOPTD or fractional order models). Considering a fractional Smith predictor structure, a efficient and simple analytical strategy to tuning a FOC for fractional models is suggested. The design strategy is based on desired time-domain specifications (PO and ts). To achieve these requirements, many tuning analytical formulas are derived using on Bode’s ideal closed-loop transfer function. The resulting new controller can be decomposed into fractional order PID controller cascaded with a fractional order filter. The parameters of the FOPID controller are directly obtained by using the parameters of the fractional order models and the fractional-order filter parameters are easily obtained Using formulas of time-domain specifications (PO and ts). After a successful implementation of the proposed fractional controller on single-input-single-output (SISO) process, it is implemented on the complex system (multi-input multi-output (MIMO) system). Where the simulation results show that the suggested strategy of fractional Smith predictor improves the closed-loop control system compared to other controllers.

Credit scoring becomes an important task to evaluate an applicant by a banker. Many tools are available for making initial lending decisions. This paper presents a Hidden Markov Model (HMM ) for credit scoring, and uses Baum-Welch method; an iterative procedure approach; for building a set of credit scoring models. We introduce HMM/Baum-Welch model: a tool developed to explore a good accurate model for classification problems. There are two phases in this model: learned an initial model from training data using HMM, and re-estimating HMM parameters by an iterative process using Baum-Welch algorithm. The proposed model is successfully applied to a real credit problem, and the application procedure is illustrated through two data sets: German and Australian. The criteria used to evaluate the performance of different resulting models are the accuracy and AUC (area under the ROC curve). The experiment of this model, shows that, HMM with Baum-Welch approach can improve the pattern classification performance in credit scoring.

The fast and sensitive detection of copper ions would be essential for water monitoring. Herein, we report a novel development of an impedimetric sensor based on carbon dots/chitosan nanocomposite. Carbon dots (CDs) were synthesized by simple heating of an acidic aqueous solution of glucose. The prepared CDs were characterized by TEM, FTIR, XRD, UV-visible, and PL. These measurements revealed that the CDs possess a mean size of 3.2 nm, a graphitic structure with carboxyl and hydroxyl groups on the surface, and shows particular optical properties. A glassy carbon electrode (GCE) was modified with a carbon dot/chitosan nanocomposite and was characterized by scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The CDs-CHITO/GCE electrode exhibits a large surface area, good conductivity film, and charge transfer at film/electrolyte interface. The proposed impedimetric sensor exhibits a linear response to Cu(II) ranging from 10−9 M to 10−5 M with a detection limit about 5 × 10−10 M. In addition, the sensor shows a good selectivity toward Cu(II) ions, which is less than 5%. Therefore, the as-developed impedimetric sensor exhibits good reproducibility, stability, selectivity, and a low limit of detection, which augur well for its application in water safety control processes.

In the present study, five NPs (containing ZnO, Au-ZnO, Cu-ZnO, TiO2, and Au-TiO2) were characterized using dynamic light scattering and transmission electron microscopy, in order to observe their behavior under environmental change. The applicability of NPs for degradation of three polycyclic aromatic hydrocarbons (PAHs), including benzo(a)pyrene, fluoranthene, and benzanthracene, using UV irradiation showed the high photocatalytic efficiency of doped NPs for the removal of the study pollutants. To predict the environmental impact and interaction between NPs and PAHs on marine organisms, Mytilus galloprovincialis mussels were exposed to concentrations of each chemical (50 and 100 μg/L) for 14 days. The mussel’s response was determined using the oxidative stress biomarker approach. Measured biomarkers in the mussel’s digestive gland showed possible oxidative mechanisms in a concentration-dependent manner occurring after exposure to PAHs and NPs separately. Overall, this finding provides an interesting combination to remove PAHs in water, and the incorporation of chemical element into the crystallographic structure of NPs and the combination of two different NPs to form a binary hybrid NPs are promising materials.

The widespread occurrence of drugs in the aquatic environment has raised concerns over the last decades about the mechanisms to be used to remove these emerging contaminants because of their harmful effects on the water quality and human health. This current research was aimed to explore the preparation of an activated carbon from kernel oil treatment residues of Argania Spinosa for the removal of amoxicillin (AMX) from water. The conditions for synthesizing the activated carbon (AC-RS) were used: an impregnation weight ratio of char to KOH 1:1 at activation temperature of 800 °C for 1 h of activation time, which gave a BET surface area and total pore volume of 1618 m2 g−1 and 0.763 cm3 g−1, respectively. The effect of AMX/AC-RS interactions as well as the operating conditions (time, solution pH, initial concentration, and temperature) on the adsorption capacity was also studied. The adsorption of AMX onto AC-RS, with maximum adsorption capacity of 303.03 mg g−1, is much higher compared to several adsorbents reported in the literature. The high amount of AMX absorbed by AC-RS at pH 6 mainly results from the adsorption sites involved in the electrostatic attraction force, π-π hydrophobic interaction, and hydrogen bonding. The regeneration study ascertained the fact that AC-RS was reusable even after four consecutive cycles and the efficiency remains higher than 70%. Therefore, the optimized batch parameters were employed for the AMX removal in real wastewater, a percentage of 77.2% was obtained.

Epilepsy disease is one of the most prevalent neurological disorders caused by malfunction of large symptoms number of neurons. That’s lead us to propose an automated approach to classify Electroencephalography (EEG) signals of the aforementioned pathology. To realize an efficient seizures detection the output of our classification is divided into three classes; normal, pre-ictal and ictal class. In fact, we propose to use the Short-Time Fourier Transform (STFT) as a non-stationary signal processing technique to extract useful information from the EEG signals. After that, we transform the STFT into a spectrogram image which will be used as an input in the classification process. In this context, we developed a deep convolutional neural network (CNN) model capable to efficiently detect and classify epilepsy seizures based on the EEG spectrogram images. It should be noted that the database used in this work is the publicly available EEG data set of Bonn University. In order to evaluate the performance of the proposed classification method several metrics are calculated, such as; sensitivity, specificity, accuracy and precision. The experimental results prove that the proposed method is a powerful tool in classifying EEG signals with a high average accuracy rate of 98.22%.