Using first-principles calculations, we study the structural, electronic and optical properties of the monolayer, bilayer and trilayer germanium monosulfide GeS. The results reveal an indirect semiconducting band gap for the monolayer and trilayer GeS, whereas the gap is direct for the bilayer GeS. Both the generalized gradient approximation and the screened hybrid functionals assess a decrease in band energy as the number of layers is improved. Furthermore, due to the high buckling of lattice structures, the optical spectra show significant degree of anisotropy. The number of layers engineers key optical parameters including the refractive index, the reflectivity absorption and provides the layered GeS with excellent absorption in the low energy region, namely the visible and UV range of the electromagnetic spectrum. Accordingly, 2D hexagonal GeS few-layers can be used as a highly promising material in the optoelectronic, ultraviolet optical nanodevices and photovoltaics.

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long–short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

In this paper, a new approach being different from the concept of DTC and IFOC for a robust torque control design for induction motor is addressed. The design is based on the framework of singularly perturbed system theory and linear varying parameter systems. In these systems, the rotor flux is considered to be a time-varying parameter in order to guarantee a robust torque control with LPV flux observer with respect to the speed and resistance variations. In fact, this observer is designed to estimate the rotor flux as well as an MRAS observer is introduced to estimate the mechanical speed and rotor resistance. The main feature of this proposed structure is the enhancement of robustness with flux, speed and rotor resistance variation. This improvement leads to a considerable decrease of the torque ripples and ensures the stability for the entire operating range. The obtained simulations and experimental results are used to validate the effectiveness of the proposed control strategy.

Tunnel FET is one of the promising devices advocated as a replacement of conventional MOSFET to be used for low power applications. Temperature is an important factor affecting the performance of circuits or system, so temperature associated reliability issues of double gate Tunnel FET and its impact on essential circuit design components have been addressed here. The temperature reliability investigation is based on double gate Tunnel FET, containing Si_1-xGe _x /Si, source/channel and HfO₂ high-k gate dielectric material. During investigation, it has been found that at high temperature application range ~ 300 K - to - 600 K,the Tunnel FET device design parameters exhibit weak temperature dependency with switching current (I_ON), while the off-state current (I_OFF) is slightly varying ~10⁻¹⁷A/μm-to-10⁻¹⁰A/μm. In addition, the impact of temperature on various device design element such as V_TH(i.e.,switching voltage),on-current (I_ON), off-current (I_OFF), switching ratio (I_ON/I_OFF) and average subthreshold slope (i.e., SS_avg), ambipolar current (I_AMB) have been done in this research work.The essential circuit design components for digital and analog/RF applications, such as current amplification factor(g_m) and its derivative (g_m’),the C-V components of device design, Cgg, Cgd and C_gs, cut - off frequency (ƒ_T) and gain band width (GBW) product have deeply investigated. In conclusion, the obtained results show that the designed double gate Tunnel FET device configuration and its circuit design components are suitable for ultra-low power circuit,system applications and reliable for hazardous temperature environment.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

We analyzed the influence of land use and water physical-chemical characteristics on the trait composition of benthic macroinvertebrates in arid-land streams of North-East Algeria. Macroinvertebrates were sampled in the spring season of 2015, 2017 and 2018 at 36 sampling sites distributed along 5 streams of the Belezma biosphere reserve. Samples were taken from the various substratum types using a Surber net. Most of the variability of the trait-environment relationship was explained by increasing temperature and conductivity along the downstream gradient. Whilst agriculture at higher elevations did not have a great influence on the functional trait composition of macroinvertebrate communities, agriculture and urbanization at lower elevations generated significant deviations from predictable functional structures. Owing to the natural downstream decrease in community diversity in streams of the study region, entire taxa and/or functional groups were more likely to be wiped out in response to anthropogenic perturbations at lower elevations. Despite human activities, climate-related variables in arid lands play a major role on hydrological regimes that effect instream habitats, water chemistry, and macroinvertebrate communities. Given the environmental constraints in arid-land streams of North Africa, even slight increases in anthropogenic pressure can have negative effects on the taxonomic and functional composition of macroinvertebrate communities.

The issue of robustness of stability has been prominent in the literature on control theory over the last two decades. An important state-space approach to robustness analysis is the stability radii theory. The robust stability problems of infinite dimensional systems subjected to stochastic bounded structured perturbations have been studied using the stability radius approach. In the applications it is important to study these problems in the case where the perturbations operator structure are unbounded, because it covers the case of partial differential equations with boundary and ..

This paper presents the first description of the mcr-5.1 gene in a colistin-resistant Cupriavidus gilardii isolate from well water that supplies a maternity hospital in Algeria. The whole-genome sequence of this strain showed the presence of putative β-lactamase, aac(3)-IVa, and multidrug efflux pump-encoding genes, which could explain the observed multidrug resistance phenotype. Our findings are of great interest, as we highlight a potential contamination route for the spread of mcr genes. 

IMPORTANCE Colistin resistance mediated by mcr genes in Gram-negative bacteria has gained significant attention worldwide. This is due to the ability of these genes to be horizontally transferred between different bacterial genera and species. Aquatic environments have been suggested to play an important role in the emergence and spread of this resistance mechanism. Here, we describe the first report of an mcr-5-positive Cupriavidus gilardii aquatic isolate through its isolation from well water in Algeria. The significance of our study is in shedding the light on an important environmental reservoir of mcr genes.

The aim of this work is to study the mechanical behavior of the sediments extracted from the Koudiet Meddaouar, Timgad dam (Algeria), for a possible valorization in the field for building works in order to minimize this phenomenon which is currently a concern for the operators and the persons in charge of the mobilization of the water resources. This siltation therefore severely limits its storage capacity and consequently it’s operating life. The extraction of the sediments accumulated in the dam's reservoir is therefore imperative, on the pain of seeing it perish in the medium term. These sediments are, however, of great geotechnical and mechanical value. The results of the tests conducted in the laboratory have enabled us to identify the different sediments from a physical and geotechnical point of view In front of the difficulties noted in the control of the silting up of the dams in Algeria, a very important quantity of silt being deposited annually in the dams. In order to achieve our objective, different mixtures of silt with or without lime treatment, cement glass fibers and powdered fibers were studied for the possible manufacture of Compressed Earth Bricks (CEB). The results obtained show that some of the mixtures present very interesting results in the different tests (compression and bending), verifying the conditions of the standards in force and thus allowing their use in the field of the manufacture of building materials.

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.

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.

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.

In this work, a modified perturbs and observes (P&O) technique is used in the hybrid power generation system with power management. There are several algorithms for extracting the maximum power point (MPP) provided from the PV generator; P & O algorithm has a good performance, ease of implementation by analog and digital electronics. However, this algorithm has disadvantages because it oscillates at the point of maximum power and has a relatively long convergence time; therefore, a modification is made to the P & O algorithm in order to reduce setup time and oscillations in the MPP. The proposed system allows optimal use of the photovoltaic (PV) system and the DFIG wind proves its efficiency under variable load conditions. The model is implemented in the MATLAB / Simulink platform.