In this paper, graded Ge mole fraction aspect is proposed as a new way to achieve the dual benefit of improved Si/SiGe-based solar cell photoconversion efficiency and suppressed degradation related-dislocation effects. Our purpose resides mainly on decreasing the defect density at the Si/SiGe interface through shifting the Ge concentration gradually with the SiGe absorber layer thickness. Further, a careful mechanism analysis based on investigating numerically the impact of the proposed graded Ge content paradigm on reducing the degradation related-dislocation effect is performed. The advantage of using a SiGe layer with graded Ge concentration instead of a thin-film SiGe alloys is presented. Moreover, the impact of the proposed SiGe layer thickness on the solar cell conversion efficiency is carried out. It is found that the proposed feature brings the opportunity of reducing the lattice mismatch at the Si/SiGe interface, which can in turn improve the Si/SiGe-based solar cell conversion efficiency. In addition, increasing the Ge content progressively suggests the band-gap modulation aspect that enables improving the solar cell optical absorption and the total resistance. Therefore, the proposed design pinpoints a new path toward avoiding recombination losses through suppressing the degradation related-dislocation effects, which makes it potential alternative for providing high-efficiency Si-based solar cells.

In this paper, a new nanoscale double-gate junctionless tunneling field-effect transistor (DG-JL TFET) based on a Si1-xGex/Si/Ge heterojunction (HJ) structure is proposed to achieve an improved electrical performance. The effect of introducing the Si1-xGex material at the source side on improving the subthreshold behavior of the DG-JL TFET and on suppressing ambipolar conduction is investigated. Moreover, the impact of the Ge mole fraction in the proposed Si1-xGex source region on the electrical figures of merit (FoMs) of the transistor, including the swing factor and the ION/IOFF ratio is analyzed. It is found that the optimized design with 60 atom % of Ge offers improved switching behavior and enhanced derived current capability at the nanoscale level, with a swing factor of 42 mV/dec and an ION/IOFF ratio of 115 dB. Further, the scaling capability of the proposed Si1-xGex/Si/Ge DG-HJ-JL TFET structure is investigated and compared to that of a conventional Ge-DG-JL TFET design, where the optimized design exhibits an improved switching behavior at the nanoscale level. These results make the optimized device suitable for designing digital circuit for high-performance nanoelectronic applications.

The composition of the diet of the barbel (Luciobarbus callensis) was studied in the K’sob reservoir near the town of M’Sila (Algeria). The capture of the barbels was carried out between September 2010 and August 2011.The diet was studied from 379 specimens collected by trammel net. During the study period, it was noted that the food spectrum essentially consisted of Chironomids, Ephemeroptera and Copepods, which are abundant in the environment. The barbel of the K’sob reservoir has a seasonal food pattern influenced by climatic and ecological conditions and by its reproductive cycle. The diet of L. callensis in the study area is omnivorous, with a zoo-benthophage tendency.

In this paper, we propose a new and practical variant of ElGamal encryption which is secure against every passive and active adversary. Under the hardiness of the decisional Diffie-Hellman assumption, we can prove that the proposed scheme is secure against an adaptive chosen ciphertext attacks in the standard model. Such security verifies not only the confidentiality but also verifies the integrity and the authentication of communications. We display that the modified scheme furthermore achieves anonymity as well as strong robustness.

Diabetic patients are prone to daily and severe health-related risks, namely hyper and hypoglycaemia. Hypoglycaemia phenomenon happens when the glucose level in patient’s blood is lower than a well-determined sill. It may induce serious impacts, such as functional brain failure or even the death. Hypoglycaemia is especially dangerous when it occurs during the night while the patient is asleep because it becomes difficult to be detected by the patient itself or other persons around him. While all existing sensor-based solutions are detection-only driven, the proposed solution goes beyond and attempts to treat autonomously, and at low cost, the nocturnal hypoglycaemia. The presented system detects the nocturnal hypoglycaemia phenomenon based on accelerated heart-rate symptom and a progressive detection algorithm. The system treats then the detected nocturnal hypoglycaemia throughout safe and automatic injection of glucagon.

Increasing the specter efficiency has been an object for many studies. In this paper, we investigate the higher modulation 256 QAM using Artificial Neural Networks (ANN) as an equalization model. Multilayer perceptron (MLP) and Radial Basis Function (RBF) are considered as non-linear equalizer based on back-propagation and Euclidian norm respectively. They are designed in a simplified architecture and employing some performing strategies for a better learning and an increased processing speed. ANNs are presented and applied with Orthogonal Frequency Division Multiplexing (OFDM) over Rayleigh fading channel in order to optimize the modulation scheme's processing and performances despite its sensitivity to noise. The models will be compared to the theoretical BER simulation in terms of BER, and also in terms of MSE to show performance and efficiency; by that, this work will show the supremacy of MLP in decision making with 256 QAM.

The Graphene–based Double Gate Field-Effect Transistors (G-DG FETs) have received great attention in recent years due to their high electrical performance provided for analog and Radio-frequency (RF) nanoelectronic applications. To calculate accurately the drain current and the Figures-of-Merit (FoMs) of the nanoscale G-DG FETs requires the solution of Schrödinger/Poisson equations, assuming the quantum effects are to be fully accounted. However, for nanoelectronic circuit simulation, the 2D numerical solution through the fully self-consistent coupled Schrödinger/Poisson equations is an overkill approach in terms of both complexity and computational time cost. Hence, new approach and simulation tools which can be applied to design and simulate Graphene-based nanoelectronic circuits are required to overcome the limitations imposed by the accuracy and computational time cost. In this paper, we investigate the efficiency of a new approach based on the ANN-based computation (Artificial neural network) to analyze and simulate Graphene-based nanoelectronic circuits. In this context, this work presents the applicability of ANN for the simulation of the voltage amplifier by investigating the impact of the G-DG FET design parameters on the analog and RF performances. The ANN-based model can be easily implemented into commercial circuit simulators like: SPICE, Cadence and Silvaco.

The average unavailability and the average unconditional failure intensity of safety-instrumented systems represent the main performance indicators of safety integrity. This paper employs an approach based on the exploitation of the availability expression to obtain both performance measures in a simultaneous and straightforward way for any KooN configuration. The implementation of such an approach is generalized to take into account the contribution of common cause failures using any parametric model. The validation of the obtained results is verified through their application using several architectures and using Beta Factor and Binomial Failure Rate models to handle such type of dependent events. Therefore, the contribution of this paper lies in proposing one single formula that can be used to estimate the two main safety integrity’s performance indicators for any KooN architecture using any kind of common cause failures parametric model.