A Study on the FTIR Spectroscopy of Polylactide Doped with Nano-Aluminium Oxide and Nano-Cupric Oxide

Elucidation of the structure of naturally existing or synthesized substances is an important criterion in the study of materials to predict the application of the substance. In this study, polylactide was doped with nano-aluminium oxide and nano-cupric oxide with 1 and 3 mg of concentration variants. The interaction between the polymer matrix and the nanoparticles has been studied using Fourier transform infrared. Successful doping of the polymer has been observed. Attention has been drawn to check the intermolecular bonding in films having varying thicknesses, films prepared at higher sonication temperatures, and chemical homogeneity of the doped polymer films.     

A low-power RF integrated circuit for implantable sensors

A low-power low-voltage analog signal processing circuit has been designed, fabricated, and tested. The circuit is capable of processing an analog sensor current and producing an ASK modulated digital signal with modulating signal frequency proportional to the sensor current level. An on-chip regulator has been included to stabilize the supply voltage received from an external RF power source. The circuit can operate with a power supply as low as 1 V and consumes only about 20 μW of power, which is therefore very suitable for implantable biomedical applications. The whole chip was laid out and fabricated in a 0.35 μm bulk CMOS technology. Experimental results show good agreement with the simulation results.     

Thin film polycrystalline Si p-n junction solar cells with preferential doping

A model has been introduced for a polycrystalline thin film silicon p-n junction solar cell with preferential doping along the grain boundaries. Detailed numerical calculations have been done for the effect of doping depths along the grain boundaries, for different grain sizes, on the performance of the cell under AM1 conditions. The results indicate that preferential doping of grain boundaries leads to significant improvement of the cell performance.     

Ballistic performance study of fabric armor based on numerical simulations with multiscale material model

Based on a multiscale model for fabric materials, dynamic simulations of the fabric ballistic performance were implemented. Through parameter research, it was found that the ballistic performance and mechanical behavior of the fabric materials are determined by a combination of factors and conditions rather than by the material properties alone. The material mechanical properties reflect the inherent strength of the fabric; the fabric weaving structure, boundary conditions, material orientation, and projectile shape also play important roles and have a significant influence on the ballistic performance of the fabric. The multiscale material model incorporates not only the membrane‐like properties of the fabric but also the underlying weaving structure, yarn interaction, and yarn composition. The simulations results show good agreement with the experimental data. Various physical phenomena can be observed in the simulations, such as yarn decrimping, material anisotropy, and two types of damage modes. Copyright © 2011 John Wiley & Sons, Ltd.     

Towards Securing Machine Learning Models Against Membership Inference Attacks

From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities, and we highlight current progress in the research works proposing defence techniques against ML security and privacy attacks. The relevant background for the different attacks occurring in both the training and testing/inferring phases is introduced before presenting a detailed overview of Membership Inference Attacks (MIA) and the related countermeasures. In this paper, we introduce a countermeasure against membership inference attacks (MIA) on Conventional Neural Networks (CNN) based on dropout and L2 regularization. Through experimental analysis, we demonstrate that this defence technique can mitigate the risks of MIA attacks while ensuring an acceptable accuracy of the model. Indeed, using CNN model training on two datasets CIFAR-10 and CIFAR-100, we empirically verify the ability of our defence strategy to decrease the impact of MIA on our model and we compare results of five different classifiers. Moreover, we present a solution to achieve a trade-off between the performance of the model and the mitigation of MIA attack.     

Deep eutectic solvents: designer fluids for chemical processes

The increasing demand for multi‐task green solvents has spurred the development of next‐generation liquid media such as deep eutectic solvents (DESs), which have recently attracted increased attention. DESs are mixtures of salts and complexing agents, having freezing points lower than those of starting individual components. Similarly to ionic liquids, DESs exhibit distinctive properties such as chemical and thermal stability, biodegradability, non‐flammability, and cost effectiveness. These features account for their wide range of applications, e.g. as extractants, reactants, catalysts, reaction media, additives, and lubricants. This review summarizes the recent research efforts directed at exploring the potential applications of DESs in various chemical processes. With the rapid publication of reports on this new generation of solvents, other roles also are expected to be seen sooner or later. © 2017 Society of Chemical Industry     

Experimental study of closed‐loop thermosyphon with a different evaporator geometry

In this study, the effect of evaporator geometry on the loop thermosyphon's heat transfer coefficient is experimentally verified by using water as a working fluid with three filling ratios (50%, 70%, 90%), constant heat input (185 W), and condenser cooling water flow rate remaining constant at 2 Lpm. Three evaporator pipes are used (I: straight; II: helical coil evaporator with a diameter of 100‐mm coil and two turns; III: helical coil evaporator with a diameter of 50‐mm coil and four turns). From the experimental results, it can be observed that the performance of evaporator III is higher than the two other forms. A greater heat transfer coefficient value is found in case of type III evaporator and is equivalent to 2456 W/m²·°C. The maximum thermal resistance reduction occurs in the type III evaporator (37.32%), and the highest effective thermal conductivity for the same type is 6.123e + 05 W/m·°C. The experimental results demonstrate good agreement with the empirical equations.     

Analysis of the enhancement of the performance of a polysilicon solar cell due to simultaneous passivation of grain boundaries and back contact

The enhancements in the short circuit current, open circuit voltage and efficiency of an n⁺/p polysilicon solar cell due to simultaneous passivation of the grain boundaries and the back contact are studied analytically. The results show that these enhancements are greater than the sum of the enhancements obtained by passivating the grain boundaries only and by passivating the back contact only. That is the enhancements due to simultaneous passivation cannot be deduced from studies on passivation of each boundary at a time.     

Using remotely sensed and ancillary data to predict spatial variability of rainfed crop yield

Rainfed agriculture is dominant in Sudan. The current methods for crop yield estimation are based on taking random cutting samples during harvesting time. This is ineffective in terms of cost of information and time. The general objective of this study is to highlight the potential role of remote-sensing techniques in upgrading methods of monitoring rainfed agricultural performance. The specific objective is to develop a relationship between satellite-derived crop data and yield of rainfed sorghum. The normalized difference vegetation index (NDVI), rainfall, air temperature (AT) and soil moisture (SM) are used as independent variables and yield as a dependent variable. To determine the uncertainty associated with the independent variables, a sensitivity analysis (SA) is conducted. Multiple models are developed using different combinations of data sets. The temporal images taken during sorghum’s mid-season growth stage give a better prediction than those taken during its development growth stage. Among predictor variables, SM is associated with the highest uncertainty.     

Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore, a PSO is adapted for constrained optimization, yielding two constrained variants, denoted SF-PSO and VCH-PSO. Both SF-PSO and VCH-PSO are evaluated with respect to five engineering problems: the Himmelblau’s nonlinear optimization, the welded beam design, the spring design, the pressure vessel design, and the three-bar truss design. The simulation results show that both algorithms are consistent in terms of their solutions to these problems, including their different available versions. Comparison of the SF-PSO and the VCH-PSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used. We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.