Insight into the Impact of Oxidative Stress on the Barrier Properties of Lipid Bilayer Models

As a new field of oxidative stress-based therapy, cold physical plasma is a promising tool for several biomedical applications due to its potential to create a broad diversity of reactive oxygen and nitrogen species (RONS). Although proposed, the impact of plasma-derived RONS on the cell membrane lipids and properties is not fully understood. For this purpose, the changes in the lipid bilayer functionality under oxidative stress generated by an argon plasma jet (kINPen) were investigated by electrochemical techniques. In addition, liquid chromatography-tandem mass spectrometry was employed to analyze the plasma-induced modifications on the model lipids. Various asymmetric bilayers mimicking the structure and properties of the erythrocyte cell membrane were transferred onto a gold electrode surface by Langmuir-Blodgett/Langmuir-Schaefer deposition techniques. A strong impact of cholesterol on membrane permeabilization by plasma-derived species was revealed. Moreover, the maintenance of the barrier properties is influenced by the chemical composition of the head group. Mainly the head group size and its hydrogen bonding capacities are relevant, and phosphatidylcholines are significantly more susceptible than phosphatidylserines and other lipid classes, underlining the high relevance of this lipid class in membrane dynamics and cell physiology.     

Restoration of images degraded by compound noise sources using Markov random field models

The main theme of this paper is the derivation of a new algorithm for restoring digitized images degraded by both additive and multiplicative noise sources. In order to keep the derivation sufficiently general, the authors also include degradation caused by blur and a class of nonlinearities. The images under consideration are modeled as Markov random fields, while the additive and multiplicative noise sources are assumed to be Gaussian processes with known means and variances. Blurring of images is accomplished by a shift-invariant point-spread function. Test results with degraded images indicate that the algorithm is effective in restoring images degraded by high levels of additive and multiplicative noise.     

SD‐WLB: An SDN‐aided mechanism for web load balancing based on server statistics

Software‐defined networking (SDN) is a modern approach for current computer and data networks. The increase in the number of business websites has resulted in an exponential growth in web traffic. To cope with the increased demands, multiple web servers with a front‐end load balancer are widely used by organizations and businesses as a viable solution to improve the performance. In this paper, we propose a load‐balancing mechanism for SDN. Our approach allocates web requests to each server according to its response time and the traffic volume of the corresponding switch port. The centralized SDN controller periodically collects this information to maintain an up‐to‐date view of the load distribution among the servers, and incoming user requests are redirected to the most appropriate server. The simulation results confirm the superiority of our approach compared to several other techniques. Compared to LBBSRT, round robin, and random selection methods, our mechanism improves the average response time by 19.58%, 33.94%, and 57.41%, respectively. Furthermore, the average improvement of throughput in comparison with these algorithms is 16.52%, 29.72%, and 58.27%, respectively.     

Adaptive Vehicle Lateral-Plane Motion Control Using Optimal Tire Friction Forces With Saturation Limits Consideration

This paper presents an adaptive nonlinear control scheme aimed at the improvement of the handling properties of vehicles. The control inputs for steering intervention are the steering angle and wheel torque for each wheel, i.e., two control inputs for each wheel. The control laws are obtained from a nonlinear 7-degree-of-freedom (DOF) vehicle model. A main loop and eight cascade loops are the basic components of the integrated control system. In the main loop, tire friction forces are manipulated with the aim of canceling the nonlinearities in a way that the error dynamics of the feedback linearized system has sufficient degrees of exponential stability; meanwhile, the saturation limits of tires and the bandwidth of the actuators in the inner loops are taken into account. A modified inverse tire model is constructed to transform the desired tire friction forces to the desired wheel slip and sideslip angle. In the next step, these desired values, which are considered as setpoints, are tackled through the use of the inner loops with guaranteed tracking performance. The vehicle mass and mass moment of inertia, as unknown parameters, are estimated through parameter adaptation laws. The stability and error convergence of the integrated control system in the presence of the uncertain parameters, which is a very essential feature for the active safety means, is guaranteed by utilizing a Lyapunov function. Computer simulations, using a nonlinear 14-DOF vehicle model, are provided to demonstrate the desired tracking performance of the proposed control approach.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Flexible Prime‐Field Genus 2 Hyperelliptic Curve Cryptography Processor with Low Power Consumption and Uniform Power Draw

This paper presents an energy‐efficient (low power) prime‐field hyperelliptic curve cryptography (HECC) processor with uniform power draw. The HECC processor performs divisor scalar multiplication on the Jacobian of genus 2 hyperelliptic curves defined over prime fields for arbitrary field and curve parameters. It supports the most frequent case of divisor doubling and addition. The optimized implementation, which is synthesized in a 0.13 μm standard CMOS technology, performs an 81‐bit divisor multiplication in 503 ms consuming only 6.55 μJ of energy (average power consumption is 12.76 μW). In addition, we present a technique to make the power consumption of the HECC processor more uniform and lower the peaks of its power consumption.     

Ferroic Halide Perovskite Optoelectronics

Metal halide perovskites (MHPs) as one of the most active materials gained tremendous attention in the past decade because of their outstanding performance in optoelectronics. Owing to their perovskite structure, ferroelectricity is anticipated in this class of materials. However, whether MHPs are ferroelectric or not remains elusive. Recently, discussion regarding ferroelasticity in MHPs has been also raised. In addition, ionic motion and structural dynamics are well known in MHPs. The interplay of these phenomena including electric polarization, strain, ionic motion, and structural dynamics can have a significant impact on optoelectronics. Therefore, understanding the mechanism behind these phenomena and their interactions is critical in addressing the controversy about ferroicity of MHPs and developing functional devices. Here, the current findings about MHP's ferroicity are summarized and evaluated and a perspective for the future is provided. It is suggested that ionic motion and associated phenomena, coupled with ferroic behavior, are the main drivers behind MHPs functionality. The challenges are also discussed in probing MHPs’ ferroicity and what new measurement modalities are needed to fully understand and characterize MHP behavior. Finally, it is discussed how ferroic and strain can affect the optoelectronic performance of MHPs and how they can be used for engineering of higher performance devices.     

Ionization coefficient of monolayer graphene nanoribbon

A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy E_t and the distance travelled by electron gaining E_t. In addition, ionization threshold energy is semi-analytically modelled for GNR. During modelling, we justify our assumptions using analytical modelling and comparison with simulation. Furthermore, it is shown that conventional silicon models are not valid for calculation of ionization coefficient of GNR. Finally, the profile of ionization is presented using the proposed models and the results are compared with that of silicon.     

Improved impossible differential and biclique cryptanalysis of HIGHT

HIGHT is a lightweight block cipher introduced in CHES 2006 by Hong et al as a block cipher suitable for low‐resource applications. In this paper, we propose improved impossible differential and biclique attacks on HIGHT block cipher both exploiting the permutation‐based property of the cipher's key schedule algorithm as well as its low diffusion. For impossible differential attack, we found a new 17‐round impossible differential characteristic that enables us to propose a new 27‐round impossible differential attack. The total time complexity of the attack is 2^120.4 where an amount of 2^59.3 chosen plaintext‐ciphertext pairs and 2^107.4 memory are required. We also instantiate a new biclique cryptanalysis of HIGHT, which is based on the new idea of splitting each of the forward and backward keys into 2 parts where the computations associated to each one are performed independently. The time complexity and data complexity of this attack are 2^125.7 and 2⁴², respectively. To the best of our knowledge, this is the fastest biclique attack on full‐round HIGHT.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.