Effects Of Parameter Variations Research Articles

Trigonal warping, satellite Dirac points, and multiple field tuned topological transitions in twisted double bilayer graphene

We show that the valley Chern number of the low energy band in twisted double bilayer graphene can be tuned through two successive topological transitions, where the direct bandgap closes, by changing the electric field perpendicular to the plane of the graphene layers. The two transitions with Chern number changes of -3 and +1 can be explained by the formation of three satellite Dirac points around the central Dirac cone in the moir\'e Brillouin zone due to the presence of trigonal warping. The satellite cones have opposite chirality to the central Dirac cone. Considering the overlap of the bands in energy, which lead to metallic states, we construct the experimentally observable phase diagram of the system in terms of the indirect bandgap and the anomalous valley Hall conductivity. We show that while most of the intermediate phase becomes metallic, there is a narrow parameter regime where the transition through three insulating phases with different quantized valley Hall conductivity can be seen. We systematically study the effects of variations in the model parameters on the phase diagram of the system to reveal the importance of particle-hole asymmetry and trigonal warping in constructing the phase diagram. We also study the effect of changes in interlayer tunneling on this phase diagram.

A Mars Environment Chamber Coupled with Multiple In Situ Spectral Sensors for Mars Exploration.

Laboratory simulation is the only feasible way to achieve Martian environmental conditions on Earth, establishing a key link between the laboratory and Mars exploration. The mineral phases of some Martian surface materials (especially hydrated minerals), as well as their spectral features, are closely related to environmental conditions. Therefore, Martian environment simulation is necessary for Martian mineral detection and analysis. A Mars environment chamber (MEC) coupled with multiple in situ spectral sensors (VIS (visible)-NIR (near-infrared) reflectance spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS), and UV-VIS emission spectroscopy) was developed at Shandong University at Weihai, China. This MEC is a comprehensive research platform for Martian environmental parameter simulation, regulation, and spectral data collection. Here, the structure, function and performance of the MEC and the coupled spectral sensors were systematically investigated. The spectral characteristics of some geological samples were recorded and the effect of environmental parameter variations (such as gas pressure and temperature) on the spectral features were also acquired by using the in situ spectral sensors under various simulated Martian conditions. CO2 glow discharge plasma was generated and its emission spectra were assigned. The MEC and its tested functional units worked well with good accuracy and repeatability. China is implementing its first Mars mission (Tianwen-1), which was launched on 23 July 2020 and successfully entered into a Mars orbit on 10 February 2021. Many preparatory works such as spectral databases and prediction model building are currently underway using MECs, which will help us build a solid foundation for real Martian spectral data analysis and interpretation.

Brake Noise Reduction Method Based on Monte Carlo Sampling and Particle Swarm Optimization

Brake noise is one of the principal components of vehicle noise and is also one of the most critical measures of vehicle quality. During the braking process, the occurrence of brake noise has a significant relationship with the working conditions of the brake system. In the present study, dynamometer test data and the finite element method (FEM) were used to analyze the direct and indirect effects of variations in the working parameters on the brake noise, and a brake noise reduction method was developed. With this method, Monte Carlo sampling was used to consider variations in the parameters of the brake lining during the braking procedure, and the particle swarm optimization method was used to calculate the optimal parameter combination for the brake lining. A dynamometer test was carried out to validate the effect of optimization on brake noise mitigation.

Repetitive Transcranial Magnetic Stimulation for Obsessive-Compulsive Disorder: A Meta-analysis of Randomized, Sham-Controlled Trials

BackgroundObsessive-compulsive disorder (OCD) is a chronic, disabling mental health condition with limited treatment options available to date. Numerous randomized controlled trials have explored the efficacy of repetitive transcranial magnetic stimulation (rTMS) in OCD. This meta-analysis synthesized data from selected randomized controlled trials and examined the impact of different treatment parameters to generate hypotheses that would direct future randomized controlled trials. MethodsA database search was performed to identify studies published in English up to October 2020. Randomized, sham-controlled studies that used rTMS to treat OCD were included. Effect sizes were calculated using Hedges’ g for pre- to post-treatment Yale-Brown Obsessive Compulsive Scale scores. Subgroup analyses were conducted to assess the effects of variations in rTMS treatment parameters. ResultsA total of 26 studies with 781 participants were included. Overall, rTMS demonstrated a modest effect on reduction of Yale-Brown Obsessive Compulsive Scale scores (Hedges’ g = 0.64, 95% confidence interval = 0.39–0.89; p < .0001). The largest significant effect size was obtained by targeting the bilateral dorsolateral prefrontal cortex. High- and low-frequency rTMS showed comparable effects. Studies with follow-up data suggested that the effects of active rTMS remain significantly superior to those of sham 4 weeks after treatment. ConclusionsThe therapeutic effects of rTMS are superior to those of sham in the treatment of OCD. Targeting the bilateral dorsolateral prefrontal cortex was the most favorable approach in administering rTMS. Further research is required to determine the optimal frequency, total pulses per session, and duration of treatment with rTMS for OCD.

Cotton Wastes Functionalized Biomaterials from Micro to Nano: A Cleaner Approach for a Sustainable Environmental Application.

The exponential increase in textile cotton wastes generation and the ineffective processing mechanism to mitigate its environmental impact by developing functional materials with unique properties for geotechnical applications, wastewater, packaging, and biomedical engineering have become emerging global concerns among researchers. A comprehensive study of a processed cotton fibres isolation technique and their applications are highlighted in this review. Surface modification of cotton wastes fibre increases the adsorption of dyes and heavy metals removal from wastewater. Cotton wastes fibres have demonstrated high adsorption capacity for the removal of recalcitrant pollutants in wastewater. Cotton wastes fibres have found remarkable application in slope amendments, reinforcement of expansive soils and building materials, and a proven source for isolation of cellulose nanocrystals (CNCs). Several research work on the use of cotton waste for functional application rather than disposal has been done. However, no review study has discussed the potentials of cotton wastes from source (Micro-Nano) to application. This review critically analyses novel isolation techniques of CNC from cotton wastes with an in-depth study of a parameter variation effect on their yield. Different pretreatment techniques and efficiency were discussed. From the analysis, chemical pretreatment is considered the most efficient extraction of CNCs from cotton wastes. The pretreatment strategies can suffer variation in process conditions, resulting in distortion in the extracted cellulose’s crystallinity. Acid hydrolysis using sulfuric acid is the most used extraction process for cotton wastes-based CNC. A combined pretreatment process, such as sonication and hydrolysis, increases the crystallinity of cotton-based CNCs. The improvement of the reinforced matrix interface of textile fibres is required for improved packaging and biomedical applications for the sustainability of cotton-based CNCs.

Estimating parking generation rate for Karbala holy city using multi-variables approach

Car parking planning, design, and management processes are very important to all cities and places to ensure efficient traffic system. Estimating the demand of car parking represents the significant start point for the success of these processes. Generally, there are many local and international estimating criteria, but such criteria need continuing update due to many reasons related to socioeconomic factors, lifestyle changes, development in technology, etc. Moreover, the majority of these criteria depend on single parameter for the estimation of parking demand; such as bed or employee for hospital, gross floor area or employee for office, and so on. The main aim of this research is to estimate the park generation rate for specific land uses depend on multivariable to increase the accuracy and limiting the effect of variation in parameters. Statistical analysis was conducted to create predicting models for each land use. The collected data was nominated for Karbala holy city, where different parameters are scaled for different city sectors. Groups of statistical models (i.e., simple, multi linear and nonlinear statistical models, and Weighted Linear Regression (WLR)) were used to create best representative relationship between the number of demands for car parking and multivariable parameters or factors affecting these lands used demands. Resulted statistical models were tested for best fit using statistical indices for model verification. Results disclose the significant of multivariable model compare with simple models. Also, WLR model shows it validity compare with multi-regression model for almost land use models. Consequently, for more accurate estimation the multi variable models are initiated with continuous need for updating.

Buckling analysis of square Functionally Graded Material (FGM) plate using Discrete Kirchhoff Mindlin Triangular (DKMT) element

This paper presents the convergence behavior of Discrete Kirchhoff Mindlin Triangular (DKMT) element in buckling analysis under uniaxial compression of square plate problems. The DKMT element has a good result for a thin plate and a thick plate. For the Functionally Graded Material (FGM) problem, the DKMT element is reformulated. FGM is a graded composite material that has high-temperature and structural flexibility resistance. The numerical results of mechanical buckling of square FGM plate under uniaxial compression using the DKMT element are reported. The critical buckling of the square FGM plate is compared to the reference existing solutions. The effects of parametric variation, such as the type of meshing, boundary conditions, power-law index, and ratio L/h are presented. The results show that the DKMT element gives good results on the buckling analysis of square FGM plate problems.

Propagation of hyperbolic sinusoidal Gaussian beams in uniaxial crystals orthogonal to the optical axis

In this paper, hyperbolic sinusoidal Gaussian(HSG) beam propagates in uniaxial crystal orthogonal to the propagation axis. Received field expression is evaluated analytically based upon Huygens-Fresnel integration. Numerical results are plotted in order to analyze the effect of variations in source beam parameters, crystal refractive index features, and propagation distance. We investigate that beam size and peak intensity of received beam can be managed by adjusting crystal structure. We anticipate that our results will be useful for optical tracking applications.

Variable-Speed PICO Hydel Energy Storage With Synchronverter Control to Emulate Virtual Inertia in Autonomous Microgrids

Autonomous microgrids are potential alternative to grid connectivity for powering remote communities around the globe. A sustainable microgrid with renewables and energy storage having minimum operation and maintenance routines is the most sought option. Batteries are predominantly used to support the stochastic behavior of renewables in such microgrids. However, they are prone to frequent failure and require periodic maintenance, which demands an alternative. Thus, in this article, the renewable powered irrigation system in India was configured to form sustainable pico hydel energy storage (PHES). To enhance the inertia of the microgrid with static sources, virtual inertia capability was induced into PHES by modified synchronverter technology. First, the small-signal modeling approach was presented to derive the closed-loop transfer function of the system. Subsequently, the effect of control parameter variation on system stability and the interaction between the governor and the synchronverter was investigated using eigenvalue analysis. Next, the performance of synchronverter was compared with the established vector control through time-domain simulations in MATLAB/Simulink. The simulation results revealed that the proposed strategy improved the inertial response of PHES and outperformed vector control by reducing peak overshoot, settling time, and steady-state error.

Adaptive hierarchical sliding mode control for full nonlinear dynamics of uncertain ridable ballbots under input saturation

AbstractThis research proposes a nonlinear adaptive controller based on a hierarchical sliding mode control (HSMC) framework to stabilize the operation of a ridable ballbot based on the full nonlinear dynamics (FND). The FND of ballbots is a multiple‐input‐multiple‐output underactuated system with a non‐invertible input matrix. By applying a novel approach to transform the FND into two subsystems, the invertibility of the input matrix of the transformed subsystems can be guaranteed. Thus, an HSMC scheme can be applied to control the ridable ballbot system based on stabilizing the transformed subsystems. In addition, to improve the robustness of HSMC, a new adaptive law is proposed to deal with the effects of exogenous disturbances and parameter variations on the modeled dynamics. Chattering and actuator saturation phenomena are also reduced or avoided with the proposed controller. Comparative simulations are conducted to demonstrate the merit features of the proposed approach and the robustness of the developed control algorithm.

A novel fractional nonautonomous chaotic circuit model and its application to image encryption

Abstract A novel fractional nonautonomous system is proposed by introducing fractional order meminductor-memristor based circuit. Four circuit models are presented by different arrangements for the elements and the dynamic behaviors for each circuit are explored. It is observed that the four systems exhibit chaotic and hyperchaotic behaviors which have been verified using Lyapunov exponents. Bifurcation diagrams and phase portraits are employed to examine the effects of parameters variation on the qualitative dynamics of each model. An image encryption scheme is presented based on pseudo chaos orbit generated by two interval extensions of chaotic circuit model. The security analysis is carried out to verify the robustness and efficiency of the encryption scheme against possible attacks.

The Effect of Input Parameter Variation on the Accuracy of a Vanadium Redox Flow Battery Simulation Model

Accurately predicting battery behavior, while using low input data, is highly desirable in embedded simulation architectures like grid or integrated energy system analysis. Currently, the available vanadium redox flow battery (VRFB) models achieve highly accurate predictions of electrochemical behavior or control algorithms, while the optimization of the required input data scope is neglected. In this study, a parametrization tool for a DC grey box simulation model is developed using measurements with a 10 kW/100 kWh VRFB. An objective function is applied to optimize the required input data scope while analyzing simulation accuracy. The model is based on a differential-algebraic system, and an optimization process allows model parameter estimation and verification while reducing the input data scope. Current losses, theoretical storage capacity, open circuit voltage, and ohmic cell resistance are used as fitting parameters. Internal electrochemical phenomena are represented by a self-discharge current while material related losses are represented by a changing ohmic resistance. Upon reducing input data the deviation between the model and measurements shows an insignificant increase of 2% even for a 60% input data reduction. The developed grey box model is easily adaptable to other VRFB and is highly integrable into an existing energy architecture.

A strategy for advanced biofuel production and emission utilization from macroalgal biorefinery using superstructure optimization

The rapid increase in the emission of greenhouse gases over the years signifies the urgent need to explore fuels that emit less CO2, such as biofuels. CO2 emission can be reduced further by productively utilizing the CO2 generated during industrial processes. In this study, we have proposed a strategy to produce advanced biofuel from macroalgal biorefinery and to utilize all waste streams from processing into value-added products. To achieve this aim, we developed a novel superstructure for biorefinery process synthesis based on Saccharina japonica (macroalgae) to determine an optimal design of the biorefinery. Process integration was performed to utilize direct greenhouse gas emissions from the biorefinery and to reduce pollutant emissions and freshwater consumption. A techno-economic and environmental mixed-integer non-linear model was formulated based on this superstructure. To achieve the economic and environmental goals, two objective functions were studied: maximization of the net present value and minimization of CO2 emissions. A comprehensive sensitivity and Monte Carlo simulation model was formulated to evaluate the effects of variation in key model parameters on the overall economics and to perform the risk assessment, respectively. The minimum ethanol selling price range for the integrated design was found to be USD 0.36–0.56/L. The optimal design achieved a 90% reduction in CO2 emissions, from 4.86 kg/s to 0.42 kg/s, as well as a 38.6% reduction in freshwater consumption. The risk of the optimal design was found to be 20–44% on the basis of the minimum selling price of ethanol. Therefore, this design can be implemented as an economically and environmentally feasible approach to biofuel production.

Prediction of net-tension failure of multi-bolt composite joints: A fast approach for laminates with arbitrary layup

In this study, a new fast approach to predict the net-tension failure strength of multi-bolt composite joints was developed based on finite fracture mechanics and p-version parametric finite element methods. In the proposed approach, stress distribution and stress intensity factor are obtained using the principle of superposition, based on which the problem can be divided into open hole tension and hole bearing problems. The stress distribution and stress intensity factor are determined by using p-version parametric finite element models, which is very computational efficient and can address laminates with arbitrary layup. For the validation purpose, the failure strengths of three multi-bolted joints were predicted and compared to the test data from public literatures, and good agreements were obtained. In addition, the effects of typical parameter variations on the net-tension failure strength were presented by performing a series of parametric studies, which would be a reference for engineering design of multi-bolt composite joints.

Relationship between the brain and the central pattern generator based on recurrent neural network

In animals, the command centers in the brain can drive the locomotion. However, it remains unclear how the brain modulates the locomotor central pattern generator (CPG). In this paper, a novel model is established to describe the relation between the brain and the CPG with time delay. The artificial recurrent neural network (RNN) consists of various computational modules that are used to model the brain. The brain synchronization under amplitude and frequency variations of the CPG and the effect of the RNN parameters variations on the CPG are investigated. In the paper, the excitatory neuron probability and average connections number are parameter space of RNN and the parameter space of CPG, which include frequency and amplitude. According to the simulation results, the best RNN synchronization could be obtained by finding the optimum parameters space between the RNN and the CPG. I propose that the parameter space of some CPGs is related to the parameter space of the brain. This leads to a brain load decrement that facilities the control action. The results are meaningful to investigate how to study the relationship between the brain and the locomotion.

Theoretical analysis of Sagnac Interferometer based highly sensitive temperature sensor on photonic crystal fiber

In this paper, a highly sensitive temperature sensor is proposed on the platform of photonic crystal fiber (filled with ethanol) and Sagnac interferometer. The sensing characteristics of the device are analyzed by the finite element method. The results show a high temperature sensitivity of 23.23 nm/°C and − 8.71 nm/°C for the fiber length of 24 cm within the temperature range of 45 °C to 60 °C. In addition, the maximum sensitivity of 29.12 nm/°C and − 18.75 nm/°C can be achieved by limiting the temperature range from 43 °C to 50 °C for the same fiber length. Additionally, the effects of structural parameter variation on sensitivity are graphically represented. At last, refractive index (RI) sensitivity, due to the temperature sensitive liquid, is also calculated and obtained average sensitivities are −72,857 nm/RIU and 45,000 nm/RIU as RI varies from 1.3485 to 1.3515. Due to the above promising results, the proposed device may find potential applications in surrounding environmental temperature detection.

Observers for High-Speed Sensorless PMSM Drives: Design Methods, Tuning Challenges and Future Trends

Rotor position and speed estimation methods are consolidated to reduce the cost and volume of permanent magnet synchronous motor (PMSM) sensorless control drives while maintaining high performance. Advanced nonlinear algorithms require accurate design for precise estimation tracking throughout the entire PMSM operation range. This paper presents a broad review of the main high-speed estimation methods for sensorless PMSM drives. First, the stability constraints and design methodologies of the main estimation techniques presented in the literature are discussed. In the second part, it is investigated the new observer design trends, which are used under non-ideal conditions, such as robustness to distortions, the effects of parameter variation, sensorless parameter estimation, and low sampling-frequency-to-speed ratio operation. Future trends on the design of observers for high-speed sensorless PMSM drives are also discussed.

Avr System Analysis and Simulation by Using Fopid and Parameters Variation Effects

The FOPID control units for an AVR system with a fractional filter are a unique fractional order. The main responsibility for controlling the reactive power and voltage level is an automated tensile regulator (AVR). PID controller, sensor, exciter, and stabiliser or amplifier are used for the module system. The system is designed according to state-space technology. The proposed controller must have seven independent parameters. A comparison of the published study with optimal adjusted AVR PID and FOPID controls also shows that the proposed controller is superior. The recommended controls derived from the bode analysis with their frequency response characteristics are shown. Finally, the resilience of the controller design is individually examined for both the parameter uncertainties of AVR system and outside storages introduced into AVR system. Given the overall results presented there are clear improvements to the performance of AVR system by a fractional filter in a proposed FOPID controller, and that the AVR system may successfully be applied to the suggested control.

Effects of Variations in Imaging Parameters on Image Quality of Non Contrast Computed Tomography Scans of Brain: A Cross-sectional Study

Introduction: The radiation dose absorbed during Computed Tomography (CT) investigation of the head, which could be reduced by altering the scan parameters, is of great concern considering the radiosensitive organs around this region. Aim: To investigate the relationship between variations in scan parameters and image quality in brain CT scanning. Materials and Methods: This was an experimental cross-sectional study conducted at a private diagnostic clinic in Enugu State, Nigeria, from September 2018 to February 2019 with a total sample size of 150. All adult patients who reported at the centre for non contrast CT of brain during the study period {but not limited to participants with Anteroposterior Circumference Diameter (APCD) 170-180 cm of brain} were selected using consecutive sampling method. The samples was divided into three groups, a control group and two experimental groups. Scan parameters such as kilovolts (kV), milliamperes (mA) and Gantry Rotation Time (GRT) varied across the three groups studied and the corresponding image parameters such as image noise, Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) were obtained and statistically analysed using both descriptive and inferential statistics. Results: There were statistically significant correlations of image parameters with scan parameters Group I (control): (CNR: r=0.857, p-value &lt;0.001; Image noise: r=0.206, p-value=0.125, and SNR: r=0.875, p-value &lt;0.001) (CNR: r=0.857, p-value &lt;0.001; Image noise: r=0.211, p=0.115 and Image noise: r=-0.164, p-value=0.124 and SNR: r=0.880, p-value &lt;0.001) for mA and kV, respectively. Group II (CNR: r=0.744, p-value &lt;0.001, Image noise: r=0.521, p-value=0.024, and SNR: r=0.765, p-value &lt;0.001), (CNR=r=0.761, p-value &lt;0.001, Image noise: r=0.241, p-value=0.011 and SNR: r=0.780, p-value p=0.024) for mA and GRT, respectively. Group III (CNR: r=0.684, p-value=0.032; Image noise: r=0.646, p-value=0.032 and SNR: r=0.664, p-value=0.001) for mA. There was statistically significant relationship between scan parameters and radiation does parameters across all the sub groups at p-value &lt;0.05. Conclusion: Altering the scan parameters (mA, kV and GRT) during CT brain investigation has tremendous effects on the image quality, particularly on the image noise and also on the radiation dose received by the patients.

Physical Parameter Variation Analysis on the Performance Characteristics of Nano DG-MOSFETs

DG-MOSFETs are the most widely explored device architectures for nano-scale CMOS circuit design in sub-50 nm due to the improved subthreshold slope and the reduced leakage power compared to bulk MOSFETs. In thin-film (tsi by tsi- induced quantum confinement along with the confinement caused by a very high electric field at the interface. Therefore, quantum confinement effects on the device characteristics are also quite important and it needs to be incorporated along with short channel effects for nano-scale circuit design. In this paper, we analyzed a DG-MOSFET structure at the 20 nm technology node incorporating quantum confinement effects and various short channel effects. The effect of physical parameter variations on performance characteristics of the device such as threshold voltage, subthreshold slope, ION - IOFF ratio, DIBL, etc. has been investigated and plotted through extensive TCAD simulations. The physical parameters considered in this paper are operating temperature (Top), channel doping concentration (Nc), gate oxide thickness (tox) and Silicon film thickness (tsi). It was observed that quantum confinement of charge carriers significantly affected the performance characteristics (mostly the subthreshold characteristics) of the device and therefore, it cannot be ignored in the subthreshold region-based circuit design like in many previous research works. The ATLASTM device simulator has been used in this paper to perform simulation and parameter extraction. The TCAD analysis presented in the manuscript can be incorporated for device modeling and device matching. It can be used to illustrate exact device behavior and for proper device control.