Nonlinear Profile Research Articles

Efficiency enhancement of ultrathin CIGS solar cells by optimal bandgap grading. Part III: piecewise-homogeneous grading

In Parts I [ Appl. Opt. 58, 6067 (2019)APOPAI0003-693510.1364/AO.58.006067] and II [ Appl. Opt. 61, 10049 (2022)APOPAI0003-693510.1364/AO.474920], we used a coupled optoelectronic model to optimize a thin-film CIGS solar cell with a graded-bandgap photon-absorbing layer, periodically corrugated backreflector, and multilayered antireflection coatings. Bandgap grading of the CIGS photon-absorbing layer was continuous and either linear or nonlinear, in the thickness direction. Periodic corrugation and multilayered antireflection coatings were found to engender slight improvements in the efficiency. In contrast, bandgap grading of the CIGS photon-absorbing layer leads to significant enhancement of efficiency, especially when the grading is continuous and nonlinear. However, practical implementation of continuous nonlinear grading is challenging compared to piecewise-homogeneous grading. Hence, for this study, we investigated piecewise-homogeneous approximations of the optimal linear and nonlinear grading profiles, and found that an equivalent efficiency is achieved using piecewise-homogeneous grading. An efficiency of 30.15% is predicted with a three-layered piecewise-homogeneous CIGS photon-absorbing layer. The results will help experimentalists to implement optimal designs for highly efficient CIGS thin-film solar cells.

Multi-Channel Nonlinearity Mitigation Using Machine Learning Algorithms

This paper investigates multi-channel machine learning (ML) techniques in the presence of receiver nonlinearities and noise, and compares the results with the single-channel receiver architecture. It is known that the multi-channel architecture relaxes the sampling speed requirement of analog to digital conversion and provides significant robustness to clock jitter and front-end noise due to the bandwidth-splitting property inherent in these receivers. However, when a high-voltage swing signal is used in a wireline communication link, the received signal suffers from third-order harmonic distortions and inter-modulation products caused by the nonlinearity profile of the analog front-end (AFE). To this end, this paper proposes the channel decision passing (CDP) algorithm in combination with nonlinear feedback cancellation as a low-complexity candidate for nonlinearity mitigation and compares the performance of this solution with other well-known ML algorithms. Simulation results show significant improvement in a multi-channel receiver architecture equipped with nonlinear feedback cancellation and CDP in comparison with its single-channel counterpart under practical nonlinearity profiles and noise conditions.

Wave-gravity-induced sediment transport on steep shoreface

ABSTRACT This research is dedicated to examining downslope sediment transport on steep shorefaces. We present a model that incorporates nonlinear surface wave profiles, sediment movement thresholds, and slope effects, utilizing a set of semi-empirical formulas. The model quantitatively assesses the disparity between wave-augmented upslope transport and gravity-augmented downslope transport, and computes sediment transport rates using a single calibration coefficient. Validation of the model is carried out in the Fuji coast of Japan, where offshore wave conditions and seabed topography with a uniform grain size distribution serve as inputs. The computational outcomes reveal that sediment transport primarily occurs under high wave conditions, and downslope transport dominates on steep slopes. The calibration coefficient is determined through a comparison with observed data, demonstrating a strong agreement in the average annual sediment loss in the target area. Moreover, the model offers insights into the possible mechanism behind the existence of a transition area between the upper and lower shoreface, marked by an abrupt change in seabed slope.

Thermal Vibration Analysis of Functionally Graded Porous Plates Reinforced by Graphene Platelets Supported by Arbitrarily Distributed Kerr Foundations Under a Nonlinear Temperature Profile

Thermal Vibration Analysis of Functionally Graded Porous Plates Reinforced by Graphene Platelets Supported by Arbitrarily Distributed Kerr Foundations Under a Nonlinear Temperature Profile

An Analytical Model for Nonlinear-Elastic Compliant Mechanisms with Tension-Compression Asymmetry

Abstract While nonlinear-elastic materials demonstrate potential in enhancing the performance of compliant mechanisms, their behavior still needs to be captured in a generalized mechanical model. To inform new designs and functionality of compliant mechanisms, a better understanding of nonlinear-elastic materials is necessary and, in particular, their mechanical properties that often differ in tension and compression. In the current work, a beam-based analytical model incorporating nonlinear-elastic material behavior is defined for a folding compliant mechanism geometry. Exact equations are derived capturing the nonlinear curvature profile and shift in the neutral axis due to the material asymmetry. The deflection and curvature profile are compared with finite element analysis along with stress-distribution across the beam thickness. The analytical model is shown to be a good approximation of the behavior of nonlinear-elastic materials with tension-compression asymmetry under the assumptions of the von Kármán strain theory. Through a segmentation approach, the geometries of a semicircular arc and folding compliant mechanism design are defined. The deflection of the folding compliant mechanism due to an applied tip load is then evaluated against finite element analysis and experimental results. The generalized methods presented highlight the utility of the model for designing and predicting the behavior of other compliant mechanism geometries and different nonlinear-elastic materials.

A simple and rapid HPLC-UV method for the determination of valproic acid in human plasma using microwave-assisted derivatization with phenylhydrazine hydrochloride

This study presents an efficient high-performance liquid chromatography with ultraviolet detection (HPLC-UV) method for monitoring valproic acid (VPA) level in human plasma. This method is distinguished by its simplicity, cost-effectiveness, and rapid execution, addressing the limitations associated with other advanced analytical techniques like liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and immunoassays, which are generally complex and costly for routine application. A challenge in analyzing VPA is its non-linear protein binding profile and the absence of a chromophore in its structure, making direct detection difficult. To overcome this, the study developed an efficient HPLC-UV for VPA determination in human plasma, utilizing a simplified and rapid microwave-assisted derivatization process. Due to the lack of a chromophore in VPA structure, this work developed a microwave-assisted derivatization of VPA using phenylhydrazine hydrochloride (PH HCl). The process optimization was achieved at 450 W for 50 s, facilitating effective HPLC-UV detection. The derivatized product was characterized using 1H nuclear magnetic resonance (NMR) and Fourier transform infrared spectrometer (FT-IR). The derivative, identified as (Z)-N-phenyl-2-propylpentanehydrazonic acid, demonstrated specificity in plasma analysis with no detectable interference. The method exhibited a linear response for VPA concentrations ranging from 30 to 150 μg/mL, with a correlation coefficient exceeding 0.99. Recovery varied between 86.7% and 107%, with a maximum coefficient of variation (CV) of 10.0%. The findings suggest that the microwave-assisted derivatization technique substantially improves the feasibility and cost-effectiveness of HPLC-UV for the analysis of VPA in plasma. This method provides a viable alternative to conventional HPLC methodologies, offering a balance of efficiency and economic practicality for VPA quantification.

A Generalized Slab Model

Abstract We construct a generalized slab model to calculate the ocean’s linear response to an arbitrary, depth-variable forcing stress profile. To introduce a first-order improvement to the linear stress profile of the traditional slab model, a nonlinear stress profile, which allows momentum to penetrate into the transition layer (TL), is used [denoted mixed layer/transition layer (MLTL) stress profile]. The MLTL stress profile induces a twofold reduction in power input to inertial motions relative to the traditional slab approximation. The primary reduction arises as the TL allows momentum to be deposited over a greater depth range, reducing surface currents. The secondary reduction results from the production of turbulent kinetic energy (TKE) beneath the mixed layer (ML) related to interactions between shear stress and velocity shear. Direct comparison between observations in the Iceland Basin, the traditional slab model, the generalized slab model with the MLTL stress profile, and the Price–Weller–Pinkel (PWP) model suggest that the generalized slab model offers improved performance over a traditional slab model. In the Iceland Basin, modeled TKE production in the TL is consistent with observations of turbulent dissipation. Extension to global results via analysis of Argo profiling float data suggests that on the global, annual mean, ∼30% of the total power input to near-inertial motions is allocated to TKE production. We apply this result to the latest global, annual-mean estimates for near-inertial power input (0.27 TW) to estimate that 0.08 ± 0.01 TW of the total near-inertial power input are diverted to TKE production.

Simultaneous reproduction of experimental profiles, fluxes, transport coefficients, and turbulence characteristics via nonlinear gyrokinetic profile predictions in a DIII-D ITER similar shape plasma

Experimental conditions obtained on the DIII-D tokamak in the ITER Similar Shape (ISS) have been compared extensively with nonlinear gyrokinetic simulation using the CGYRO code [Candy et al., J. Comput. Phys. 324, 73–93 (2016)] with comparisons spanning ion and electron heat fluxes, electron and impurity particle transport, and turbulent fluctuation levels and characteristics. Bayesian optimization techniques [Rodriguez-Fernandez et al., Nucl. Fusion 62(7), 076036 (2022)], combined with nonlinear gyrokinetics, have been used to obtain simultaneously Qi, Qe, and Γe flux-matched profiles that are found to be in good agreement with experimental profile measurements. Synthetic diagnostics were used to compare measured beam emission spectroscopy and correlation electron cyclotron emission turbulent fluctuations with nonlinear simulation. Although some disagreements exist, nonlinear simulations are found to be in generally good agreement with measured fluctuation levels, spectral shapes, and measured radial trends in low-k δne/ne and δTe/Te. Low (Li and C) and mid-Z (Ca) impurity transport was also compared with these flux-matched simulations. Fully stripped, low-Z impurities are well reproduced by the gyrokinetic modeling while clear disagreement exists in comparisons with mid-Z impurities. Nonlinear gyrokinetic investigation into the Z dependence of impurity transport in the ISS conditions is also performed, demonstrating clear trends of impurity diffusion with impurity Z (both D∝Z and D∝1/Z) that vary with the radial location studied. These trends are shown to result from the local dominance of ion temperature gradient or ∇n driven trapped electron mode turbulence and may contribute to the disagreement between simulation and experiment in mid-Z impurity transport. The results of this work represent one of the most complete validation studies of the gyrokinetic model performed to date and provide an example of new capabilities for predicting performance in future fusion devices.

Chemical state and atomic structure in stoichiovariants photochromic oxidized yttrium hydride thin films

Abstract We investigate the effective oxidation state and local environment of yttrium in photochromic YHO thin film structures produced by e-beam evaporation, along with their chemical structure and optical properties. Transmission electron microscopy images reveal the oxidized yttrium hydride thin film sample exhibiting a three-layered structure. X-ray photoelectron spectroscopy (XPS) measurements manifest that the oxidation state of yttrium is modified, dependent on the film’s composition/depth. Furthermore, Ion beam analysis confirms that this variability is associated with a composition gradient within the film. X-ray absorption spectroscopy at the Y K-edge reveals that the effective oxidation state of yttrium is approximately +2.5 in the transparent/bleached state of YHO. Spectroscopic ellipsometry investigations showed a complex non-linear optical depth profile of the related sample confirming the dominant phase of YHO and the presence of Y2O3 and Y towards the middle of the film. The first evidence of (n; k) dispersion curves for e-beam sputtered photochromic YHO thin films are reported for transparent and dark states.

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

First-in-Human Safety, Tolerability, and Pharmacokinetics of Single-Dose Kukoamine B Mesylate in Healthy Subjects: A Randomized, Double-Blind, Placebo-Controlled Phase I Study.

Kukoamine B mesylate (KB) is a mesylate chrysamine B targeting lipopolysaccharides and CpG DNA, two potential treatment targets in sepsis. This first-in-human, randomized, double-blind, placebo-controlled, phase I study was conducted from July 2014 to May 2015 to explore the safety, tolerability, and pharmacokinetics of KB in healthy subjects. This study consisted of a pre-phase (four participants; KB at 0.005mg/kg) and a dose escalation phase (eight participants/dose group, randomized 6:2 to KB or placebo; KB at 0.02, 0.04, 0.08, 0.12, 0.24, and 0.48mg/kg). The primary endpoint was safety. Fifty-two participants were enrolled, including four in the pre-phase and 48 in the dose escalation phase. Among the 40 participants who received KB, 12 (30.0%) experienced adverse events (AEs), while two (16.7%) experienced AEs among 12 participants who received the placebo. The most common AEs in the KB group were headache (5.0%), influenza (5.0%) and positive white blood cell in urine (5.0%). After the administration of KB, the mean plasma elimination half was around 1.61-4.24h. The relationship between the KB plasma exposure of KB and the administered dose was not linear. The percentage of cumulative urinary excretion of KB was similar among the different dose groups (21.7-35.2%) and the urinary excretion of KB decreased significantly about 8h after administration. Single-dose KB demonstrated favorable safety and tolerability in healthy subjects at the dose level of 0.005-0.48mg/kg. KB exhibited a non-linear pharmacokinetic profile with a half-life of about 1.61-4.24h, which mainly distributed in plasma. ClinicalTrials.gov identifier, NCT02219971.

Characterization of the nonlinear THz focus for 2D THz spectroscopy

We present a sensitive imaging method capable of the selective characterization of the nonlinear THz beam profile, providing a direct handle to optimize χ(3) light-matter interactions that are critical to two-dimensional (2D) THz spectroscopies. In particular, this method facilitates the development of experimentally challenging 2D THz spectroscopies with multiple THz emitters, which enables direct investigations of fast picosecond dynamics in liquids and solids that are challenging in single-emitter 2D THz spectroscopic setups.

Optimization of Electric Bus Charging: Integrating Discrete Event Modeling in Public Transportation Systems

The transition to electric transportation is gaining more and more importance in the last years leading to new challenges to be addressed. A key role in this framework is represented by public transportation systems. This paper introduces a Discrete Event (DE) optimization model to optimize the charging scheduling of electric buses (EBs). By accounting for time tabling constraints, energy demand satisfaction, and the capability for simultaneous multi-socket charging, our model addresses the complexities inherent in managing EB fleets. Utilizing a periodic model to capture cyclical system behavior, we incorporate a detailed battery model reflecting nonlinear charging profiles to enhance accuracy in power requirement prediction and charging duration estimation. Validation of our model is performed using real-world data, demonstrating its practical applicability.

Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals.

Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series.

Symmetrical Doherty Power Amplifier With Extended Bandwidth and Back-Off Range Based on Nonlinear Current Profile

Symmetrical Doherty Power Amplifier With Extended Bandwidth and Back-Off Range Based on Nonlinear Current Profile

Neural Sequences and the Encoding of Time.

Converging experimental and computational evidence indicate that on the scale of seconds the brain encodes time through changing patterns of neural activity. Experimentally, two general forms of neural dynamic regimes that can encode time have been observed: neural population clocks and ramping activity. Neural population clocks provide a high-dimensional code to generate complex spatiotemporal output patterns, in which each neuron exhibits a nonlinear temporal profile. A prototypical example of neural population clocks are neural sequences, which have been observed across species, brain areas, and behavioral paradigms. Additionally, neural sequences emerge in artificial neural networks trained to solve time-dependent tasks. Here, we examine the role of neural sequences in the encoding of time, and how they may emerge in a biologically plausible manner. We conclude that neural sequences may represent a canonical computational regime to perform temporal computations.

Frequency-Variant Double-Zero Single-Pole Reactive Coupling Networks for Coupled-Resonator Microwave Bandpass Filters

In this work, a family of frequency-variant reactive coupling (FVRCAFVC) networks is introduced and discussed as new building blocks for the synthesis of coupled-resonator bandpass filters with real or complex transmission zeros (TZs). The FVRCAFVC is a type of nonideal frequency-dependent inverter that has nonzero elements on the diagonal of the impedance matrix, along with a nonlinear frequency-variation profile of its transimpedance parameter. The distinctive feature of these new FVRCsAFVC is that they can be modeled with a simple bridged-T network consisting of four reactive elements. Such FVRCsAFVCs can introduce one pole and up to two TZs—so that they are referred to as double-zero single-pole (DZSP) networks. DZSP networks allow the pole to be located above, below, or in-between the TZs. Depending on the choice of the elements in the bridged-T circuit, the position of the TZs and the pole can be controlled independently. The coupling matrix for filters with DZSP networks can be found by solving an inverse-structured nonlinear eigenvalue problem (ISNEVP). Examples of possible implementations of DZSP couplingAFVC networks are provided for both lumped and quasi-lumped-element circuits, as well as for transmission-line-based and waveguide technologies. The application of DZSP coupling networks to the design of microwave bandpass filters with generalized Chebyshev-type characteristics is illustrated by means of the synthesis of three microstrips and two waveguide coupled-resonator bandpass filters. The waveguide filters are verified through electromagnetic (EM) simulations and the microstrip filters are manufactured and characterized for proof-of-concept demonstration purposes, showing a fairly close agreement between EM simulations and measurements.

Output performance improvement for thermoelectric transistor with the consideration of the Thomson effect and geometry optimization

Despite significant attention paid to thermoelectric generators in the energy conversion field, their limited output performance has restricted their large-scale applications. Therefore, there is a pressing need to conceive a novel thermoelectric device concept that can strengthen their competitiveness. In this context, the concept and principle of thermoelectric (TE) transistor with ultrahigh performance is a demand-driven innovation. Built on the foundation of a PNP structure, TE transistor only relies on the Seebeck voltage to operate normally. This work employs P-type Bi0.5Sb1.5Te3 and N-type Bi2Te2.97Se0.03 as the component materials. By considering the conduction heat, Joule heat, Thomson heat, and temperature-dependent material properties, a one-dimensional heat transfer model was used to obtain the nonlinear temperature profile inside TE transistor. Further, the structure and operation conditions of TE transistor with varying geometric dimensions were determined based on the hole concentration distribution and build-in voltage variation. Finally, the optimal concentrations and geometric dimensions of TE transistor were obtained using a compromise method. The calculation results reveal that TE transistor can achieve an optimal output power of 133.7 mW under a temperature difference of 50 K (Th = 353 K, Tc = 303 K) with the corresponding conversion efficiency of 7.73%. This work provides theoretical guidance for future experimental validation of TE transistor.

Experimental and CFD modeling of a progressive cavity pump (PCP) using overset unstructured mesh part 1: Single-phase flow

Progressive Cavity Pumps (PCP) are integral in industries for transporting high-viscosity fluids due to their efficacy. Nevertheless, their computational modeling via Computational Fluid Dynamics (CFD) poses challenges, primarily attributed to meshing algorithms and computational demands. This research underscores that structured meshes are not imperative for assessing PCP efficiency, given that operational parameters reflect a 10% accuracy when juxtaposed with experimental data. An overset mass conservation model was harnessed to rectify mass conservation errors and the computational burden from interpolation. The overset grid methodology emerged as a favorable technique for PCP modeling, with its applicability critically analyzed. Experimental data comparison revealed that variables like volumetric flow rate, power consumption, and efficiency were accurately depicted with a marginal error of less than 10%. Interestingly, while high-viscosity oils resonated with the Hagen-Poiseuille flow equation in clearance zones, water showcased elevated velocities, inducing a non-linear pressure profile. Analysis of slip components indicated that high-viscosity oils offered superior efficiencies due to augmented slip resistance. Thus, CFD modeling of PCPs across different viscosity spectrums is feasible without mandating operations to enhance mesh quality. This study pioneers in offering insights into PCP dynamics, emphasizing the accuracy and optimization potential of the overset unstructured mesh approach.

Real-time vehicle relocation and staff rebalancing problem for electric and shared vehicle systems

ABSTRACT This study addresses the challenging real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem in electric carsharing services. The complexity arises from ad-hoc demand, charging requirements of electric vehicles (EVs), and staff scheduling constraints. The problem aims to maximize the profit of carsharing operators by determining strategies for vehicle relocation, vehicle charging, and staff rebalancing in real-time. It considers the uncertainty of demand and the practical nonlinear charging profile of EVs. We formulate this problem as a Markov Decision Process (MDP), and propose an efficient concurrent-scheduler-based policy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed policy and methodology. The results show that the proposed policy significantly improves service level and profitability compared to a benchmark policy. It is also found that ignoring staff rebalancing in decision making can lead to overestimation of service level and profitability. In conclusion, this study presents a real-time solution for vehicle relocation and staff rebalancing in one-way electric carsharing services. The proposed policy and methodology improve performance and highlight the importance of considering staff rebalancing in decision making.