Nonlinear Loss Research Articles

Spiral shaped highly sensitive rectangular PCF-based cancer cells detector in terahertz regime

Abstract The increasing fatality rate of cancer in the modern world has become a global concern. To address this issue, early detection of cancer cells in human blood is essential. In this article, a rectangular spiral shaped photonic crystal fiber has been proposed for sensing cancer cells in terahertz regime. The proposed PCF based sensor features a square shaped core surrounded by rectangular air holes of different sizes in a spiral manner. Finite element method based COMSOL Multiphysics software has been used for the design and analysis of the model. The model is sensitive to refractive index variation for the identification of cancer cells. The values of effective refractive index, effective mode area, relative sensitivity, non-linearity, and confinement loss are obtained for MCF-7, MDA-MB-231, Jurkat, HeLa, Basal and PC-12. The PCF based sensor resulted in excellent results of relative sensitivity and Confinement loss as good as 99.377% and 8.59 × 10−12 dBm−1 respectively. Furthermore, the present PCF based sensor model is fabrication feasible, and we hope that it can be utilized at industrial level for biosensing applications in terahertz waveguide regime.

Dynamics of Nonlinear Optical Losses in Silicon‐Rich Nitride Nano‐Waveguides

AbstractFree carrier absorption (FCA) is established to be the cause of nonlinear losses in plasma‐enhanced chemical vapor deposition (PECVD) silicon‐rich nitride (SRN) waveguides. To validate this hypothesis, a photo‐induced current is measured in SRN thin films with refractive indices varying between 2.5 and 3.15 when a C‐band laser light is illuminating the SRN films at various powers, indicating the generation of free carriers. Furthermore, nonlinear loss dynamics is, for the first time, measured and characterized in detail in SRN waveguides by utilizing high peak power C‐band complex shape optical pulses for estimation of free carrier generation (FCG) and free carrier recombination (FCR) lifetimes and their dynamics. Both FCG and FCR are found to decrease with an increase in the refractive index of SRN, and, specifically, the FCR lifetimes are found (92 ± 7) ns, (39 ± 3) ns, and (31 ± 2) ns for the SRN indices of 2.7, 3, and 3.15, respectively. Lastly, nonlinear losses in high refractive index SRN waveguides are demonstrated to be minimized and altogether avoided when the pulse duration reduced below the free carrier generation lifetime, thus providing a way of taking a full advantage of the large inherent SRN nonlinear properties.

Efficient second-harmonic generation in a lithium niobate metasurface governed by high-Q magnetic toroidal dipole resonances.

Lithium niobate (LN) is an excellent nonlinear optical material due to its large nonlinear coefficient, low loss, and broad optical transparency window. So, it is widely used in the generation of nonlinear harmonics. Magnetic toroidal dipole (MTD) resonance is a special optical resonance mode, which can effectively localize the light field inside the device, thus enhancing the nonlinear effects of the materials. In this work, we numerically study the second-harmonic generation (SHG) effect of the LN metasurface based on the MTD mode with a high quality factor (Q-factor). The designed LN nanorod dimer metasurface supports high Q-factor MTD guided mode resonances (GMRs), which are excited by varying the center spacing of the two nanorods, and the Q-factor can be controlled by the offset distance. The excited MTD can effectively confine the electric field within the device, which enables the LN metasurface SHG conversion efficiency to reach 1.15 × 10-2. In addition, by adjusting the structural parameters, it is possible to effectively modulate the wavelength and conversion efficiency of the SHG. Our results provide a new route for high-quality nonlinear light sources.

A reliable deterministic method for multi-area economic dispatch considering power losses

This paper offers a reliable deterministic method for solving multi-area economic dispatch (MAED). The proposed method can handle nonlinear power losses in electrical networks and discrete and nonlinear characteristics in power plants, such as fuel change facilities and nonconvex cost functions. To this end, the presented method reformulates the products of two variables in the power loss terms as one-dimensional expressions. Then, it converts all nonlinear terms in the MAED into linear segments. Moreover, it handles discrete decisions in fuel selection through binary variables. We assess the performance of the proposed method by some MAED test systems. The results show that the method can reliably obtain accurate solutions in the adopted test systems. It converges to costs of 10604.7, 654.7, 121592.7, 124628.4, and 169528.9 respectively in 2-area 4-unit, 3-area 10-unit, 4-area 40-unit, 2-area 40-unit, and 10-area 130-unit. These findings reproduce or outperform results found in earlier optimization algorithms. Moreover, the computational time evaluations indicate that the method can solve the adopted MAED problems in less than one minute, showing its efficiency. Overall, the obtained results confirm the efficacy of the method’s efficacy in handling the MAED with discrete and nonlinear terms.

Bayesian estimation for mean vector of multivariate normal distribution on the linear and nonlinear exponential balanced loss based on wavelet decomposition

This paper addresses the problem of Bayesian wavelet estimating the mean vector of multivariate normal distribution under a multivariate normal prior distribution based on linear and nonlinear exponential balanced loss functions. The covariance matrix of multivariate normal distribution is considered known. Bayes estimators of mean vector parameter of multivariate normal distribution are achieved. Then two soft shrinkage wavelet threshold estimators based on Stein’s unbiased risk estimate (SURE) and Bayes estimators are provided. Finally, the performance of the soft shrinkage wavelet estimators was checked through simulation study and Electrical Grid Stability Simulated data set. Simulation and real data results showed the better performance of SURE thresholds based on linear and nonlinear exponential balanced loss functions compared to other classical wavelet methods. Also, they showed better performance for SURE threshold based on nonlinear exponential balanced loss function in multivariate normal distribution with small dimensions.

A prediction method of diabetes comorbidity based on non-negative latent features

In this paper, we present a novel network-based approach, namely Inherently Non-negative Latent Feature Analysis for Diabetes Mellitus Comorbidity Detection (INDM), to enhance the detection and analysis of comorbidities associated with diabetes mellitus. Different from existing methods, INDM is the first computational approach that integrates comorbidity networks of the chronic disease spectrum with patient clinical characteristics. To perform the analytical tasks, the proposed INDM adopts the following core components. First, comorbidity networks representing patients diagnosed solely with hypertension and those with hypertension and diabetes are constructed, following the case-control design that establishes a 1:1 matching in age and gender between two cohorts. Subsequently, the disease set is modeled in the comorbidity network according to the relative risk methodology. This enables nodes and edges in the comorbidity network to represent disease interactions that are derived from the patient-disease bipartite graph. Second, a nonlinear loss function with the capability of inherently non-negative latent feature analysis followed by a comorbidity classifier is adopted to uncover the patterns indicating the diabetes comorbidity in the comorbidity network. The proposed INDM has been rigorously tested on actual diabetes comorbidity datasets. The notable results demonstrate that INDM exhibits superior detection accuracy. Furthermore, the topological structure discovered by the proposed INDM can provide a profound insight into hypertension comorbidity in both the case and control groups.

Microwave susceptibility analysis of ultrafast moment dynamics in a multicore magnetic nanoparticle system

Room-temperature magnetization dynamics of multicore magnetic nanoparticles often account for intrinsic dipolar magnetism to behave as a single macrospin at low-frequency regime. Either magnetic particle imaging or hyperthermia benefits from the resulting superparamagnetism in terms of nonlinear magnetization response and relaxation losses at frequencies where the rotation of magnetic moments dominates over Brownian motion for a given sinusoidal field. For this situation, spontaneous thermal relaxation (in the absence of external fields) of each composing particle moment within the cluster is critical to define effective Néel time constant and may intersect with ferromagnetic resonance (FMR) at GHz range. Here, we performed broadband AC susceptometry on both immobilized single-core and multicore iron-oxide nanoparticles up to 26.5GHz under DC bias fields. For each solid sample, we confirmed FMR frequency, where large single-core nanoparticle systems demonstrated typical resonance blueshift as DC field increased. Interestingly, high DC field induced the secondary satellite peak in the imaginary part of AC susceptibility spectra for the case of multicore nanoparticle systems. We further highlighted that a synchronous precession of the polarized macrospins under nonuniform effective fields was responsible for splitting FMR peaks at nearby microwave frequencies. Upon curve fitting of the field-dependent FMR frequency spectra, the Landau-Lifshitz-Gilbert-Kittel model later elaborates on the complex moment dynamics of multicore nanoparticle systems in correlation with distribution functions. Published by the American Physical Society 2024

Understanding Phosphoric Acid Dynamics in High-Temperature Pemfcs: Mechanisms and Durability of PA-PBI and Ion-Pair-Based Systems.

High-Temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) can operate at > 100 oC under anhydrous conditions, providing benefits such as system simplification and superior contaminant tolerance. Most researches on HT-PEMFC have focused on PA-PBI-based system since 1995, however the durability of this system is still on controversial. Some insisted the stable operation of PA-PBI HT-PEMFC, which showed the stability under mild conditions such as low current density about 0.2 A cm-2, while others pointed out the instability specifically under accelerated stress conditions with higher current densities above 0.6 A cm-2, lower ( < 140 oC) or higher ( > 180 oC) operating temperatures, or dynamic protocols like start-up/shut-down cycles, etc.This limited durability of PA-PBI HT-PEMFC is known to be related to acid loss from the membrane-electrode-assemblies (MEAs). Several mechanisms were proposed to explain the linear and nonlinear acid loss behaviors. PA evaporation and steam-distillation are known to explain the constant PA loss. Despite the extensive efforts on mechanisms and acid loss mitigation strategies, current PA-PBI systems still struggle against the limited durability due to PA loss. To improve the durability of HT-PEMFC, a novel system was introduced based on the quaternary ammonium-biphosphate ion-pair coordinated PEM [1]. Due to a stronger interaction in the ion-pair PEMs, the durability was also improved by alleviating the PA loss during PEMFC operation. Nonetheless, the acid retention mechanism of this novel system is not explored yet.Here, we investigate the mechanisms of PA loss and PA retention within electrodes of both PA-PBI-based and ion-pair-based HT-PEMFC systems. Through PA energetics studies and experimental investigations under controlled stress conditions, we elucidate distinct PA loss/retention mechanisms in these systems, leading to different durability behaviors. Our findings offer insights for enhancing the durability of both HT-PEMFC systems [2].[1] Lim, K. H.; Lee, A. S.; Atanasov, V.; Kerres, J.; Park, E. J.; Adhikari, S.; Maurya, S.; Manriquez, L. D.; Jung, J.; Fujimoto, C.; Matanovic, I.; Jankovic, J.; Hu, Z.; Jia, H.; Kim, Y. S. Protonated phosphonic acid electrodes for high power heavy-duty vehicle fuel cells, Nature Energy, 7, 248 (2022).[2] Lim, K. H.; Matanovic, I.; Maurya, S.; Kim, Y.; Castro, E. S.; Jang, J-H.; Park, H.; Kim, Y. S. High temperature polymer electrolyte membrane fuel cells with high phosphoric acid retention, ACS Energy Letters, 8, 529 (2023).

Second register production on the clarinet: Nonlinear losses in the register hole as a decisive physical phenomenon.

This study investigates the role of localized nonlinear losses in the register hole of a clarinet in producing second-register notes. First, an experiment is conducted to study the ability of the opening of a register hole to trigger a jump in oscillatory regime from the first to the second register. A cylindrical tube is drilled with holes of increasing diameter: five at the register hole level and five at the thumb hole level of a B-flat clarinet. Clarinetists are asked to play with constant parameters, blindfolded, beginning with all holes closed. The operator randomly opens one of the ten holes, noting the resulting register. The experiment is replicated numerically by time integration of two different models. The first is the model from Taillard, Silva, Guillemain, and Kergomard [(2018). Appl. Acoust. 141, 271-280] based on the modal decomposition of the input impedance. The second accounts for localized nonlinear losses in the register hole, through the model from Dalmont, Nederveen, Dubos, Ollivier, Méserette, and Sligte [(2002). Acta Acust. united Ac. 88, 567-575]. These losses are handled through variable modal coefficients. For the first model, simulations never produce the second register for any of the open holes. For the second, the proportion of second-register production is close to the experiment for upstream holes, but remains at zero for downstream holes.

HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization.

Existing matrix factorization methods face challenges, including the cold start problem and global nonlinear data loss during similarity learning, particularly in predicting associations between long noncoding RNAs (LncRNAs) and diseases. To overcome these issues, we introduce HPTRMF, a matrix factorization approach incorporating high-order perturbation and flexible trifactor regularization. HPTRMF constructs a high-order correlation matrix utilizing the known association matrix, leveraging high-order perturbation to effectively address the cold start problem caused by data sparsity. Additionally, HPTRMF incorporates a flexible trifactor regularization term to capture similarity information on LncRNAs and diseases, enabling the effective handling of global nonlinear data loss by capturing such data in the similarity matrix. Experimental results demonstrate the superiority of HPTRMF over nine state-of-the-art algorithms in Leave-One-Out Cross-Validation (LOOCV) and Five-Fold Cross-Validation (5-Fold CV) on three data sets.HPTRMF and data sets are available in https://github.com/Llvvvv/HPTRMF.

Existence and stability of dissipative solitons in a dual-waveguide lattice with linear gain and nonlinear losses

In this study, we investigate the existence and stability of in-phase and out-of-phase dissipative solitons in a dual-waveguide lattice with linear localized gain and nonlinear losses under both focusing and defocusing nonlinearities. Numerical results reveal that both types of dissipative solitons bifurcate from the linear amplified modes, and their nonlinear propagation constant changes to a real value when nonlinearity, linear localized gain, and nonlinear losses coexist. We find that increasing the linear gain coefficient leads to an increase in the power and propagation constant of both types of dissipative solitons. For defocusing nonlinearity, in-phase solitons are stable across their entire existence region, while focusing nonlinearity confines them to a small stable region near the lower cutoff value in the propagation constant. In contrast, out-of-phase solitons have a significantly larger stable region under focusing nonlinearity compared to defocusing nonlinearity. The stability regions of both types of dissipative solitons increase with increasing nonlinear losses coefficient. Additionally, we validate the results of linear stability analysis for dissipative solitons using propagation simulations, showing perfect agreement between the two methods.

The effects of thermalization on the performance of passively Q-switched IC-OPOs

In this paper, we report a new model for passively Q-switched intracavity optical parametric oscillators (IC-OPOs), using Cr:YAG as a Q-switch crystal. The model considers the effects of thermalization in the Nd:YAG laser crystal, excited state absorption in the Cr:YAG saturable absorber, nonlinear loss due to pump depletion in the nonlinear crystal, the thermal lens effect and the mode overlap of the fields within the nonlinear crystal. The calculated pulse width for the 1,064 nm pump light was 6.2 ns compared to the experimental result of 6 ns, while the signal wave was calculated to have a pulse width of 1.75 ns compared to the experimental result of 2 ns. The model is shown to accurately calculate both the temporal and output power characteristics of the IC-OPO, which has not been previously reported to the best of our knowledge.

Stimulated Raman-induced beam focusing

Stimulated Raman scattering, employing a pump and a Stokes beam, exhibits itself through both the Raman loss observed in the pump beam and the Raman gain in the Stokes beam. This phenomenon finds application in spectroscopy for chemical analyses and microscopy for label-free bioimaging studies. Recent efforts have been made to implement super-resolution Raman microscopy using a doughnut-shaped pump, Stokes, or depletion beam. In this study, it is shown that the amplitude and phase of the pump or Stokes beam undergo significant modulation through the stimulated Raman process when they are configured as one of the higher-order Laguerre-Gauss modes, achieved using appropriate spiral phase plates or spatial light modulators. The resulting intensity distributions of the pump and Stokes beams are determined by a superposition of multiple Laguerre-Gauss modes that are coupled through nonlinear Raman gain and loss processes. Calculation results are used to elucidate the limitations associated with super-resolution coherent Raman imaging with a toroidal pump or Stokes beam. This stands in contrast with the stimulated emission depletion fluorescence microscopy technique, which has no fundamental limit in the spatial resolution enhancement.

Suspended nanomembrane silicon photonic integrated circuits

Leveraging the low linear and nonlinear absorption loss of silicon at mid-infrared (mid-IR) wavelengths, silicon photonic integrated circuits (PICs) have attracted significant attention for mid-IR applications including optical sensing, spectroscopy, and nonlinear optics. However, mid-IR silicon PICs typically show moderate performance compared to state-of-the-art silicon photonic devices operating in the telecommunication band. Here, we proposed and demonstrated suspended nanomembrane silicon (SNS) PICs with light guiding within deep-subwavelength waveguide thickness for operation in the short wavelength mid-IR region. We demonstrated key building components, namely, grating couplers, waveguide arrays, micro-resonators, etc., exhibiting excellent performances in bandwidths, back reflections, quality factors, and fabrication tolerance. Moreover, we show that the proposed SNS PICs have high compatibility with the multi-project wafer foundry services. Our study provides an unprecedented platform for mid-IR integrated photonics and applications.

Optimal management of a microgrid Li-Ion battery considering non-linear losses using the Integer Zig-Zag formulation

This paper presents a model for optimizing the management of a Li-Ion battery in a microgrid, considering the presence of nonlinear losses during the charging and discharging processes. The paper examines the nature of these losses and proposes updated nonlinear expressions that depend on the state of charge and on the charging or discharging power. The piecewise linear approximations of the losses bivariate functions are implemented using the novel integer zig-zag (ZZI) formulation. The corresponding ZZI constraints are integrated into an optimization model to determine the optimal schedule for a residential isolated microgrid comprising a solar panel, a diesel generator, and a Li-Ion battery. The case study compares different triangulation strategies regarding their impact on the quality of the obtained solution and the computational burden.

The Discrete Nonlinear Schrödinger Equation with Linear Gain and Nonlinear Loss: The Infinite Lattice with Nonzero Boundary Conditions and Its Finite-Dimensional Approximations

The Discrete Nonlinear Schrödinger Equation with Linear Gain and Nonlinear Loss: The Infinite Lattice with Nonzero Boundary Conditions and Its Finite-Dimensional Approximations

Physics-aware recurrent convolutional neural networks for modeling multiphase compressible flows

Multiphase compressible flow systems can exhibit unsteady and fast-transient dynamics, marked by sharp gradients and discontinuities, and material boundaries that interact with the evolving flow. The transient nature of the dynamics presents challenges to employing artificial intelligence (AI) and data-driven models for predicting flow behaviors. In this study, we explore the potential of physics-aware recurrent convolutional neural networks (PARC) to model the spatiotemporal dynamics of multiphase flows in the presence of shocks and reaction fronts. PARC is a neural network model that incorporates the generic form of the diffusion–advection–reaction equation in its network architecture, which mimics the process of solving the governing equations of fluid flows. In contrast to physics-informed machine learning approaches such as physics-informed neural networks (PINNs) where models are trained to directly minimize the residual of governing equations, PARC takes a dynamical systems viewpoint and does not seek to minimize potentially nonconvex and nonlinear loss terms. To assess the ability of PARC to accurately learn and simulate the physics of multiphase flows, we train and test PARC on various flow simulation problems, including the Burgers’ equation, fluid flow behind a cylindrical cross-section, and unsteady shock interactions with a particle at varying Mach numbers. We analyze PARC’s performance and examine sources of error in its prediction, in terms of differentiation and integration schemes and different weighting strategies for the model update. Based on our observations, we discuss PARC’s capabilities and limitations in multiphase flow applications and propose future research directions.

Silicon‐Rich Nitride Refractive Index as a Degree of Freedom to Maximize Nonlinear Wave Mixing in Nanowaveguides

Silicon nitride is widely used in integrated photonics for optical nonlinear wave mixing due to its low optical losses combined with relatively high nonlinear optical properties and a wide‐range transparency window. It is known that a higher concentration of Si in silicon‐rich nitride (SRN) magnifies both the nonlinear response and optical losses, including nonlinear losses. To address the trade‐off, four‐wave mixing (FWM) is implemented in over a hundred SRN waveguides prepared by plasma‐enhanced chemical vapor deposition in a wide range of SRN refractive indices varying between 2.5 and 3.2 (measured in the C‐band). It is determined that SRN with a refractive index of about 3 maximizes the FWM efficiency for continuous‐wave operation, indicating that the refractive index of SRN is indeed a crucial optimization parameter for nonlinear optics applications. The FWM efficiency is limited by large nonlinear optical losses observed in SRN waveguides with indices larger than 2.7, which are not related to two‐photon absorption. Finally, the third‐order susceptibility and the nonlinear refractive index are estimated for multiple SRN refractive indices, and, specifically, the nonlinearities as large as and are estimated in a waveguide with an SRN refractive index of 3.2.

Hybrid method for hydraulic transient analysis of liquid pipe networks based on characteristic lines and equivalent circuits

Water hammer poses a significant concern in the transportation of pipelines and pipe networks. Due to the passage of reflection of water hammer waves at nodes, a large number of high-frequency pressure fluctuation signals appear in the pipe network, demanding an intensive computational workload. A novel hybrid method based on characteristic lines and equivalent circuits is proposed for the accurate and fast simulation of pipe networks. Multiple methods are employed for the simulation of pipelines and pipe networks, followed by a comparative analysis. The accuracy of the hybrid method is validated by comparing the proposed method with simulation results of commercial software. The hybrid method reduces the significant computational workload inherent in the classic method of characteristics and enhances the accuracy at high frequencies in the state-of-the-art elastic water column model. A trade-off between simulation accuracy and computation speed is achieved by adjusting the time step. The accuracy of calculating higher-frequency pressure fluctuations is further validated with an increase in the pipe diameter and a corresponding reduction in the friction loss. Generally, this method enables the simultaneous descriptions of high-frequency characteristics and nonlinear friction loss, enhancing calculation speed with little impact on the accuracy. The results demonstrate that the proposed model can be utilized for the transient analysis of pipe networks, providing support for predicting system performance and designing optimal strategies.

Worst-case Conditional Value at Risk for asset liability management: A framework for general loss functions

Asset–liability management (ALM) is a challenging task faced by pension funds due to the uncertain nature of future asset returns, employees’ wages, and interest rates. To address this challenge, this paper presents a new mathematical model that uses a Worst-case Conditional Value-at-Risk (WCVaR) constraint to ensure that, with high probability, the funding ratio remains above a regulator-mandated threshold under the worst-case density function that plausibly explains historical sample data. A tractable reformulation of this WCVaR constraint is developed based on the definition of the Worst-case Lower Partial Moment (WLPM) for a general loss function. Additionally, a data-driven moment-based ambiguity set is constructed to capture uncertainty in the moments of the density functions of random variables in the ALM problem. The proposed approach is evaluated using real-world data from the Canada Pension Plan (CPP) and is shown to outperform classical ALM models, based on either CVaR or WCVaR with fixed moments, on out-of-sample data. The proposed framework for handling correlated uncertainty using WCVaR with nonlinear loss functions can be used in other application areas.