Parameter Extraction Research Articles

An analytic, efficient and optimal readout algorithm for compact interferometers based on deep frequency modulation

Compact laser interferometers with large dynamic range are one of the core emerging tools to improve low frequency performance in gravitational wave detectors by providing local displacement sensing with sub 1 pmHz-0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {pm Hz}^{-0.5}$$\\end{document} precision. Strong sinusoidal frequency modulations are used in such laser interferometers to create heterodyne-like photodetector signals from which the phase and other parameters, such as the absolute distance, can be extracted. The nested sinusoidal function in such signals is a challenge for the real-time parameter estimation in low-noise applications. In this article, we present an algorithm to calculate exact signal parameters in a non-iterative way from such interferometric signals. The algorithm makes use of a recurrence relation between Bessel functions to enable a direct extraction of modulation parameters from the signal. Additionally, the algorithm is capable of dealing with high phase dynamics where the Doppler-shift of the signal becomes relevant and can limit the range and precision of the parameter estimation, if not accounted for. Simulations show that the algorithm is computationally efficient, can be well parallelised and the phase estimation is close to optimal precision given by the Cramer–Rao lower bound of the signal parameters.

Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.

Valorisation of white wine lees: optimisation of subcritical water extraction of antioxidant compounds

This study explored the extraction of antioxidant compounds from white wine lees using subcritical water extraction (SWE). First, SWE was compared to extraction using conventional solvents mixed with water: 50 % methanol, 50 % ethanol and 50 % acetonitrile. The antiradical activity of the extract increased to 12.8 mg TE/g of extract dry matter (DM) using conventional solvents and to 29.2 mg TE/g DM with SWE, compared to the antiradical activity of dried lees, which was 5.7 mg Trolox Equivalent/g of dry matter (mg TE/g DM). Second, SWE conditions were optimised to obtain lees extracts with a high antioxidant activity by applying a Doehlert design and response surface methodology (RSM). The extraction parameters modulated during RSM optimisation were extraction time (t, 15 – 60 min), temperature (T, 100 – 250 °C) and stirring speed (S, 100 – 1000 RPM). Antioxidant activity was assessed using DPPH and FRAP tests, as well as by measuring the Oxygen Consumption Rate (OCR) of lees extracts. Temperature was found to be the main parameter that had a significant effect on antioxidant activity. The results of the RSM showed the optimal conditions (temperature, duration and speed) for attaining maximum antioxidant activity to be: 240 °C, 15 min and 550 RPM respectively. The best-known antioxidant compounds in white wine lees, Total Polyphenol Content (TPC), Glutathione (GSH) and Total Sulfhydryl groups (TSH) were also quantified. Extracts with the highest antioxidant capacity had the highest TPC and TSH concentrations. In conclusion, this study showed (i) that subcritical water extraction is a green process that has potential as an alternative to using conventional solvents for extracting white wine lees, (ii) the chemical components involved in their antioxidant capacity, and (iii) the potential of the lees for oenological and other industrial applications.

A Novel Spotted Hyena Optimizer for the Estimation of Equivalent Circuit Model Parameters in Li-Ion Batteries

Li-ion batteries possess significant advantages like large energy density, fast recharge, and high reliability; hence, they are widely adopted in electric vehicles, portable electronics, and military and aerospace applications. Albeit having their merits, accurate battery modeling is subjected to problems like prior information on internal chemical reactions, complexity in problem formulation, a large number of unknown parameters, and the need for extensive experimentation. Hence, this article presents a reliable Spotted Hyena Optimizer (SHO) to determine the equivalent circuit parameters of lithium-ion (Li-ion) batteries. The methodology of the SHO is derived from the living and hunting tactics of spotted hyenas, and it is efficiently applied to solve the battery parameter estimation problem. Nine unknown battery model parameters of a Samsung INR 18650-25R are determined using this method. The model parameters estimated are endorsed for five different datasets with various discharge current values. Further, the effect of parameter range and its selection is also emphasized. Secondly, for validation, various performance metrics such as Integral Squared Error, mean best, mean worst, and Standard Deviation are evaluated to authenticate the superiority of the proposed parameter extraction. From the computed results, the SHO algorithm is able to explore the search area up to 89% in the case of larger search ranges. The chosen model and range of the SHO precisely predict the behavior of the proposed Li-ion battery, and the results are in accordance with the catalog data.

Micro‐motion parameters extraction of rotating target based on analytical solution in vortex electromagnetic wave radar

AbstractThe linear Doppler effect and angular Doppler effect in vortex electromagnetic wave radar echoes can be used to extract comprehensive micro‐motion parameters. However, the analytical solution of micro‐motion parameters for rotating targets in vortex EM wave radar has not been obtained. To solve the above problems, the analytical solution of micro‐motion parameters is derived, and a micro‐motion parameter extraction method is proposed. Firstly, the shape and properties of the angular Doppler curve in various cases are discussed. Subsequently, the properties of three points on the angular Doppler curve are derived. The micro‐motion parameters are obtained based on the three points. The simulations demonstrate that the proposed method can greatly reduce the computational complexity and improve the accuracy of micro‐motion parameter extraction.

Priority radiomic parameters for computed tomography of head and neck malignancies: A systematic review

BACKGROUND: Radiomics is the newest and most promising direction in modern radiographic diagnostics. The number of head and neck cancer studies employing radiomics is increasing annually. A systematic review of recent publications (2021–2023) on computed tomography (CT) of head and neck malignancies was performed. AIM: To present systematized data on parameters for radiomic analysis for head and neck malignancies identified by CT data. MATERIALS AND METHODS: The literature search was carried out in PubMed. The basic characteristics of the selected articles were extracted, and their quality was assessed using RQS 2.0 and the modified QUADAS-CAD questionnaire. The reproducibility level of radiomic parameters selected for predictive models in different studies was assessed. Eleven articles were selected for the review. In most cases, a high risk of systematic error associated with data imbalance in terms of demographic parameters and level of pathologies was noted. RESULTS: The range of RQS 2.0 scores for the included articles varied from 19.44% to 50.00% of the maximum possible score. The decreasing research quality was mainly caused by the lack of external result validation (73% of the analyzed articles) and data accessibility and transparency (82%). Inter-study reproducibility of radiomic parameters was low owing to the wide variety of techniques used for image acquisition, image post-processing, extraction, and statistical processing of radiomic parameters. CONCLUSION: A set of stable radiomic parameters must be successfully introduced into clinical practice. The standardization of radiomics method and creation of an open radiomics database are necessary for this purpose.

Optimizing MFCC parameters for the automatic detection of respiratory diseases

Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) are widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC extraction parameters on respiratory disease diagnosis. In this study, we address this gap by examining the effects of key parameters, namely the number of coefficients, frame length, and hop length between frames, on respiratory condition examination. Our investigation uses four datasets: the Cambridge COVID-19 Sound database, the Coswara dataset, the Saarbrücken Voice Disorders (SVD) database, and a TACTICAS dataset. The Support Vector Machine (SVM) is employed as the classifier, given its widespread adoption and efficacy. Our findings indicate that the accuracy of MFCC decreases as hop length increases, and the optimal number of coefficients is observed to be approximately 30. The performance of MFCC varies with frame length across the datasets: for the COVID-19 datasets (Cambridge COVID-19 Sound database and Coswara dataset), performance declines with longer frame lengths, while for the SVD dataset, performance improves with increasing frame length (from 50 ms to 500 ms). Furthermore, we investigate the optimized combination of these parameters and observe substantial enhancements in accuracy. Compared to the worst combination, the SVM model achieves an accuracy of 81.1%, 80.6%, and 71.7%, with improvements of 19.6%, 16.10%, and 14.90% for the Cambridge COVID-19 Sound database, the Coswara dataset, and the SVD dataset respectively. To validate the generalization of these findings, we employ the Long Short-Term Memory (LSTM) model as a validation model. Remarkably, the LSTM model also demonstrates improved accuracy of 14.12%, 10.10%, and 6.68% across the datasets when utilizing the optimal combination of parameters. The optimal parameters are validated using an external voice pathology dataset (TACTICAS dataset). The results demonstrate the generalization capabilities of the optimized parameters across various pathologies, machine-learning models, and languages.

Investigating the impact of solvent extraction parameters on oil yield and oil properties: a review

Oilseeds are rich in fatty acids, proteins, fibers, minerals, and vitamins that are necessary for human health. It mainly comprises triglycerides and other minor components, including phytosterols, phenols, carotenoids, tocopherols, and phospholipids. Oils can be extracted using a variety of extraction methods including but not limited to the conventional mechanical and solvent method, and novel methods like supercritical fluid extraction. However, the quality and quantity of extracted oil depends on the process parameters. Among many process parameters temperature is the main parameter which controls the yield and its quality. Temperature range depends upon the nature and type of oilseed. High temperature leads to increased yield while relatively low temperature results in low oil yield and longer extraction time. Large particle size of the solid creates a temperature gradient between the inner and outer surface of the seeds which in turn lowers oil yield. Based on this review, an extraction temperature slightly below the boiling point of the solvent is recommended.

Modelling and Analysis of Inhomogeneous Back-to-Back Schottky Junction Diode

Back-to-back Schottky diode (BBSD) is a simple device structure made from Schottky junctions that can be applied in various applications. In modelling and simulating the BBSD, the inhomogeneity of the barrier height at the Schottky interface should be considered. This paper presents device models and simulation result of an inhomogeneous BBSD. Two device models with different current spreading condition are considered. The distribution of the inhomogeneous barrier height is represented by Gaussian distribution. All the computation was done using Python programming software. When the standard deviation of the barrier height increases, the apparent barrier height that influence the electrical characteristic decreased. This is consistent with the reported experimental data. Device model that considers full current spreading showed 37.8% reduction of apparent barrier height at standard deviation of 100 meV. This is greater that the model with no current spreading. The ideality factor showed correlation with the standard deviation of barrier height, particularly when no current spreading is considered. At standard deviation of 100 meV, the ideality factor increased 2.6 %. The obtained non-unity ideality factor is associated to the significant fluctuation in the barrier height. The computed temperature-dependent current curves also showed good agreement with the reported experimental results. The device model can be a convenient tool to investigate the device operation and to validate the method used for extraction of Schottky barrier parameters.

Mathematical Modeling of Sorptive Extraction of Lithium Chloride from Lithium-containing Brine of the Aral Sea Region

Introduction The article presents the results of chemical and physicochemical analysis of initial lithium-containing hydromineral raw materials and sorbent based on bentonite clay with titanium oxide addition. Methods It is established that the mineral is montmorillonite group and kaolinite according to the results of the analysis of the microstructure and elemental composition of bentonitic clay of the Darbazinsky deposit. Brine is characterized by the change of lithium chloride content in the range of 403- 1001 mg/l. Physical and mechanical characteristics of the obtained sorbents based on bentonite clay and titanium oxide additive are characterized by a high mechanical strength of 5.31 MPa, density of up to 2,6 g/cm3, and specific surface area of 1572 cm2/g. The technological parameters of sorption extraction of lithium chloride from lithium-containing hydromineral raw materials were optimized using the system analysis “STATISTICA” developed by StatSoft company. It is established that to increase the rate of lithium chloride extraction from brine, it is necessary to maintain the rate of brine flow within 8 l/min at maximum humidity of solution output up to 43g/m3 on the basis of the obtained volumetric graphical dependencies. Results The microstructure of the used sorbents with titanium oxide addition up to 20% is characterized by the predominance of lithium chlorides, which are represented by heap-shaped, prismatic crystals. Conclusion The maximum amount of lithium chloride in the form of clusters of light-white crystals of tabular shape on the surface of the sorbent is observed at increasing the content of titanium oxide up to 40%.

Two-step deep-learning identification of heel keypoints from video-recorded gait.

Accurate and fast extraction of step parameters from video recordings of gait allows for richer information to be obtained from clinical tests such as Timed Up and Go. Current deep-learning methods are promising, but lack in accuracy for many clinical use cases. Extracting step parameters will often depend on extracted landmarks (keypoints) on the feet. We hypothesize that such keypoints can be determined with an accuracy relevant for clinical practice from video recordings by combining an existing general-purpose pose estimation method (OpenPose) with custom convolutional neural networks (convnets) specifically trained to identify keypoints on the heel. The combined method finds keypoints on the posterior and lateral aspects of the heel of the foot in side-view and frontal-view images from which step length and step width can be determined for calibrated cameras. Six different candidate convnets were evaluated, combining three different standard architectures as networks for feature extraction (backbone), and with two different networks for predicting keypoints on the heel (head networks). Using transfer learning, the backbone networks were pre-trained on the ImageNet dataset, and the combined networks (backbone + head) were fine-tuned on data from 184 trials of older, unimpaired adults. The data was recorded at three different locations and consisted of 193 k side-view images and 110 k frontal-view images. We evaluated the six different models using the absolute distance on the floor between predicted keypoints and manually labelled keypoints. For the best-performing convnet, the median error was 0.55 cm and the 75% quartile was below 1.26 cm using data from the side-view camera. The predictions are overall accurate, but show some outliers. The results indicate potential for future clinical use by automating a key step in marker-less gait parameter extraction.

Optimizing a Facile and Rapid Method for Extraction of Flavonoids from Matricaria Chamomilla Using TiO2 Nanoparticles

AbstractFlavonoids are known as valuable natural compounds and Chamomile is a rich source of flavonoids. The extraction efficiency of flavonoids is affected by various conditions. So it appears necessary to develop a practical method for the extraction of these compounds. In this study, we attempted to extract the phenolic compounds from chamomile using the surface capacity of titanium oxide nanoparticles (TiO2). UV and FT‐IR spectroscopy was used to determine the accuracy of the binding. The HPLC‐PDA technique was used for the quantification of phenolic compounds and the recovery test result was 84 %. Determining the antioxidant, phenolic, and flavonoid contents suggested that the developed method is successful for the extraction of phenolic compounds. The influential extraction parameters after optimization consisted of the pH of 7, extraction temperature of 25 °C, extraction time of 15 min, and TiO2 nanoparticle concentration of 4 mM. The method seems acceptable for industrial applications.

Using natural deep eutectic solvents for the extraction of antioxidant compounds from cornelian cherry (Cornus mas L.) fruits

Traditionally used extraction methods are usually based on toxic and/or flammable solvents. Therefore, currently, increased attention has been focused on environmentally friendly green extraction techniques. We aimed to develop a green extraction procedure by using natural deep eutectic solvents (NADES) to recover antioxidant compounds from cornelian cherry fruits (Cornus mas L.). Natural deep eutectic systems combined with maceration with shaking extraction were tested to recover of antioxidant compounds from cornelian cherry fruits. In this study, four different independent variables, namely solvent type (Glu-CA; Glu-Gly; LA-Glu), water concentration (30, 50, and 70 %; v/v), extraction time (30, 60, 90, and 120 min), and extraction temperature (25, 40, and 50 °C) were investigated. Glucose-glycerin (Glu-Gly; 1:1 M ratio) based solvent provided the best results. In addition, one-factor-at-a-time (OFAT) optimization showed that a temperature of 25 °C, 120 min of extraction time, and water concentration of 50 % (v/v) to be the best extraction parameters for Glu-Gly based NADES extraction. The developed method has been successfully applied for the extraction of antioxidant compounds from 13 different cornelian cherry cultivars. NADES can be used safely to extract phenolic compounds from cornelian cherry fruits compared to conventional extraction solvents (water and 80 % (v/v) ethanol).

Deep learning based automated quantification of powders used in additive manufacturing

This study proposes a novel deep learning technique for efficient powder morphology characterization, crucial for successful additive manufacturing. The method segments powder particles in microscopy images using Pix2Pix image translation model, enabling precise quantification of size distribution and extraction of critical morphology parameters like circularity and aspect ratio. The proposed approach achieves high accuracy (Structural Similarity Index of 0.8) and closely matches established methods like laser diffraction in measuring particle size distribution (within a deviation of ∼7 %) and allows determination of additional particle attributes of aspect ratio and circualarity in a reliable, repeated, and automated way. These findings highlight the potential of deep learning for automated powder characterization, offering significant benefits for optimizing additive manufacturing processes.

Extraction Optimization of Quercus cerris L. Wood Chips: A Comparative Study between Full Factorial Design (FFD) and Artificial Neural Network (ANN).

From a circular bio-economy perspective, biomass valorization requires the implementation of increasingly efficient extraction techniques to ensure the environmental and economic sustainability of biorefining processes. This research focuses on optimizing the specialized metabolite extraction of Turkey oak chips from Quercus cerris L. by applying a 3 levels Full Factorial Design (FFD). The goal is to obtain an extract with the highest antioxidant activity [evaluated by 1,1-diphenyl-2-picryl hydrazyl (DPPH) scavenging activity and ferric reducing antioxidant power (FRAP) assays] and specialized metabolites content [measured as total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and hydrolysable tannins content (THC)]. With this objective, three different variables were investigated and compared: temperature (20 °C, 50 °C, 80 °C), solvents EtOH/H2O (0%, 20%, 40%), and time (3 h, 6 h, 24 h), resulting in 27 different extracts. Following the FFD analysis, the optimal extractive conditions were determined to be 80 °C, 40% EtOH/H2O, and 19.8 h. Finally, the prediction ability of FFD was compared with that of artificial neural network (ANN) for DPPH scavenging activity, FRAP, and TPC data based on the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that ANN predictions were more precise than FFD ones; however, both methods were useful in optimizing the extraction process as they returned comparable optimized extraction parameters.

Ultrasound-assisted polyphenols extraction with deep eutectic solvent and ethanol-based from Baccaurea macrocarpa pericarp and kinetic model

In this study, the extraction of polyphenols from the Baccaurea macrocarpa pericarp using ultrasonic-assisted deep eutectic solvents (DES-UAE) of 15 different types and ethanol (EtOH-UAE) was investigated. It was found that choline chloride-lactic acid (1:2) is the optimal choice for polyphenol extraction due to its highest extraction efficiency. Subsequently, extraction parameters were optimized using the response surface methodology. The highest total polyphenolic content (TPC) for DES-UAE was achieved with a solvent concentration of 70.2 %, an extraction time of 31.8 min., a temperature of 44.5 °C, and a solvent-solid ratio of 25.1 mL/g. For EtOH-UAE, the optimal solvent concentration was 81.1 %, with an extraction time of 40.4 min, a temperature of 45.5 °C, and a solvent-solid ratio of 20.4 mL/g. Additionally, the second-order extraction kinetic model was found to best describe the extraction behavior for both DES-UAE and EtOH-UAE. This study suggests that DES-UAE is a viable, sustainable, and efficient technique for obtaining polyphenols from Baccaurea macrocarpa pericarp. DES-UAE also presents a promising tool for exploring the commercial feasibility of polyphenol extraction.

Extraction of anthocyanins from purple sweet potato using supercritical carbon dioxide and conventional approaches

This research investigates the potential of supercritical carbon dioxide (SCCO2) technology as a green and sustainable approach to improve anthocyanin extraction from purple sweet potatoes (PSP). The study explored different extraction parameters (i.e., temperatures of 35–55 °C, cosolvent concentrations of 10–30 % (w/w), ethanol ratios of 30–70 % (v/v), pressures of 30–40 MPa, extraction times of 120–180 min, and solvent: sample ratios of 25:1–45:1(v/w)), to optimize the total phenolic content (TPC), anthocyanin content (ANC), and antioxidant activity (AA). Additionally, conventional extraction methods using different solvent (ethanol and methanol) mixtures, solvent: sample ratios (15:1; 45:1; 6:1 (v/w)), temperatures (35–50 °C), and extraction times (50–60 min) were performed for the extraction of phenolic compounds from PSP. The highest TPC (340 mg GAE/g dry PSP), ANC (136 mg C3G/100 g dry PSP), and AA (ABTS (7.3 mg TEV/g dry PSP), DPPH (18.2 mg TEV/g dry PSP), and FRAP (1399 mg GAE/100 g)) were achieved with 30 MPa, 35 °C and 20 % cosolvent concentration operated for 180 min. These findings underscore the use of SCCO2 extraction to obtain anthocyanin extracts that can potentially be used as a coloring agent in the food industry. The proposed SCCO2 extraction method provides an eco-friendly alternative to conventional methods for extracting phenolic compounds.

Optimization and evaluation of biologically active compound with antibacterial and anti-inflammatory properties from peony seed meal

Peony seed meal is produced annually on a large scale in China, resulting in considerable solid residue. Unfortunately, most of this residue is discarded, thereby creating environmental pressure. However, the untapped medicinal value of peony seed meal prompted us to establish an efficient methodology for optimizing the extraction and purification of total alkaloid, followed by a detailed analysis of its biological activities. A single-factor experiment and an orthogonal test were used to identify the optimal extraction parameters. Then, further optimization was performed using the response surface methodology. The extract was enriched using D-101 macroporous adsorption resin. Subsequently, silica gel column chromatography was used for separation, and high-performance liquid chromatography was used for further separation and quantification. Meanwhile, gas chromatography–mass spectrometry (GC–MS) was used to identify alkaloid components. Finally, the antibacterial and anti-inflammatory were evaluated. The optimal extraction conditions substantially improved the yield of alkaloids. A total of 15 alkaloids were separated. GC–MS analysis revealed the presence of Sophoridine, which accounted for 91.88 % of the total alkaloid content. The antibacterial activity of the total alkaloid was tested, and the lowest inhibitory concentrations of E. coli ATCC 25922 and S. aureus ATCC 29213 were 0.04516 and 0.05645 mg/mL, respectively. Additionally, total alkaloid had an inhibitory effect on ear swelling induced by xylene and toe swelling induced by egg white, reducing by 80 % and 24 %, respectively. The current research discovered the antibacterial activity of PSM alkaloid, which has important practical significance for the research and development of new plant-derived antibacterial agents.

Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits’ optimization algorithm and conducting laboratory tests

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem’s main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

A second-order kinetic model for global analysis of vibrational polariton dynamics.

The interaction between cavity photons and molecular vibrations leads to the formation of vibrational polaritons, which have demonstrated the ability to influence chemical reactivity and change material characteristics. Although ultrafast spectroscopy has been extensively applied to study vibrational polaritons, the nonlinear relationship between signal and quantum state population complicates the analysis of their kinetics. Here, we employ a second-order kinetic model and transform matrix method (TMM) to develop an effective model to capture the nonlinear relationship between the two-dimensional IR (or pump-probe) signal and excited state populations. We test this method on two types of kinetics: a sequential relaxation from the second to the first excited states of dark modes, and a Raman state relaxing into the first excited state. By globally fitting the simulated data, we demonstrate accurate extraction of relaxation rates and the ability to identify intermediate species by comparing the species spectra with theoretical ground truth, validating our method. This study demonstrates the efficacy of a second-order TMM approximation in capturing essential spectral features with up to 10% excited state population, simplifying global analysis and enabling straightforward extraction of kinetic parameters, thus empowering our methodology in understanding excited-state dynamics in polariton systems.