Actual Response Research Articles

Abstract 6789: Development of an in vitro gastric cancer 3D co-culture platform using a microphysiological system (MPS) to improve outcomes in oncology clinical trials

Abstract Introduction: Despite significant advancements in scientific and diagnostic technologies, clinical trials for cancer often fail due to a lack of efficacy. Access to a platform that accurately predicts patient response is crucial to increase the success rate of clinical studies, and to minimize unnecessary exposure to ineffective and potentially harmful drug testing for patients. Because of the complexity of the disease, it would be beneficial to deploy patient samples for drug testing in a side-by-side comparison during the clinical trial. An accurate prediction of patient response earlier in the clinical study would aid decision making to determine the appropriate duration of treatment for each patient. Methods: We developed a 3D human cell co-culture model using our proprietary MPS technology, the Curio-spheroid Curiochip. Using patient samples isolated and expanded from either ascites fluids or biopsies to recapitulate the clinical phenotypes of advanced gastric cancer, we quantified the migratory properties of the patient cancer cells (i.e., the unique metastatic potential), and measured the level of secretion of VEGF. Somatic mutational analyses using NGS and RNAseq were performed for subtyping and clustering the samples. The patient cancer cells were expanded within 2 weeks post-collection, and used to form a spheroid that was loaded into a hydrogel embedded within the Curio-Spheroid, enabling the patient spheroids to interact through the hydrogel with neighboring (healthy) human endothelial cells. The patient spheroids induced aggressive vasculogenesis in the co-culture model, which was subdued by the anti-VEGFR2 antibody Ramucirumab. The platform was retrospectively validated with the Standard-of-Care (SOC) treatments that the patients received. Results: The patient cell testing took 3-4 weeks from sample collection to imaging. The patient cells preserved their migratory capacity, pro-angiogenic potential and drug responses in the vascularized model. The dose responses to SOC in the platform were consistent with the actual patient response assessments based on RECIST criteria, supporting the potential to use this platform to accurately predict patient clinical responses. Conclusions: The Curio-spheroid 3D co-culture model of vascularized gastric cancer provides a dynamic microenvironment to preserve the phenotypes of the patient cells and cell-cell interactions that are known to influence drug responses. This platform is compatible with high-throughput screening (HTS) for target or drug discovery with a good assay window. The ability to accurately recapitulate patient responses to SOC in the clinic make the Curio-Spheroid platform a promising method to test patient samples and measure drug responses of patients in real time during ongoing clinical trials. Citation Format: Yeongmin Choi, Sumin Kim, Youngsook Song, Yangji Kim, Jin Hawn Jang, Suryong Kim, Jihye Baek, Delaney Donnelly, Bushra Rajiput, Byungjun Lee, Woo Sun Kwon, Tsung-Li Liu, Kyusuk Baek, Sanghee Yoo, Sun Young Rha. Development of an in vitro gastric cancer 3D co-culture platform using a microphysiological system (MPS) to improve outcomes in oncology clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6789.

Study on the permanent deformation of asphalt mixtures based on the modified Burgers model

Based on the confined test results and the viscoelastic constitutive model of asphalt mixtures, a three-dimensional finite element model of an asphalt pavement was established to study the effect of different working conditions on asphalt pavement. With the actual pavement structure response, a comparative analysis was conducted to assess the stress and strain experienced by the asphalt pavement under various working conditions. The results show that the asphalt pavement structure model can reflect the effect of different working conditions on asphalt pavement; the transverse stress and strain of asphalt pavement are most significantly affected by the working conditions during cornering, and the position of the maximum value is concentrated on the outer edge line of the wheel and a distance away from it; however, the longitudinal tensile stress and strain of the asphalt pavement are most significantly impacted by the working conditions of driving and braking, and the position of the maximum value is mainly distributed in the center line of the single wheel. The research results can provide a reference for the design of asphalt pavement structures.

Establishment of a patient-specific avatar organoid model derived from EUS-guided fine-needle biopsy for timely clinical application in pancreatic ductal adenocarcinoma (with video)

Background and aimsPancreatic ductal adenocarcinoma (PDAC) has the worst survival rate among tumors. At the time of diagnosis, over 80 percent of PDACs are considered surgically unresectable, and there is an unmet need for treatment options in these inoperable PDACs. The study aimed to establish a patient-derived organoid (PDO) platform from endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) collected at diagnosis and to determine its clinical applicability for the timely treatment of unresectable PDAC. MethodsPatients with suspected PDAC were prospectively enrolled at the Samsung Medical Center from 2015 to 2019. PDAC tissues were acquired by EUS-FNB to establish PDAC PDOs, which were comprehensively analyzed for histology, genomic sequencing, and high-throughput screening (HTS) drug sensitivity test. ResultsPDAC PDOs were established with a success rate of 83.2% (94/113). It took approximately 3 weeks from acquiring minimal EUS-FNB specimens to generating sufficient PDAC PDOs for the simultaneous analysis of HTS drug sensitivity test and genomic analysis. The high concordance between PDAC tissues and matched PDOs was confirmed, and whole-exome sequencing revealed the increased detection of genetic alterations in PDOs, compared with in EUS-FNB tissues. The HTS drug sensitivity test showed the clinical correlation between the ex vivo PDO response and the actual chemotherapeutic response of the study patients in the real world (13 out of 15 cases). In addition, whole-transcriptome sequencing identified candidate genes associated with nab-paclitaxel resistance, such as ITGB7, ANPEP, and ST3GAL1. ConclusionsThis PDAC PDO platform allows several therapeutic drugs to be tested within a short time window and opens the possibility for timely personalized medicine as a “Patient Avatar Model” in clinical practice.

Defining the calibration process for building thermal performance simulation during the warm season: a case study of a single-family log house

ABSTRACT The study aimed to develop a holistic, evidence-based calibration strategy and define a hybrid step-by-step methodology for building thermal simulations during the warm season. The methodology was demonstrated using the thermal performance of a naturally ventilated single-family log house in a temperate climate. The calibration process was supported by on-site measurements of indoor thermal conditions and occupant behavioural reports between April and July. Hybrid calibration methodology combines statistical indicators, such as NMBE, CV(RMSE), and R 2, and a graphical evaluation of the deviation between the simulated and measured values. After 28 proposed calibration steps, a final calibrated model predicted the actual thermal response of the log house with an accuracy of ±1°C at 71.6% or ±2°C at 98.4% of the considered time. The results showed that occupant-related information, material properties and infiltration rates are crucial to improving the reliability of building thermal models. Moreover, in the calibration process of log houses, appropriate determination of building envelope materials’ thermal conductivity, wood's solar absorptivity, and airtightness showed high importance. The study highlights the importance of calibration for reliable building thermal models, specifically in overheating prevention simulations.

Thermo-Mechanical analysis of laminated Doubly-Curved Shells: Higher order Equivalent Layer-Wise formulation

The paper presents a refined two-dimensional formulation for the thermo-mechanical analysis of laminated doubly-curved shell structures under thermodynamic equilibrium conditions. Both the kinematic configuration variables and the temperature variation with respect to the natural equilibrium state are described with a generalized formulation, following the Equivalent Layer-Wise (ELW) approach employing higher order polynomials, along with some proper zigzag functions. The governing equations are derived from the stationary configuration of the Helmholtz free energy of the system, and a semi-analytical solution is found. In the post-processing phase, the Fourier-based Generalized Differential Quadrature (F-GDQ) is applied to recover the actual three-dimensional response of the doubly-curved shell solid, and very accurate results are obtained for the quantities of both mechanical and heat conduction problems. In addition, the integrals occurring in the theory are performed numerically with the Taylor-based Generalized Integral Quadrature (GTIQ), showing a high level of accuracy with a reduced number of sample points. The model is validated in some case studies, where the accuracy of the model is shown, and the numerical predictions are successfully compared with those ones from refined three-dimensional numerical simulations. After that, an extensive set of parametric investigations is reported, pointing out the effects of the panel curvature, lamination schemes and different levels of coupling on the thermo-mechanical structural behavior of the investigated panels.

Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods

Temperature sensitivity (Q10) of ecosystem respiration (Re) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q10 has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q10 for Re in the Heihe River Basin, northwest China. Specifically, these methods are Q10 estimated from the long-term method (Q10_long), short-term method (Q10_short), and the low-frequency (Q10_lf) and high-frequency (Q10_hf) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q10_lf and Q10_long are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q10_short and Q10_hf reflect the actual response of respiration to temperature. 3) Overall, Q10_long has a larger variability (1.7 ± 0.3) across different biomes, whereas Q10_short and Q10_hf show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q10 can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q10 value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.

Post-earthquake fire resistance of self-centering concrete beam-column joint: An experimental investigation

Fire is an accidental disaster resulting in disastrous consequences for building. Post-earthquake fire is a secondary disaster induced by earthquake, triggering off larger destruction in a higher probability of occurrence. The potential severity of consequences to structures makes fire and post-earthquake fire safety imperative in the design of any new structures. Self-centering concrete joints are introduced as a promoting alternative to traditional reinforced concrete (RC) joints. They always exhibit excellent seismic performance and self-centering capacity, available in high-seismic zone. However, the current lack of experimental data on fire response of self-centering concrete joints means that they remain an open issue in current design codes. In this paper, full-scale quasi-static cyclic test and fire tests were conducted on two self-centering concrete joints connected with bolted angles and posttensioned (PT) tendons. The experiments aimed to explore the performance of self-centering joint in fire situations, and the impact of post-earthquake damages on fire response of self-centering joint. The deformation patterns and thermal responses of self-centering joints including temperature distribution, prestress force of PT tendons, displacements at beam ends and duration were recorded and analyzed. The results show that both self-centering joints exhibited satisfactory fire resistance with endurance over 170 min. Generally, the bolted angles and PT tendons in self-centering concrete joints showed acceptable working performance in fire. The findings unveil the actual response of self-centering concrete joints during the fire and post-earthquake fire conditions, providing experimental data for further numerical simulation and analysis. It can be used to calibrate the design approach for self-centering joints under fire and seismic-induced fire exposure.

A condition fitting method of temperature field for tunnel structure based on test measurements

Accurate temperature distribution of the tunnel lining under a fire scenario is crucial for conducting reliable thermal–mechanical investigations, as it directly impacts thermal stress and property degradation. In this regard, achieving consistency between simulation and actual response is imperative. However, the precision of most simulated temperature fields is hindered by a phenomenon known as the 'temperature platform', which occurs around the vaporization point of water. To this end, a Condition Fitting Method (CFM) is proposed in this paper. In CFM, optimal parameters are adjusted based on local historical measurements, enabling the acquisition of a detailed temperature field for the entire tunnel structure in real-time or even forward time step. Based on a preliminary segment fire test, the implementation of CFM successfully captures the temporal characteristics of the temperature platform on certain survey points. The results of both the current and forward time steps obtained from CFM demonstrate close agreement with the reference measurements obtained from the fire test, indicating consistency throughout the entire process. The maximum average error of the calculation result on survey points is 50.4 °C while the value for prediction result is 65.3 °C. For further investigation, a parametric study is conducted to identify the most crucial parameters. Additionally, numerical verification is performed to assess the feasibility of the proposed method and to compare the efficiency of different optimal algorithms. The results indicate that the proposed method has overcome the drawback of conventional simulation method that the temperature deviate from actual values at early stage. This achievement ensures that the proposed method provides a reliable temperature field for further investigations.

Research on vibration response of 550 kV GIL isolation compensation unit against earthquake loading

To analyze the vibration response of Gas Insulated Transmission Lines (GIL) of urban power grid under different seismic intensity levels, based on the actual structure of the standard isolation compensation unit (isolation unit and compensation unit) of 550kV GIL, A fine 3D model of isolation compensation unit segment of 550 kV GIL was established, and the actual vibration response of each standard unit segment of GIL under different seismic conditions was studied by response spectrum analysis. This study clarified the action mechanism and law of GIL vibration characteristics under seismic conditions of urban power grids, provided the relative support for the research of shock absorption of GIL, and was great significance to the actual project of GIL.

Retrieval of Tree Height Percentiles over Rugged Mountain Areas via Target Response Waveform of Satellite Lidar

The retrieval of tree height percentiles from satellite lidar waveforms observed over mountainous areas is greatly challenging due to the broadening and overlapping of the ground return and vegetation return. To accurately represent the shape distributions of the vegetation and ground returns, the target response waveform (TRW) is resolved using a Richardson–Lucy deconvolution algorithm with adaptive iteration. Meanwhile, the ground return is identified as the TRW component within a 4.6 m ground signal extent above the end point of the TRW. Based on the cumulative TRW distribution, the height metrics of the energy percentiles of 25%, 50%, 75%, and 95% are determined using their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs closely resemble the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias, and root-mean-square error (RMSE) taking values of 0.92, 0.0813, and 0.0016, respectively. In addition, the accuracies of the derived height percentiles from the proposed algorithm are greatly improved compared with the conventional Gaussian decomposition method and the slope-adaptive waveform metrics method. The mean bias and RMSE values decrease by the mean values of 1.68 m and 2.32 m and 1.96 m and 2.72 m, respectively. This demonstrates that the proposed algorithm can eliminate the broadening and overlapping of the ground return and vegetation return and presents good potential in the extraction of forest structure parameters over rugged mountainous areas.

Investigation of offshore landslides impact on bucket foundations using a coupled SPH–FEM method

BackgroundAs the exploitation of marine resources intensifies, the impact of submarine landslides on underwater structures has become a significant issue. Existing research primarily focuses on the impact on pipelines, often neglecting the actual deformation and mechanical response of underwater structures under impact loads in numerical simulations, thus complicating the evaluation of the reliability of these engineering structures in extreme conditions. Moreover, the dynamic response of bucket foundations, a common form of underwater base, under the effect of submarine landslide impacts remains unclear.MethodsTo address this knowledge gap, we have developed a fluid-structure coupling system that employs the coupled Smoothed Particle Hydrodynamics (SPH)-Finite Element Method (FEM) to investigate a single impact process and analyze the displacement response of bucket foundations within a water-offshore landslide-bucket foundationsubgrade context. The accuracy of this developed method has been systematically verified through comparisons with previous experimental and numerical results.ResultsDuring a submarine landslide impact event, the impact force demonstrates a distinct decrease followed by stabilization, and the displacement response of the bucket foundation exhibits a rebound effect after reaching its maximum value. Furthermore, we conducted an extensive analysis of different impact angles for underwater data centers equipped with multi-bucket foundations. Our study revealed that group-bucket foundations experience a combined translation-turnover failure when subjected to submarine landslide impacts, and the most unfavorable scenario for such impact is identified. The research introduces a novel numerical simulation approach for investigating the impact of submarine landslides on complex underwater structures.

Dynamic cyclic loading tests for investigating the influence of loading rate and axial force on the lateral response of RC columns

AbstractThe material properties of steel and concrete vary depending on the loading rate to which they are subjected. To realistically evaluate the seismic performance of reinforced concrete (RC) columns, it is necessary to simulate the loading rate that structures will experience during seismic events as accurately as possible. Real‐time dynamic testing is preferred over quasi‐static testing for this purpose. On the other hand, the seismic performance of RC columns also depends on the axial force applied to the column. However, due to the large axial stiffness of RC columns, accurately controlling the axial force during the test, particularly in real‐time testing, is challenging. Consequently, only a few component‐level dynamic tests have been conducted for RC columns. This study aims to explore the influence of axial force and loading rate on the seismic performance of RC columns. To this end, both slow and fast cyclic loading tests were conducted on square cross‐section RC columns subjected to four different axial forces. By utilizing a robust actuator control method along with a specially designed load transfer element, the axial force was successfully controlled, while the lateral displacements were imposed on the columns in real‐time. The results include lateral strengths, postyield responses, damage patterns, rebar strains, and their rates, all of which constitute unique experimental data that can contribute to a deeper understanding of the actual seismic response of RC columns.

Contrast response function estimation with nonparametric Bayesian active learning.

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential.

Bridging Time and Frequency for Low-Order, Model-Based PID Control

In the context of traditional PID model-based control, where sinusoidal or PRBS excitation on a real plant is not advisable or desirable, this paper outlines a method to obtain some relevant information by merely comparing model-based and actual time-domain reaction curves. This comparative analysis yields valuable information that can be subsequently translated into a practical estimate of uncertainty in the frequency domain. This, in turn, will enable the control designer to gain some insight into imposing realistic boundaries for closed-loop response specifications, which will ultimately be consistent with what can be expected from the actual plant response. The conditions for robust stability and robust performance are outlined and further assessed through academic simulation experiments.

Response load prediction of demand response users based on parallel CNN

YAs China advances its transition towards green and low-carbon energy, the proportion of new energy generation in the power grid is gradually increasing, leading to a significant rise in the demand for power resource scheduling. However, due to the scarcity of historical load response data from users, it is challenging to effectively predict user-responsive loads. To address this issue, this study proposes a method of augmenting historical load response data in a weakly supervised manner. Taking into account the unique circumstances of high-voltage users, a sparse CNN for anomaly detection is introduced, along with a multi-branch parallel CNN model capable of weighted output of prediction results from both global and local perspectives. Subsequently, effective iterative training of the model is performed using the EM algorithm. Ultimately, accurate prediction of user-responsive loads is achieved. Based on historical 96-point load data and load response data from high-voltage users in a specific city in China, the predicted results are compared with actual load response data, validating the rationality and accuracy of this method in predicting user-responsive loads.

Partial exploitation of host egg patches resulting from a high rejection rate of healthy hosts cautions the mechanistic use of functional response models

Abstract The functional response of parasitoids is a main component of models that describe host–parasitoid interactions. When the functional response assumed in a model (e.g., a population model) is inaccurate, predictions by the model based on the functional response also become unreliable. This study reports that conventional functional response models may commonly fail to capture the actual functional response of parasitoids and discuss its implications. To assist the discussion, an experiment was conducted to describe interactions between the pupal parasitoid Dirhinus giffardii (Hymenoptera: Chalcididae) and its host Bactrocera dorsalis (Diptera: Tephritidae) in the laboratory. Results showed that the parasitoids typically interacted with all hosts in the environment but parasitised a small fraction of them (e.g., most encountered hosts were rejected), which deviates from the assumption of mechanistic functional response models. An increasing number of published studies on functional response also suggest (although circumstantially) the rejection of unparasitised hosts is common, suggesting that this behaviour is widespread among parasitoid species. By using the experimental results, the study discusses why existing hypotheses are insufficient for the observed behaviour and the implications of this behaviour within the realm of theories surrounding host–parasitoid interactions.

Structural Dynamic Response Reconstruction Based on Recurrent Neural Network–Aided Kalman Filter

In structural health monitoring (SHM), an important issue is the limited availability of measurement data due to the spatial sparsity of sensors installed on the structure. These measurements are insufficient to accurately depict the actual dynamic behavior and response of the structure. Therefore, full‐field (i.e., every degree of freedom) structural response reconstruction based on sparse measured data has drawn a lot of attention in recent years. Kalman filter (KF) is an effective technology for response reconstruction (also known as state estimation), providing an optimal solution for systems that can be well‐represented by a fully known Gaussian linear state‐space model. This implies that both the process noise and measurement noise follow known zero‐mean Gaussian distribution, which is impractical in many civil engineering applications considering the unavoidable modeling errors and variations of environmental conditions. To address this challenge, a data‐physics hybrid‐driven method, i.e., KalmanNet, is proposed in this study for response reconstruction of partially known systems. By integrating a recurrent neural network (RNN) module into the KF framework, KalmanNet can efficiently learn and compute the Kalman gain using available monitoring data, without any Gaussian assumptions or explicit noise covariance specifications (e.g., covariance matrices of process and measurement noise). Both numerical and experimental investigations are conducted to validate this method. The results demonstrate that under the influence of non‐Gaussian noise and modeling errors, KalmanNet can effectively and accurately reconstruct the structural response from sparse measurements in real‐time and has higher accuracy and robustness compared to traditional KF even with optimal parameter settings.

Bridge finite element model updating using stochastic vehicle-induced static response monitoring data

Finite element model updating (FEMU) is a frequent method for minimizing the differences between actual and predicted bridge structural behavior. However, static model updating for long-span bridges usually requires load tests to obtain the response under known static loads, which is costly due to long traffic interruptions. To offset this disadvantage, a static FEMU method based on long-term monitoring data is proposed to update the model in real-time by taking stochastic traffic loads with certain statistical characteristics as known static loads. First, the theoretical relationship between stochastic traffic flow statistical characteristics, static response statistical characteristics, and structural stiffness parameters is established. An objective function based on the probability characteristics of the long-term vehicle-induced response is constructed. Then, the FEMU process based on monitoring data is introduced, including the acquisition of the finite element model vehicle-induced response, the extraction of the actual vehicle-induced response based on the monitoring data, the selection of model updating parameters, and the application of the global-local parameter updating optimization method. Finally, the effectiveness of the proposed method is verified by applying numerical examples and real bridge examples to a long-span suspension bridge. It can be concluded that the proposed method can achieve FEMU based on long-term monitoring data, avoiding the inconvenience and large cost caused by load tests. The accuracy and efficiency of the model updating can meet the engineering requirements well through comparison with the result of the traditional load test method.

Response surface optimization of thermo-sonication conditions and taro mucilage concentrations for the preparation of soy yogurt

The present study embarked on developing soy yogurt utilizing soybean and taro mucilage. Box-Behnken Design (BBD) of Response Surface Methodology (RSM) was executed to optimize the ingredient and thermo-sonication conditions such as temperature (A: 40–60 °C), time (B: 5–15 min), and mucilage concentrations (0.1–0.3 g/100 mL) on the responses such as pH, acidity, syneresis, water holding capacity, viscosity, texture profile (firmness, adhesiveness, gumminess), and overall acceptability. The analyses showed that the soy yogurts have a titratable acidity (0.73 ± 0.43 % - 0.85 ± 0.58 %), pH (4.7 ± 0.40–4.93 ± 0.68), total soluble solid (26 ± 3.2 °Brix - 42.2 ± 4 °Brix), syneresis (26 ± 3.2 % - 39.98 ± 3.7 %), water holding capacity (36.33 ± 4.91 % - 55.7 ± 2.27 %), viscosity (1367.3 ± 2.98 mPa s - 2306.13 ± 3.21 mPa s), whitening index (76.1 ± 3.22–82.33 ± 2.18), firmness (14 ± 5 g - 26 ± 2 g), cohesiveness (13 ± 3.74 g s - 23.2 ± 2.88 g s), gumminess (0.7 ± 0.01 g - 1.8 ± 0.03 g), and overall acceptability (3.96 ± 0.16–4.54 ± 0.32). From the variance analysis, the R2 of eleven response variables is more than 0.85, which indicates the model explained a high proportion of variability. At 60 °C thermo-sonication temperature, 13 min thermo-sonication time, and 0.3 g/100 mL mucilage concentration, the optimal conditions recorded for 0.780 ± 0.21 % (titratable acidity), 4.701 ± 0.51 (pH), 10.986 ± 0.76 °Brix (total soluble solid), 27.886 ± 0.49 % (syneresis), 54.687 ± 0.38 % (water holding capacity), 1810.541 ± 5.75 mPa s (viscosity), 23.126 ± 0.45 (firmness), 16.945 ± 0.21 (cohesiveness), 1.485 ± 0.48 (gumminess), 81.764 ± 0.32 (whitening index), and 4.365 ± 0.42 (overall acceptability). Furthermore, the developed models' actual response and predicted values correlated with each other as the Residual Standard Error (RSE) values were ≤5 %. This suggests that our model can reliably guide the selection of optimal parameters for any combination of factors. Ultimately, the insights garnered from this study hold promise for the large-scale production of taro mucilage-based soy yogurt, offering a versatile and nutritious addition to the repertoire of dairy alternatives.

Exact Solution of the Raman Response Function of Chalcogenide Fiber and Its Influence on the Mid-Infrared Supercontinuum

We fabricated a core-cladding Ge–Sb–Se glass fiber with a Ge12.5Sb15Se72.5 core and Ge15Sb10Se75 cladding, achieved a supercontinuum spectrum spanning from 2 μm to 9 μm by pumping the Ge–Sb–Se fiber with a core diameter of 11 μm using a femtosecond laser pump at 3.8 μm, and numerically simulated the supercontinuum generation using the generalized nonlinear Schrödinger equation. In particular, we investigate the effect of the different Raman response functions that were calculated using the traditional single Lorentzian model and a multiple vibrational mode model on the evolution of the supercontinuum by comparing the supercontinua obtained from simulation and experimental results. We demonstrate that the Raman response function generated by the multiple vibrational mode model captures the actual response behavior of the material, and the supercontinuum generated using this model has more accuracy. To the best of our knowledge, this is the first reported study on supercontinuum generation in Ge–Sb–Se fiber utilizing a Raman response function calculated using the multiple vibrational mode model. This significant advancement enables more accurate simulation of supercontinuum generation in fibers with a multi-peaked structured Raman gain spectrum and holds great potential for optimizing the performance of various mid-infrared supercontinuum sources.