Accurate Correlation Research Articles

Experimental parameter study and correlation development of microchannel membrane-based absorption process for efficient thermal cooling with high compactness

The microchannel membrane-based absorber occupies a crucial position in an efficient and compact absorption refrigeration system, as it directly influences the system's cooling capacity. However, existing numerical models for describing the absorption process of this absorber often exhibit large deviations, as they are adopted from other processes. To establish highly accurate correlations, extensive experiments are performed in this study to evaluate the absorption process using H2O/LiBr as the working fluids across a wide range of operating conditions. The experimental results demonstrate that enlarging the solution flow rate, vapor pressure, and solution concentration or lowering the cooling water temperature can improve the heat and mass transfer processes significantly. By analyzing the experimental results, new correlations of Nusselt number (Nu), Sherwood number (Sh), and friction factor (f) are developed for heat/mass transfer and solution pressure drop, respectively. It is verified that these newly developed correlations significantly enhance the prediction accuracy of the overall heat transfer coefficient (U), absorption rate (J), and pressure drop (DP) by 72.39%, 78.55%, and 64.56% when compared to existing literature correlations. The exceptional accuracy achieved by these correlations contributes significantly to the design, evaluation, and optimization of efficient and compact absorbers, enabling further advancements in this field.

The Comparison of Seven Models to Simulate the Transport and Deposition of Polydisperse Particles under Favorable Conditions in a Saturated Medium

Polydisperse particles are ubiquitous in both the natural and engineered environment, and the precise prediction of the transport and capture of polydisperse particles in a saturated medium is crucial. Several efforts (Yao model, RT model, TE model, MPFJ model, NG model, MHJ model, and MMS model) were developed to obtain accurate correlation equations for the particle capture probability (single-collector removal efficiency), but the applicability of the existing models to the entire porous medium and the retention characteristic of the polydisperse particles are still unclear. In this study, sand column experiments were undertaken to investigate the transport and capture processes of the polydisperse particles in the saturated medium. The mass density was employed to quantize the effects of particle polydispersity and incorporated into the depth-dependent deposition rate. The experimental results showed that the polydisperse particles formed a hyper-exponential retention profile even under favorable conditions (no repulsion). The excellent agreement between the results obtained from the MMS model and the experimentally observed results of the breakthrough curves (BTCs), as well as the retention profiles demonstrated the validation of the MMS model, as the correlation coefficient and the standard average relative error were 0.99 and 0.005, respectively. The hyper-exponential retention profile is caused by the uneven capture of the polydisperse particles by the porous medium. This study highlights the influences of particle polydispersity on particle transport and capture in a saturated porous medium.

Revised Enskog theory for Mie fluids: Prediction of diffusion coefficients, thermal diffusion coefficients, viscosities, and thermal conductivities.

Since the 1920s, the Enskog solutions to the Boltzmann equation have provided a route to predicting the transport properties of dilute gas mixtures. At higher densities, predictions have been limited to gases of hard spheres. In this work, we present a revised Enskog theory for multicomponent mixtures of Mie fluids, where the Barker-Henderson perturbation theory is used to calculate the radial distribution function at contact. With parameters of the Mie-potentials regressed to equilibrium properties, the theory is fully predictive for transport properties. The presented framework offers a link between the Mie potential and transport properties at elevated densities, giving accurate predictions for real fluids. For mixtures of noble gases, diffusion coefficients from experiments are reproduced within ±4%. For hydrogen, the predicted self-diffusion coefficient is within 10% of experimental data up to 200 MPa and at temperatures above 171K. Binary diffusion coefficients of the CO2/CH4 mixture from simulations are reproduced within 20% at pressures up to 14.7 MPa. Except for xenon in the vicinity of the critical point, the thermal conductivity of noble gases and their mixtures is reproduced within 10% of the experimental data. For other molecules than noble gases, the temperature dependence of the thermal conductivity is under-predicted, while the density dependence appears to be correctly predicted. Predictions of the viscosity are within ±10% of the experimental data for methane, nitrogen, and argon up to 300bar, for temperatures ranging from 233 to 523K. At pressures up to 500bar and temperatures from 200 to 800K, the predictions are within ±15% of the most accurate correlation for the viscosity of air. Comparing the theory to an extensive set of measurements of thermal diffusion ratios, we find that 49% of the model predictions are within ±20% of the reported measurements. The predicted thermal diffusion factor differs by less than 15% from the simulation results of Lennard-Jones mixtures, even at densities well exceeding the critical density.

Real-time monitoring of biomass during Escherichia coli high-cell-density cultivations by in-line photon density wave spectroscopy.

An efficient monitoring and control strategy is the basis for a reliable production process. Conventional optical density (OD) measurements involve superpositions of light absorption and scattering, and the results are only given in arbitrary units. In contrast, photon density wave (PDW) spectroscopy is a dilution-free method that allows independent quantification of both effects with defined units. For the first time, PDW spectroscopy was evaluated as a novel optical process analytical technology tool for real-time monitoring of biomass formation in Escherichia coli high-cell-density fed-batch cultivations. Inline PDW measurements were compared to a commercially available inline turbidity probe and with offline measurements of OD and cell dry weight (CDW). An accurate correlation of the reduced PDW scattering coefficient µs ' with CDW was observed in the range of 5-69 g L-1 (R2 = 0.98). The growth rates calculated based on µs ' were comparable to the rates determined with all reference methods. Furthermore, quantification of the reduced PDW scattering coefficient µs ' as a function of the absorption coefficient µa allowed direct detection of unintended process trends caused by overfeeding and subsequent acetate accumulation. Inline PDW spectroscopy can contribute to more robust bioprocess monitoring and consequently improved process performance.

Experimental solubility of fexofenadine hydrochloride (antihistamine) drug in SC-CO2: Evaluation of cubic equations of state

Fexofenadine hydrochloride is an antihistamine drug which is used in the treatment of allergy symptoms, like urticaria and hay fever. In this research, the solubility of fexofenadine hydrochloride was measured in supercritical carbon dioxide (SC-CO2) at different pressures (120–270 bar) and temperatures (308.2–338.2 K), for the first time. The solubility data were in the range of 0.073 < y × 104 < 0.854. The experimental data on the solubility of fexofenadine hydrochloride were correlated by two approaches: (1) three cubic equations of state (CEoSs) with two mixing rules, and (2) nine density-based correlations with 3–6 adjustable parameters. Among CEoS, Peng-Robinson EoS with Kwak-Mansoori mixing rule, showed more accuracy, especially at higher pressures with an average absolute relative deviation percent (AARD%) of 5.48. Moreover, Garlapati and Madras (model I) with AARD% of 5.65 showed more accurate correlation compared to density-based models. Finally, total, vaporization, and solvation enthalpies of the fexofenadine hydrochloride-CO2 system were calculated to be 39.5759, 52.4635, and − 12.8876 kJ.mol−1, respectively.

Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil?

Agrometeorological models are great tools for predicting yields and improving decision-making. High-quality climatic data are essential for using these models. However, most developing countries have low-quality data with low frequency and spatial coverage. In this case, two main options are available: gathering more data in situ, which is expensive, or using gridded data, obtained from several sources. The main objective here was to evaluate the quality of two gridded climatic databases for filling gaps of real weather stations in the context of developing agrometeorological models. Therefore, a comparative analysis of gridded database and INMET data (precipitation and air temperature) was conducted using an agrometeorological model for sweet orange yield estimation. Both gridded databases had high determination and concordance coefficients for maximum and minimum temperatures. However, higher errors and lower confidence coefficients were observed for precipitation data due to their high dispersion. BR-DWGD indicated more accurate results and correlations in all scenarios evaluated in relation to NasaPower, pointing out that BR-DWGD may be better at filling gaps and providing inputs to simulate attainable yield in the Brazilian citrus belt. Nevertheless, due to the BR-DWGD database’s geographical and temporal limitations, NasaPower is still an alternative in some cases. Additionally, when using NasaPower, it is recommended to use a measured precipitation source to improve prediction quality.

Viscosity Modeling of Pure Alcohols Based on the Significant Structure Theory and a Cubic Equation of State

In this study, a previously developed model [Macías-Salinas, R.; Viscosity Modelling of Reservoir Fluids over Wide Temperature and Pressure Ranges. Chem. Eng. Trans. 2013, 32, 1573.] based on the significant structure theory (SST) in conjunction with the Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) cubic equations of state (CEoS) was modified for the accurate correlation of the viscosity of 25 pure alcohols from methanol to n-dodecanol, branched alcohols, diols, and triols at temperatures from 0 to 272 °C and pressures from 0.98 to 4860 bar, obtaining two final model versions (SST-SRK and SST-PR) valid for both gas and liquid phases in the case of methanol, ethanol, 2-propanol, and 2-methyl-1-propanol and only in liquid phase for the rest of alcohols considered here. In regard to n-decanol, n-undecanol, n-dodecanol, 1,3-propanediol, and 1,4-butanediol, they were studied at different temperatures but only at a pressure of 1 bar. The resulting model versions were correlated with 2418 dynamic viscosity experimental data, obtaining overall values of average absolute deviation (AAD), statistical bias, and absolute maximum deviation (AMD) of 1.4808, 0.0503, and 7.9217%, respectively, with the SST-SRK approach and 1.4799, 0.0476, and 7.4515%, respectively, with the SST-PR approach. The two modeling approaches were compared with the friction theory [Zéberg-Mikkelsen, C. K.; Viscosity Modeling of Associating Fluids Based on the Friction Theory: Pure Alcohols. Fluid Phase Equilib. 2002, 194–197, 1191.] (FT) and free volume theory [Allal, A.; A New Free Volume Model for Dynamic Viscosity and Density of Dense Fluids Versus Pressure and Temperature. Phys. Chem. Liq. 2001, 39, 1.] (FVT), using the same experimental viscosity database of this work under the same conditions of temperature and pressure, obtaining more accurate results with the SST approach for most of the modeled alcohols.

Yttrium-90 Activity Quantification in PET/CT–Guided Biopsy Specimens from Colorectal Hepatic Metastases Immediately after Transarterial Radioembolization Using Micro–CT and Autoradiography

PurposeTo evaluate the yttrium-90 (90Y) activity distribution in biopsy tissue samples of the treated liver to quantify the dose with higher spatial resolution than positron emission tomography (PET) for accurate investigation of correlations with microscopic biological effects and to evaluate the radiation safety of this procedure. Materials and MethodsEighty-six core biopsy specimens were obtained from 18 colorectal liver metastases (CLMs) immediately after 90Y transarterial radioembolization (TARE) with either resin or glass microspheres using real-time 90Y PET/CT guidance in 17 patients. A high-resolution micro–computed tomography (micro-CT) scanner was used to image the microspheres in part of the specimens and allow quantification of 90Y activity directly or by calibrating autoradiography (ARG) images. The mean doses to the specimens were derived from the measured specimens’ activity concentrations and from the PET/CT scan at the location of the biopsy needle tip for all cases. Staff exposures were monitored. ResultsThe mean measured 90Y activity concentration in the CLM specimens at time of infusion was 2.4 ± 4.0 MBq/mL. The biopsies revealed higher activity heterogeneity than PET. Radiation exposure to the interventional radiologists during post-TARE biopsy procedures was minimal. ConclusionsCounting the microspheres and measuring the activity in biopsy specimens obtained after TARE are safe and feasible and can be used to determine the administered activity and its distribution in the treated and biopsied liver tissue with high spatial resolution. Complementing 90Y PET/CT imaging with this approach promises to yield more accurate direct correlation of histopathological changes and absorbed dose in the examined specimens.

A review of the recent advances in the heat transfer physics in latent heat storage systems

Overwhelming research interest in developing green and renewable energy systems has fostered the innovation of novel latent heat storage systems. Transport phenomena during phase transitions significantly influence the thermal energy transfer rates and the efficiency of charging and discharging such systems. Hence, a comprehensive elucidation of heat transfer physics is essential for researchers to develop the insight, model, experiment, and analyze a thermal system. This paper reviews the heat transfer mechanisms involved in phase-changing processes in various latent heat storage system configurations. At the outset, though the phase-changing process in latent heat storage systems appears simple, a critical observation unveils the complex evolution of various coupled fluid-thermal transfer processes. An astute examination of such phenomena is essential to effectively figure out the role of geometry, orientation, process variables and their dimensionless groups, boundary conditions, material property, etc., in developing accurate models and correlations. The presence of vortical flow structures and gradients of velocity and temperature lead to local variations in heat transfer coefficients in boundaries separating the heat transfer fluid (HTF) passages and phase-changing material (PCM). Gravity influences the Rayleigh–Bénard convection phase-changing process, which offers a higher energy transfer rate than heat diffusion-dominated melting/solidification. A wide variety of test cases are surveyed and conclusively figured out the role of geometry and orientation-dependent convection in the phase-changing process. The present overview of heat transfer physics shall provide a guideline for the system designers to fine-tune HTF flow path geometrical aspects and PCM confinement for improved heat transfer response and system performance.

Precise targeting for 3D cryo-correlative light and electron microscopy volume imaging of tissues using a FinderTOP

Cryo-correlative light and electron microscopy (cryoCLEM) is a powerful strategy to high resolution imaging in the unperturbed hydrated state. In this approach fluorescence microscopy aids localizing the area of interest, and cryogenic focused ion beam/scanning electron microscopy (cryoFIB/SEM) allows preparation of thin cryo-lamellae for cryoET. However, the current method cannot be accurately applied on bulky (3D) samples such as tissues and organoids. 3D cryo-correlative imaging of large volumes is needed to close the resolution gap between cryo-light microscopy and cryoET, placing sub-nanometer observations in a larger biological context. Currently technological hurdles render 3D cryoCLEM an unexplored approach. Here we demonstrate a cryoCLEM workflow for tissues, correlating cryo-Airyscan confocal microscopy with 3D cryoFIB/SEM volume imaging. Accurate correlation is achieved by imprinting a FinderTOP pattern in the sample surface during high pressure freezing, and allows precise targeting for cryoFIB/SEM volume imaging.

Modeling the viscosity of ionic liquids using combined friction theory with perturbed hard-chain equation of state and neural network approaches

Fast and accurate prediction and correlation of thermophysical properties are always important concerns for researchers and engineers. This work is the extension of our earlier friction theory (FT)-based approach for estimating the dynamic viscosities of pure ionic liquids using a perturbed hard-chain equation of state. The model used three molecular parameters along with 5 correlation coefficients in the friction terms. The correlation coefficients were obtained using 167 data points at 0.1 MPa with an average absolute deviation (AAD) of 0.61 % and then used to predict the viscosity values at higher pressures. Using the FT-based model, 2907 experimental viscosity data for 20 pure ILs were predicted. The calculations were carried out in 273–373 K range and pressures up to 298.9 MPa for 2907 data points with an AAD of 2.63 %. The present FT-based model was not able to accurately predict the viscosities of ILs outside the temperature range used for the fit procedure. Also, comparing the precision of our FT-based model with two rough-hard-sphere theory-based models of Hosseini et al. and Hossain-Teja as well as the group-contribution method led to AADs of 2.46 % (for 2664 data points), 2.65 % (for 1605 data points), 3.25 % (for 890 data points), and 7.73 % (for 2558 data points, respectively. Viscosities of several mixtures of IL + IL type were also correlated using the present model along with some simple combining equations and a binary interaction parameter as well. The AAD of the calculated viscosities from the experimental ones was found to be 5.10 % for 960 data points examined. In addition, a neural network was trained on 3074 data points to represent the viscosity of studied pure ILs. The artificial neural network (ANN) structure utilized two layers, four input parameters, and 13 neurons for each layer. This specific structure of ANN was carefully designed to ensure an optimal performance in predicting the viscosity of ILs. The overall AAD was obtained 1.44 %.

Numerical study on unsteady heat transfer and fluid flow in a closed cylinder of reciprocating liquid hydrogen pumps

Modeling and optimization of liquid hydrogen (LH2) pumps require accurate in-cylinder heat transfer correlations. However, the applicability of existing correlations based on gas mediums to LH2 remains to be verified. In this paper, the unsteady heat transfer and fluid flow in a closed LH2 pump cylinder are numerically studied by adopting the gas spring model. The phase shifts and temperature distribution in the closed pump cylinder are investigated. LH2 is less affected by in-cylinder heat transfer and has a more uniform temperature distribution compared to nitrogen gas, while a low-temperature zone appears near the piston face at 120 rpm. Finally, the validity of Lekic's correlation in predicting the heat flux of the LH2 compression process in the closed pump cylinder is verified, and the efficiency decrement versus rotational speed is analyzed based on the correlation. This work would be useful for selecting a proper in-cylinder heat transfer model for predicting the thermodynamic process in reciprocating LH2 pumps.

Correlation between epicarp spots and the overall quality of Forsythiae Fructus

ABSTRACT Forsythiae Fructus (FF, Lianqiao in Chinese) is the dried fruit of Forsythia suspensa (Thunb.) Vahl. As one of the commonly used bulk medicinal materials, FF has been used as a heat-clearing and detoxifying traditional Chinese medicine for the treatment of infectious diseases. Epicarp spots are inherent traits of the FF. Previous reports have suggested that it might be related to the quality of FF, while the accurate correlation between them remains ambiguous. In this study, 32 groups of FF were selected from the same batch, and the information of epicarp spots was converted into spot area index (SAI). Then, the overall quality of FF was evaluated by principal component analysis (PCA) based on the content of 21 major bioactive ingredients. Subsequently, the Pearson correlation analysis was conducted to explore the correlation between SAI and the overall quality of FF. Ingredients related to SAI were identified. The results indicated that a significant negative correlation was shown between the SAI and the overall quality of FF. Six bioactive ingredients, including forsythoside B, p-coumaric acid, galuteolin, hesperidin, baicalin and kaempferol, were negatively correlated with SAI. This research will provide scientific information on the relationship between epicarp spots and the overall quality of FF and facilitate the general quality assessment of FF in a rapid and convenient way.

Exploring the accuracy of correlation coefficients representing the long-term meteorological data for projecting weather in Bahrain for sustainability

This study aims at modelling long-term weather parameters (1955 to 2020), recorded in Bahrain, and at investigating which parameters have strong correlation coefficients with such data to account for mitigation and achieve sustainability. Eight forms of fitting methods were utilized to calculate the correlation coefficient between each parameter versus each given year. These parameters (Average and Anomalies) are the average temperature, maximum temperature, minimum temperature, humidity, wind speed, dust and precipitation. The forms of correlations used are as follows: Linear regression, Quadratic regression, Cubic regression, Power regression, ab-Exponential regression, Logarithmic regression, Hyperbolic regression and Exponential regression. Among all forms of regressions, the Exponential (cubic) regression is found to have the highest correlation coefficient for such data, both Average and Anomalies data; the highest is for humidity versus year (r = 0.900, strong relation), and the least is for precipitation (r = 0.1647, poor relation) for Average data. As for the Anomalies data, the highest is for humidity versus year (r = 0.9019) and the least is for precipitation versus (r = 0.1647, no relation). The novelty of this paper lies in concluding that the exponential (cubic) regression is the most accurate correlation (among 8 correlation coefficients) to predict all the recorded long-term (65 years) weather parameters in Bahrain eight correlation. This regression fit is the most useful one in projecting the weather trend by 2050 in Bahrain to account for the future built environment to become more resilient, sustainable and tolerant with the environment due to the anticipated damage resulting from climate change. More investigation is to be made for other data set in the Gulf Cooperation Countries, and worldwide, to explore whether this Exponential (cubic) regression will still have the highest correlation coefficient among the others.

The Effects of Contrast on Correlation Perception in Scatterplots

Scatterplots are common data visualizations that can be used to communicate a range of ideas, the most intensively studied being the correlation between two variables. Despite their ubiquity, people typically do not perceive correlations between variables accurately from scatterplots, tending to underestimate the strength of the relationship displayed. Here we describe a two-experiment study in which we adjust the visual contrast of scatterplot points, and demonstrate a systematic approach to altering the bias. We find evidence that lowering the total visual contrast in a plot leads to increased bias in correlation estimates and show that decreasing the salience of points as a function of their distance from the regression line, by lowering their contrast, can facilitate more accurate correlation perception. We discuss the implications of these findings for visualization design, and provide a framework for online, reproducible, and large-sample-size (N = 150 per experiment) testing of the design parameters of data visualizations.

Modeling the solubility of ketoprofen in mono-solvents at various temperatures

Aim: To report a correlative model to calculate the solubility of ketoprofen in mono-solvents at various temperatures. Methodology: Previously reported solubility data of ketoprofen in a number of mono-solvents at various temperatures are re-analyzed using a recently developed model employing Abraham, Hansen and Catalan parameters as input values. The accuracy of the model is evaluated by computing the standard deviation of residuals (SDRs) and compared with those of previous models. Results: The new model provided very accurate correlation for the solubility of ketoprofen in the investigated mono-solvents at various temperatures. The obtained SDR is 2.20 where the SDRs of previously reported models are 1.54 to 2.56. The new model correlates whole solubility data using a single model, whereas other models correlate the solubility data in each mono-solvent using a separate set of model constants. Conclusion: The trained model provided reasonably accurate results for ketoprofen by using Abraham, Hansen and Catalan parameters of the mono-solvents as input values.

A few-shot semantic segmentation method based on adaptively mining correlation network

AbstractThe goal of few-shot semantic segmentation is to learn a segmentation model that can segment novel classes in queries when only a few annotated support examples are available. Due to large intra-class variations, the building of accurate semantic correlation remains a challenging job. Current methods typically use 4D kernels to learn the semantic correlation of feature maps. However, they still face the challenge of reducing the consumption of computation and memory while keeping the availability of correlations mined by their methods. In this paper, we propose the adaptively mining correlation network (AMCNet) to alleviate the aforementioned issues. The key points of AMCNet are the proposed adaptive separable 4D kernel and the learnable pyramid correlation module, which form the basic block for correlation encoder and provide a learnable concatenation operation over pyramid correlation tensors, respectively. Experiments on the PASCAL VOC 2012 dataset show that our AMCNet surpasses the state-of-the-art method by $0.7\%$ and $2.2\%$ on 1-shot and 5-shot segmentation scenarios, respectively.

Performance Assessment of Hybrid Fibre-Reinforced Concrete (FRC) under Low-Speed Impact: Experimental Analysis and Optimized Mixture

Fibre-reinforced concrete (FRC) has gained tremendous attention in many disciplines due to its high initial strength, favorable mechanical properties, structural lightness, and energy-absorbing properties. In this research, Barchip fibres, Forta, and Basalt are utilized to reinforce concrete under penetration effect loading to examine the energy absorption and impact strength characteristics. To determine the parameters of the percentage of fibres on the impact resistance properties, the Box–Behnken method as a subset of the response surface method (RSM) was used. A diagram of RSM is adopted to determine the optimal percentage of fibres for higher initial strength and energy absorption. Results obtained using Design-Expert software revealed an initial strength of 886.127 N and an optimal energy absorption of 4.9865 J. In addition, the calculated R2 values and normal probability graphs showed a fairy accurate correlation between the results of the experimental and mathematical approaches. Finally, this study evaluated the fracture surface, adhesion of the fibres to the concrete, and degradation modes of the fibres to pave the way for optimal utilization of these hybrid FRCs.

CFD-Based Prediction of Combustion Dynamics and Nonlinear Flame Transfer Functions for a Swirl-Stabilized High-Pressure Combustor

Thermoacoustic instabilities in gasturbine combustor systems can be predicted in the design phase with a thermoacoustic network model. In this model, the coupling between acoustic pressure fluctuations and the combustion rate is described by the Flame Transfer Function. The present paper introduces a new, efficient, and robust method for deriving the FTF from CFD predictions by means of a discrete multi-frequency sinusoidal fuel flow excitation method. The CFD-based FTF result compares well with experimental data for the time delay, but for the gain, only up to 400 Hz. Above 400 Hz, the CFD result reveals a smooth low-amplitude gain, which is not found in the measured data. A novel, accurate continuous correlation function for the FTF gain is computed based on the results for discrete frequencies. When this is implemented into a 1D acoustic network model, the stability map shows, below 600 Hz, two eigenfrequencies, by both the experiment and CFD-based FTF, that are identical. The CFD-based FTF correctly predicts marginal activity at the highest eigenfrequency, while the experimentally based FTF suggests an unstable operation. The unstable operation is not observed in the experiments. This suggests that the CFD-based FTF is also correct for high frequencies.

Non-invasive Monitoring of Soft-tissue Sarcoma Response to Neoadjuvant Radiotherapy by Quantification of Circulating Tumor DNA (ctDNA) and Multiparametric MRI (mpMRI)

Introduction: Soft-tissue sarcomas (STS) are a heterogenous group of rare, yet malignant tumors. Wide resection remains the cornerstone of treatment for localized STS. Current S3 guidelines recommend neoadjuvant radiotherapy for primary, high-grade tumors; however, therapy response for different histological subtypes and gradings has not been systematically investigated. This ongoing prospective study evaluates the response of various STS during the course of neoadjuvant radiotherapy using innovative, non-invasive methods. Through quantification of ctDNA, tumor activity and thus therapy response will be evaluated in correlation with imaging data. Materials and Methods: Liquid biopsies: Detection of circulating tumor DNA (ctDNA) in patient blood samples through targeted next-generation sequencing (NGS). Patient-specific exome sequencing allows the detection of individual structural variants (SVs) and point mutations. Circulating free DNA (cfDNA) is isolated from peritherapeutically collected patient plasma samples and ctDNA quantified therein. Bioinformatic analysis is performed using the MIRACUM Pipeline. Multiparametric MRI (mpMRI): Combination of conventional MRI sequences with diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE). Multiparametric MRI is performed before, during, and after neoadjuvant radiotherapy. Accurate co-registration and correlation of mpMRI with the macroscopic, histologic, and immunohistochemical resected specimen allow an analysis of morphological changes during therapy. Results: Quantification of ctDNA mirrors the clinical course and tumor activity. Furthermore, we present imaging markers that allow the characterization of tumor masses for heterogenous STS and the evaluation of therapy response. Conclusion: Liquid biopsies can be used to detect the primary tumor and metastases, and to monitor the course of therapy. Quantification of ctDNA in combination with tumor mass characterization through co-registration of mpMRI and histopathology enables the evaluation of STS response to neoadjuvant radiotherapy. The participation and support of a wider group of oncologic surgeons are needed to validate these findings on a larger patient cohort to create future tumor- and patient-specific therapy concepts.