Development Of Computational Models Research Articles

The effectiveness of using interactive computer models when teaching physics in modern school

Relevance. This paper concerns the use of interactive methods and technologies in the distance educational process when studying physics in secondary school.Purpose. The purpose of the paper is to identify the possibilities of interactive computer models and to develop didactic and practical bases of the methodology of their effective application in teaching physics to secondary school students.Methodology. The following pedagogical methods of literature analysis and statistical processing of the results are used in the work. Conversation, observation, experience, practical work with the predominance of the heuristic nature of the cognitive activity of students are the main methods of organising work with students.Results. Special attention is paid to the forms and types of educational activities in information networks, as well as problems that arise during distance learning. The article reveals the need to develop methodological foundations for the use of interactive computer models in teaching a physics course in secondary school. The paper considers the main provisions of the methodology for using interactive computer models in the educational process, methodological and didactic requirements for its organisation. This article discusses the prospects of using interactive methods and technologies in the distance learning process. This paper unlocks the potential of interactive computer models and justifies the expediency of their use in the process of teaching physics in secondary school.Conclusions. The article substantiates the importance of the relationship between methodological and technological approaches to the development of interactive educational computer models. The paper shows the effectiveness of interactive computer models in physics for self-training of students to increase their level of practical and applicable skills.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

This paper delves into the practical application of K-Nearest Neighbors (KNN), Kernel Ridge Regression (KRR), and Lasso Regression for the prediction of viscosity of ionic liquids in a dataset characterized by categorical variables (Cation, Anion) and numeric variables (T(K), xIL(mol%)). Indeed, mole percentage of ionic liquids and temperature were considered as inputs for the models. The models' effectiveness is rigorously assessed, with K-Nearest Neighbors notably exhibiting exceptional predictive performance. To enhance model accuracy, Tabu Search is employed as an optimization tool for hyperparameter tuning. Numeric results showcase KNN's superiority, supported by a remarkable R2 test score of 0.91628 and the lowest RMSE among the models. Tabu Search optimization further refines model performance, emphasizing the critical role of hyperparameter tuning in achieving robust regression models in predicting the viscosity of ionic liquid-water mixtures. This study contributes valuable insights into the optimization process, demonstrating tangible improvements in predictive accuracy for viscosity predictions in similar contexts.

Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures

Background and objectiveThe pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. MethodsThe Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. ResultsSimulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. ConclusionsThis study introduces a credible computational model, providing valuable biomechanical insights into the pelvis’ response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

RESEARCH OF THE STATIONARY PHENOMENON OF HEAT TRANSFER IN A SPHERICAL CONDENSED MEDIA

A method for investigating approximate analytical solutions of boundary value problems of singularly perturbed equations of stationary heat transfer processes in a spherical condensed medium, close to solving the boundary value problem for an undisturbed equation, is presented. The main task in which the structure of the article is defined is the development of mathematical and computer models of the stationary phenomenon of heat transfer in flat condensed media and a model of diffusion-reactive energy transfer depending on the thermal conductivity of materials.

NuScale-like SMR Model Development and Applied Safety Analyses with the Code Chain Serpent-DYN3D-ATHLET

NuScale is an integral pressurized water reactor (iPWR) operated with light water driven by natural circulation through two helical coil steam generators (HCSGs). This work reports the safety analyses of a boron dilution and steam line break accidental sequences in a developed plant computational model based on the specifications from the Final Safety Analysis Report (FSAR). Multi-physics calculations are performed with the code chain Serpent-DYN3D-ATHLET. A state-of-the-art multi-dimensional vessel topology is developed with ATHLET for the accurate representation of flow and temperature fields, as well as spatial core power distribution. The static calculation results show agreement with the reference values from the FSAR. The boron dilution sequence shows a homogeneous core power excursion by the boron feedback, and the reactor is tripped upon “high pressurizer pressure” signal. During the steam line break sequence, the affected HCSG depressurizes rapidly and the reactor is tripped upon “low main steam pressure” signal. None of the transients violate safety margins. The adopted 3-D vessel modelling approach and applied multi-physics calculations are able to capture both transient physics within the reactor domain, and conclude that symmetric arrangement of the HCSG tubes enhance coolant mixing and prevent a heterogeneous core power excursion.

Development of advanced hybrid computational model for description of molecular separation in liquid phase via polymeric membranes

Separation of molecules using membranes has been computationally studied in this work for understanding the separation process and also assessment of integrating machine learning (ML) techniques to computational fluid dynamics (CFD) techniques. We initially performed CFD computations to simulate mass transfer in a membrane-based molecular separation system, and then exported the mass transfer data for training ML models. Indeed, a hybrid model was developed and employed for membrane process in separating a solute from a solution. We have modeled a data set containing more than 14 thousand data rows, which has two inputs r (m) and z (m) and single response, i.e., C (mol/m3) that is solute’ content in the solution. Decision tree-based models are used in this modeling. Simple Decision Tree (DT), Boosted Decision Tree (BDT) that is DT alongside AdaBoost, and Random Forest (RF) are the selected models for this research. The ML techniques were further optimized via a novel HHO algorithm. Final assessments show the R2 scores of 0.9991, 0.9874, and 0.9997 respectively for BDT, DT, and RF models. Based on this result and other analysis, RF is selected as the best method in this study for simulation of membrane process. This model has an RMSE error rate of 2.22 × 103.

The Specifics of PTC Thermistor Applications for Limiting Surge Currents

This paper investigates the possibilities of limiting emergency short current pulses by using materials with a positive temperature coefficient. A computational model and method for simulating this process were proposed. Barium titanate was considered a promising material for current-limiting devices due to its low cost, ease of manufacturing and autonomy. The current-limiting properties of a PTC thermistor device were estimated in relation to a significant pulse current overload (10 ms). The value of taking into account the varistor effect when modeling energy dissipation by a PTC thermistor is experimentally demonstrated. The results of the experiment were used in the development of computer models of the current-limiting properties of PTC thermistors. A method for taking into account the varistor effect in the developed models is proposed. It is noted that in order to increase the accuracy of the calculation, it is important to take into account the heat transfer between the PTC thermistor and its contact system. It is also shown that this heat transfer is capable of creating a significant longitudinal temperature gradient inside the PTC thermistor, which creates a high level of thermal stress and leads to mechanical failure. Destruction of this type has been observed during experiments. This research was carried out within the state assignment FSEG-2023-0012.

DLP: Duplex Link Prediction Via Subspace Segmentation for Predicting drug-MiRNA Associations.

The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease progression has been underscored in recent studies, elevating them as potential therapeutic targets. This emphasizes the need for the development of sophisticated computational models that can effectively identify promising drug targets, such as miRNAs. Herein, we present a novel method, termed Duplex Link Prediction (DLP), rooted in subspace segmentation, to pinpoint potential miRNA targets. Our approach initiates with the application of the Network Enhancement (NE) algorithm to refine the similarity metric between miRNAs. Thereafter, we construct two matrices by pre-loading the association matrix from both the drug and miRNA perspectives, employing the K Nearest Neighbors (KNN) technique. The DLSR algorithm is then applied to predict potential associations. The final predicted association scores are ascertained through the weighted mean of the two matrices. Our empirical findings suggest that the DLP algorithm outperforms current methodologies in the realm of identifying potential miRNA drug targets. Case study validations further reinforce the real-world applicability and effectiveness of our proposed method. The code of DLP is freely available at https://github.com/kaizheng-academic/DLP.

ВЫЧИСЛИТЕЛЬНЫЕ МОДЕЛИ И ТЕХНОЛОГИИ ДЛЯ ПОЛУЧЕНИЯ ИНФОРМАЦИОННЫХ ОЦЕНОК ИЗМЕНЕНИЙ СОСТОЯНИЙ НЕОПРЕДЕЛЕННОСТИ ПАРАМЕТРА

The paper considers the issues of further development of computational models for obtaining information estimates of changes in the uncertainty states of various parameters. The proposed models are based on the analysis of changes in the values of entropy potentials and the corresponding basic values used to characterize the states of uncertainty of parameters at various stages. Such models are in demand, for example, for the organization of operational monitoring and management of uncertainty states of parameters in software and parametric control systems, where the basic values can change very significantly. The estimates obtained are objective and unified based on the information generated by the change in these states. The proposed models are compact and convenient for finding information estimates in various situations. The use of the proposed model variants will contribute to improving the efficiency and efficiency of monitoring and managing changes in the uncertainty states of parameters of objects and systems of various nature.

Recent Advances in Computational Convective Heat Transfer Study in a Sub-Channel for Nuclear Power Reactor and Future Directions

A nuclear power reactor's primary use is to generate thermal energy, which in turn produces electricity. The primary heat source is a nuclear fission event occurring inside the fuel rod. The convection heat is transmitted through the coolant by the heat energy generated at the fuel rod wall boundary. Better heat transfer is produced in the flow area by turbulence and irregularity. As a result, turbulent flow heat transfer may present a significant challenge when predicting and assessing the thermal performance of nuclear power reactors. Computational techniques in convective heat transfer have become indispensable for solving challenging issues in the fields of science and engineering thanks to the development of current sophisticated numerical methods and high-performance computer hardware. The development of novel computational techniques and models for complicated transport and multi-physical phenomena is constantly in demand throughout applicable disciplines. This chapter's objective is to provide some recent developments in computational techniques for convective heat transfer, taking into account research interests in the community of mass and heat transfer, and to showcase relevant applications in nuclear power plant engineering domains including future directions. This study describes the most recent advancements in nuclear reactor convective heat transfer research utilizing the computational fluid dynamics (CFD) method, particularly at Ansys Fluent. This work examines the convective heat transfer and fluid dynamics fluid dynamics for turbulent flows across three rod bundle sub-channels that are typical of those employed in the PWR-based VVER type reactor. In this paper, CFD analysis is carried out using the software tool Ansys Fluent. Temperature distribution profile, velocity profile, pressure drop, and turbulence properties were investigated in this study. Boundary conditions i.e. temperature, velocity, pressure, heat flux, and heat generation rate were applied in the sub-channel domain. The main obstacles and bright spots for the CFD methods in nuclear reactor engineering are discussed, which helps to further its further uses. We intend to research a full-length fuel bundle model for VVER-1200 in the future to gather specific fluid characteristic data and use the findings to analyze safety and operate nuclear power facilities in Bangladesh. This paper presents a thorough analysis of the sub-channel thermal hydraulic codes used in nuclear reactor core analysis. This review discusses several facets of previous experimental, analytical, and computational work on rod bundles and identifies potential future directions based on those earlier studies.

COMPUTER MODELS OF INCANDENSCENT LAMPS

The article is devoted to the research of existing and the development of new computer models of the hemispherical and normal spectral coefficient of emission of optical incandescent lamps. Optical lamps (filament electric lamp, FEL) remain a prevalent source of radiation in various optoelectronic systems today. They provide radiation in the ultraviolet, visible, near-infrared (IR-A, NIR), and mid-infrared (IR-B, SWIR) spectral ranges and have typical color temperatures of 2300 K, 2850 K, 3200 K.An analysis of two groups of existing FEL models was carried out.The models of the first group are oriented primarily for existing calibration and certification installations of optoelectronic systems.The models of the second group are more universal and comprehensive.They reproduce the element-by-element structure of the modeled optoelectronic system and use the physical and constructive parameters of significant elements in the modeled system. Planck's formula or Wien's approximation is the basis for all types of temperature source models. Existing models are designed for the visible range and the range of 0.34 μm – 2.6 μm for temperatures of 1600 K – 2800 K, which does not take into account all the capabilities of modern FELs.The aim of the work is to investigate known models and polynomial models for use in calculations of modern optical lamps with temperatures ranging from 2300 K to 3200 K across a wide range of wavelengths, including ultraviolet, IRA, and IR-B.The accuracy of the Larabi and Pon models has been analyzed and their parameters have been modified. The obtained dependencies ensure an increase in accuracy by 1.4 - 4 times.The accuracy and cost-effectiveness of polynomial models of the normal spectral coefficient of radiation in the ultraviolet, visible, IR-A and IR-B spectrum ranges were analyzed. Model coefficients have been calculated based on the criterion of minimizing the root mean square deviation. It has been demonstrated that, according to the criterion of average errors in the range of 0.23 μm - 2.7 μm, all dependencies have errors of less than 5 % throughout the entire range and provide a minimum of 5 correct digits.According to the criterion of maximum errors with a relative error of 5%, linear expressions cannot be applied, and an error of less than 1 % is provided only by cubic polynomials. A combined polynomial model was formed, which provides a methodological error of approximation of less than 1 % in all spectral sub-ranges, and in the visible range of 0.3 μm - 0.94 μm - less than 0.5 %.The time costs of the models are determined and the combined models are formed for use in processing systems with limited computing capabilities.

Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion

Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence drug efficacy. Therefore, research into the relationships between circRNAs and drugs can be of great significance in increasing the comprehension of circRNAs function, as well as contributing to the discovery of new drugs and the repurposing of existing drugs. However, it is time-consuming and costly to validate the function of circRNA with traditional medical research methods. Therefore, the development of efficient and accurate computational models that can assist in discovering the potential interactions between circRNAs and drugs is urgently needed. In this study, a novel method is proposed, called DHANMKF , that aims to predict potential circRNA-drug sensitivity interactions for further biomedical screening and validation. Firstly, multimodal networks were constructed by DHANMKF using multiple sources of information on circRNAs and drugs. Secondly, comprehensive intra-type and inter-type node representations were learned using bi-typed multi-relational heterogeneous graphs, which are attention-based encoders utilizing a hierarchical process. Thirdly, the multi-kernel fusion method was used to fuse intra-type embedding and inter-type embedding. Finally, the Dual Laplacian Regularized Least Squares method (DLapRLS) was used to predict the potential circRNA-drug sensitivity associations using the combined kernel in circRNA and drug spaces. Compared with the other methods, DHANMKF obtained the highest AUC value on two datasets. Code is available at https://github.com/cuntjx/DHANMKF.

Transforming Structural Engineering: Examining the Opportunities and Risks of ChatGPT and Other Large Language Models

The advancements in technology, particularly the development of high-performance computing (HPC) and large language models (LLMs) like ChatGPT, can potentially transform the field of Structural Engineering. Use of LLMs, such as ChatGPT, offers several opportunities in Structural Engineering, including the development of innovative design solutions, use in code-based structural analysis programs by automating repetitive coding tasks, conforming to building code requirements by automating compliance checks, and storing information. The critical concerns arise in LLM’s regarding biases, misinformation, safety, reliability, and lack of domain expertise. This paper explores the opportunities and risks associated with using ChatGPT and LLMs in Structural Engineering, focusing on efficiency, accuracy, and reliability. The main aim of the study is to examine the limitations and potential risks of relying solely on machine-generated information and to provide mitigation strategies to overcome them. Careful management to prevent harmful content, collaboration with human experts for accurate results, establishing guidelines and standards are obligatory measures to address ethical concerns such as bias, privacy, and abuse. Continuous monitoring and updating of LLMs are essential to maintain accuracy and relevance. While ChatGPT and LLMs offer significant benefits in Structural Engineering, responsible usage in combination with human expertise and machine-generated insights are vital to maximizing their potential while mitigating risks and ensuring safe as well as reliable engineering practices.

Digital Fracture: New Approach for 3D Organ Modeling

Biofabrication emerged a few years ago as a new research field with a set of promising technologies that have the potential to impact multiple sectors. In this field, the idea of 3D bioprinting originated from rapid prototyping (additive manufacturing) technology. This technology consists of some steps or stages, the first of which is the development of virtual computer models. However, there are some challenges to be overcome in order to develop reliable models for bioprinting purposes. Here, we present the proposal of a workflow using free and open-source software to produce reliable organ models from computerized tomography (CT) and magnetic resonance imaging (MRI) scans. We also propose the concept of digital fracture as a novel approach applicable to generating computer-aided design (CAD) models, especially for 3D bioprinting processes. This novel strategy can be used as an organic way to create smaller organ models compatible with some limitations of the current bioprinters, including relatively low speed, limited spatial resolution, and low accuracy.

Contrast enhanced computed tomography of small ruminants: Caprine and ovine.

The use of small ruminants, mainly sheep and goats, is increasing in biomedical research. Small ruminants are a desirable animal model due to their human-like anatomy and physiology. However, the large variability between studies and lack of baseline data on these animals creates a barrier to further research. This knowledge gap includes a lack of computed tomography (CT) scans for healthy subjects. Full body, contrast enhanced CT scans of caprine and ovine subjects were acquired for subsequent modeling studies. Scans were acquired from an ovine specimen (male, Khatadin, 30-35 kg) and caprine specimen (female, Nubian 30-35 kg). Scans were acquired with and without contrast. Contrast enhanced scans utilized 1.7 mL/kg of contrast administered at 2 mL/s and scans were acquired 20 seconds, 80 seconds, and 5 minutes post-contrast. Scans were taken at 100 kV and 400 mA. Each scan was reconstructed using a bone window and a soft tissue window. Sixteen full body image data sets are presented (2 specimens by 4 contrast levels by 2 reconstruction windows) and are available for download through the form located at: https://redcap.link/COScanData. Scans showed that the post-contrast timing and scan reconstruction method affected structural visualization. The data are intended for further biomedical research on ruminants related to computational model development, device prototyping, comparative diagnostics, intervention planning, and other forms of translational research.

Методический подход к исследованию магнитного взаимодействия тонкодисперсных частиц в водной суспензии методом компьютерного моделирования

The need to increase the efficiency of mining operations determines the relevance of studying the properties of fine mineral particles for their extraction and reduction of environmental pollution. The development of approaches to studying magnetic interaction of fine particles is of scientific and practical importance and is associated with the complexity of calculating the superposition of their fields when solving the problem of controlling the magnetic properties of a suspension. A computer model of the dynamics of magnetic particle interaction was developed with account for their aggregation under the impact of the magnetic dipole-dipole interaction and the destruction of aggregates during thermal (Brownian) motion in order to predict the magnetic properties of the suspension using the example of magnetite deposits of the Zaimandrovsky iron ore region. The calculation shows that the electrostatic and dispersion interactions do not have a significant effect on the interaction dynamics of micron and submicron particles of magnetite with account of the experimentally measured zeta potential and the Hamaker's constant. A procedure has been developed for calibrating a computer model of the interaction dynamics of magnetic particles using the temperature dependence of the coefficient of translational diffusion of magnetite particles and the concentration dependence of the magnetic susceptibility of a suspension. An array of calculated values of the diffusion coefficient of the model particles and the initial magnetic susceptibility of their system is formed in a wide range of computer model parameters. A procedure has been developed for linking the calculated and experimental data by varying the normalization parameters of the particle size, viscosity of the medium, and thermal energy in order to minimize the maximum discrepancy between the values. The necessity is established of taking into account the change in magnetic properties with a decrease in the size of magnetite particles during the calibration of the magnetic susceptibility of a model system. The developed methodological approach ensures good convergence of the calculated and experimental data and makes it possible to visualize the aggregation of the model particles as the result of dipole-dipole interactions. The developed computer model of the interaction dynamics of magnetic particles can be used to study the effect of an external magnetic field on the aggregation ability of fine magnetite particles in order to control their extraction in separation processes.

Enhanced modelling of planar radial-loaded deep groove ball bearings with smooth-contact formulation

AbstractBearings are mechanical components designed to restrict the relative rotary motion between moving parts and transmit loads with low friction. Their performance directly impacts the durability, efficiency and reliability of various machinery. Therefore, bearing failures can lead to economic costs, repair/stoppage times, accidents and regulatory compliance issues. In the context of Industry 4.0, the development of detailed and reliable computational models for simulating bearings’ dynamics plays a crucial role in establishing digital twins and implementing advanced predictive maintenance strategies.This work focuses on modelling radial-loaded deep groove ball bearings under the multibody systems dynamics framework and the components of the bearing (inner and outer rings, rolling elements, and cage) are treated as separate bodies. A smooth contact approach is utilised to characterise the contact/impact phenomena, providing flexibility and efficiency in monitoring the whole contact event. In this sense, suitable normal and friction contact force models are used to describe those interactions between the contacting bodies. The main contribution of this work relies on the modelling strategies to represent the cage/rolling element interaction.Having that in mind, several multibody models of radial-loaded deep groove ball bearings are developed considering different modelling assumptions, resulting in dynamic analyses with various levels of complexity. The underlying simplifications are described, and their main advantages and shortcomings are discussed. The simulation results demonstrated the significant impact of accurately selecting the modelling parameters. The promising results of this study pave the way for future investigations, extending to other geometries of rolling contact bearings and working conditions.

The Efficiency of Question-Asking Strategies in a Real-World Visual Search Task.

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversationalsystems.

Hydraulic conductivity of human cancer tissue: A hybrid study.

Elevated tumor tissue interstitial fluid pressure (IFP) is an adverse biomechanical biomarker that predicts poor therapy response and an aggressive phenotype. Advances in functional imaging have opened the prospect of measuring IFP non-invasively. Image-based estimation of the IFP requires knowledge of the tissue hydraulic conductivity (K), a measure for the ease of bulk flow through the interstitium. However, data on the magnitude of K in human cancer tissue are not available. We measured the hydraulic conductivity of tumor tissue using modified Ussing chambers in surgical resection specimens. The effect of the tumor microenvironment (TME) on K was investigated by quantifying the collagen content, cell density, and fibroblast density of the tested samples using quantitative immune histochemistry. Also, we developed a computational fluid dynamics (CFD) model to evaluate the role of K on interstitial fluid flow and drug transport in solid tumors. The results show that the hydraulic conductivity of human tumor tissues is very limited, ranging from approximately 10-15 to 10-14 m2/Pa∙s. Moreover, K values varied significantly between tumor types and between different samples from the same tumor. A significant inverse correlation was found between collagen fiber density and hydraulic conductivity values. However, no correlation was detected between K and cancer cell or fibroblast densities. The computational model demonstrated the impact of K on the interstitial fluid flow and the drug concentration profile: higher K values led to a lower IFP and deeper drug penetration. Human tumor tissue is characterized by a very limited hydraulic conductivity, representing a barrier to effective drug transport. The results of this study can inform the development of realistic computational models, facilitate non-invasive IFP estimation, and contribute to stromal targeting anticancer therapies.