Computational Model Research Articles

Investigation into the impact of charging rates on the stress development within silicon composite electrodes.

Silicon, renowned for its remarkable energy density, has emerged as a focal point in the pursuit of high-energy storage solutions for the next generation. Nevertheless, silicon electrodes are known to undergo significant volume expansion during the insertion of lithium ions, leading to structural deformation and the development of internal stresses, and causing a rapid decline in battery capacity and overall lifespan. To gain deeper insights into the intricacies of charge rate effects, this study employs a combination of in situ measurements and computational modeling to elucidate the cyclic performance of composite silicon electrodes. The findings derived from the established model and curvature measurement system unveil the substantial alterations in stress and deformation as a consequence of varying charge rates. Notably, the active layer experiences compressive forces that diminish as the charge rate decreases. At a charge rate of 0.2, the active layer endures a maximum stress of 89.145 MPa, providing a comprehensive explanation for the observed deterioration in cycling performance at higher charge rates. This study not only establishes a fundamental basis for subsequent stress analyses of silicon electrodes but also lays a solid foundation for further exploration of the impact of charge rates on composite silicon electrodes.

Numerical investigation on flow-induced wall shear stress variation of metastatic cancer cells in lymphatics with elastic valves.

Hematogenous metastasis occurs when cancer cells detach from the extracellular matrix in the primary tumor into the bloodstream or lymphatic system. Elucidating the response of metastatic tumor cells in suspension to the flow conditions in lymphatics with valves from a mechanical/fluidic perspective is necessary. A physiologically relevant computational model of a lymphatic vessel with valves was constructed using fully coupled fluid-cell-vessel interactions to investigate the effects of lymphatic vessel contractility, valve properties, and cell size and stiffness on the variations in magnitude and gradient of the flow-induced wall shear stress (WSS) experienced by suspended tumor cells. Results indicated that the maximum WSSmax increased with the increments in cell diameter, vessel contraction amplitude, and valve stiffness. The decrease in vessel contraction period and valve aspect ratio also increased the maximum WSSmax. The influence of the properties of the valve on the WSS was more significant among the factors mentioned above. The maximum WSSmax acting on the cancer cell when the cell reversed the direction of its motion in the valve region increased by 0.5-1.4 times that before the cell entered the valve region. The maximum change in WSS was in the range of 0.004-0.028 Pa/µm depending on the factors studied. They slightly exceeded the values associated with breast cancer cell apoptosis. The results of this study provide biofluid mechanics-based support for mechanobiological research on the metastasis of metastatic cancer cells in suspension within the lymphatics.

Editorial: Combining machine learning, computational modeling, and high throughput experimentation to accelerate discovery in systems microbiology

Editorial: Combining machine learning, computational modeling, and high throughput experimentation to accelerate discovery in systems microbiology

A computational model for single cell Lamin-A structural organization after microfluidic compression.

In recent years, nuclear mechanobiology gained a lot of attention for the study of cell responses to external cues like adhesive forces, applied compression, and/or shear-stresses. In details, the Lamin-A protein-as major constituent of the cell nucleus structure-plays a crucial role in the overall nucleus mechanobiological response. However, modeling and analysis of Lamin-A protein organization upon rapid compression conditions in microfluidics are still difficult to be performed. Here, we introduce the possibility to control an applied microfluidic compression on single cells, deforming them up to the nucleus level. In a wide range of stresses (~1-102 kPa) applied on healthy and cancer cells, we report increasing Lamin-A intensities which scale as a power law with the applied compression. Then, an increase up to two times of the nuclear viscosity is measured in healthy cells, due to the modified Lamin-A organization. This is ascribable to the increasing assembly of Lamin-A filament-like branches which increment both in number and elongation (up to branches four-timelonger). Moreover, the solution of a computational model of differential equations is presented as a powerful tool for a single cell prediction of the Lamin-A assembly as a function of the applied compression.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 eventas 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Distributed reinforcement learning-based optimization of resource scheduling for telematics

As a reliable computational model in telematics, Mobile Edge Computing (MEC) is utilized to lighten the load on individual autonomous vehicles. It enables computationally expensive and latency-sensitive jobs to be offloaded from cars with limited processing power to servers at edge nodes. This paper presents the joint optimization problem as a mixed nonlinear integer programming problem. The scheduling of vehicle tasks and the distribution of communication resources are then rationalized using a resource allocation algorithm based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning to arrive at a suboptimal solution based on the resource allocation policies of single-intelligent DDPG and multi-intelligent DDPG. The numerical simulation demonstrates the superior service experience of the proposed joint optimization strategy of distributed task offloading and resource allocation based on multi-intelligent DDPG over other association and channel selection strategies and joint resource allocation based on single-intelligent DDPG.

Deep learning pose detection model for sow locomotion

Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores. Our goal is to develop a computer vision model for automatically identifying specific points on the sow's body. The automatic identification and ability to track specific body areas, will allow us to conduct kinematic studies with the aim of facilitating the detection of lameness using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D videos images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37 in pixel, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D video image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.

Ion Mobility-Mass Spectrometry Strategies to Elucidate the Anhydrous Structure of Noncovalent Guest/Host Complexes.

Structural mass spectrometry (MS) techniques are fast and sensitive analytical methods to identify noncovalent guest/host complexation phenomena for desirable solution-phase properties. Current MS-based studies on guest/host complexes of drug and drug-like molecules are sparse, and there is limited guidance on how to interpret MS information in the context of host nanoencapsulation and inclusion. Here, we use structural MS strategies, combining energy-resolved MS (ERMS), ion mobility-MS (IM-MS), and computational modeling, to characterize 14 chemically distinct drug and drug-like compounds for their propensity to form guest/host complexes with the widely used excipient, beta-cyclodextrin (βCD). The majority (11/14) yielded a 1:1 guest/host complex, and ion mobility collision cross section (CCS) analysis provided subtle evidence of gas-phase compaction of complexes in both polarities. The three distinct dissociation channels observed in ERMS (i.e., charged βCD, charged guest, and partial guest loss) were used to direct charge-site assignments for computational modeling, and structural candidates were prioritized using helium-derived CCS measurements combined with root-mean-square distance analysis. The combined analytical information from ERMS, IM-MS, and computational modeling suggested that the majority of anhydrous complexes are inclusion complexes with βCD. Taken together, this work demonstrates a roadmap for how multiple MS-based analytical measurements can be combined to interpret the structures that guest/host complexes adopt in the absence of water.

A new era of Synthetic Biology - microbial community design

Abstract Synthetic biology conceptualises biological complexity as a network of biological parts, devices and systems with predetermined functionalities, and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesise and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multi-species communities. We argue for a shift in perspective from single organism-centred approaches to emphasising the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities.

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.

Revealing Detailed Cartilage Function Through Nanoparticle Diffusion Imaging: A Computed Tomography & Finite Element Study.

The ability of articular cartilage to withstand significant mechanical stresses during activities, such as walking or running, relies on its distinctive structure. Integrating detailed tissue properties into subject-specific biomechanical models is challenging due to the complexity of analyzing these characteristics. This limitation compromises the accuracy of models in replicating cartilage function and impacts predictive capabilities. To address this, methods revealing cartilage function at the constituent-specific level are essential. In this study, we demonstrated that computational modeling derived individual constituent-specific biomechanical properties could be predicted by a novel nanoparticle contrast-enhanced computer tomography (CECT) method. We imaged articular cartilage samples collected from the equine stifle joint (n = 60) using contrast-enhanced micro-computed tomography (µCECT) to determine contrast agents' intake within the samples, and compared those to cartilage functional properties, derived from a fibril-reinforced poroelastic finite element model. Two distinct imaging techniques were investigated: conventional energy-integrating µCECT employing a cationic tantalum oxide nanoparticle (Ta2O5-cNP) contrast agent and novel photon-counting µCECT utilizing a dual-contrast agent, comprising Ta2O5-cNP and neutral iodixanol. The results demonstrate the capacity to evaluate fibrillar and non-fibrillar functionality of cartilage, along with permeability-affected fluid flow in cartilage. This finding indicates the feasibility of incorporating these specific functional properties into biomechanical computational models, holding potential for personalized approaches to cartilage diagnostics and treatment.

A physically-informed machine learning model for freeform bending

AbstractThis work aims at a fast computational process model of the free-form bending process. It proposes a novel physically-informed machine learning model, which is trained with experimental data of bending constant radii and utilizes additional physical bending knowledge by integrating Timoshenko’s beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die by computing an elastic representation of the tube’s deformation with beam theory at each time step. This elastic representation serves as input for a regression model similar to a partially connected neural network. This physically-informed machine learning model generalizes the constant training radii to complex bend geometries consisting of transitional sections and true spline geometries. It is compared to a benchmark finite element simulation and has an improved prediction quality for complex kinematics while reducing the computation time by four orders of magnitude.

Computational microfluidics of reactive transport processes with solid dissolution and self-induced multiphase flow

There are still many unclear mechanisms in the multiphase reactive flow with solid dissolution processes. In this study, the reactive transport processes coupled with solid dissolution and self-induced multiphase flow in three-dimensional (3D) structures with increasing complexity is studied by developing a 3D computational microfluidic method, which considers multiphase flow, interfacial mass transport, heterogeneous chemical reactions, and solid structure evolution. Solid dissolution diagram in a simple channel in the framework of multiphase flow is proposed, with six coupled multiphase flow and solid dissolution patterns identified and the transition between different patterns discussed. Then, multiphase reactive flow in a porous chip is further studied, and the interesting 3D phenomena are discovered, including enhanced solid dissolution in the middle and enriched bubble generation at the corner along the thickness direction. Considering the importance of reactive surface area, correlations of reactive surface area-porosity-saturation with different dissolution patterns are proposed based on the pore-scale results. Finally, the computational microfluidic model is extended to investigate the multiphase reactive flow in a 3D digital core. Different dissolution patterns are recognized using the local porosity evolution character, and the corresponding pore size distribution and bubble characteristics are deciphered. These findings advance understanding of multiphase reactive transport processes and contribute to improve continuum-scale reactive transport modeling.

The use of diffusion tensor imaging in spinal pathology: a comprehensive literature review.

We reviewed the available literature systematically without meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. To evaluate contemporary literature on use of spinal diffusion tensor imaging(sDTI) in spinal pathology. sDTI reveals the location and functional state of critical long tracts and is a potentially useful adjunct in disease management. Studies were included if they presented or discussed data from investigative or therapeutic procedures involving sDTI on human subjects in the setting of surgically amenable spinal pathology. Studies were excluded if they were (1) restricted to computational models investigating parameters using data not obtained clinically, (2) about cranial DTI methods, (3) about spinal pathology data not related to surgical management, (4) discussions or overviews of methods/techniques with minimal inclusion of objective experimental or clinical data. Degenerative pathologies of interest were restricted to either cervical myelopathy (22/29,75.9%) or lumbar spondylosis 7/29,24.1%). Mass-occupying lesions included intradural pathology and discussed preoperative (7/9,77.8%) and intraoperative imaging(2/9,22.2%) as an adjunct to surgery 22.2%. Traumatic pathology focused on spinal cord injury prognosis and severity grading. sDTI seems useful in surgical decision making and outcome measurements and in establishing clinical prognoses over a wide range of surgical pathologies. Further research is warranted with longer follow-up and larger population sizes in a prospective and controlled protocol.

Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress.

The Autonomic Nervous System (ANS) plays a critical role in regulating cardiac functions. Early detection of ANS dysfunctions is crucial for preventing or slowing the progression of cardiovascular diseases. Current methods for analyzing ANS activity, such as heart rate variability analysis and muscle sympathetic nerve activity recording, face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit personalized health assessments. This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments. The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males) and diabetic subjects (8 females and 6 males). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p<0.05), and also suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p<0.05). The MMARCS model represents an innovative computational approach for quantifying ANS functionality, offering potential benefits for clinical measurements of cardiovascular, disease progression monitoring, and home health monitoring via wearable technology. Its balance between physiological accuracy and model simplicity makes it a promising tool for personalized health assessments.

Examining the Global Patent Landscape of Artificial Intelligence-Driven Solutions for COVID-19

Artificial Intelligence (AI) technologies have been widely applied to tackle Coronavirus Disease 2019 (COVID-19) challenges, from diagnosis to prevention. Patents are a valuable source for understanding the AI technologies used in the COVID-19 context, allowing the identification of the current technological scenario, fields of application, and research, development, and innovation trends. This study aimed to analyze the global patent landscape of AI applications related to COVID-19. To do so, we analyzed AI-related COVID-19 patent metadata collected in the Derwent Innovations Index using systematic review, bibliometrics, and network analysis., Our results show diagnosis as the most frequent application field, followed by prevention. Deep Learning algorithms, such as Convolutional Neural Network (CNN), were predominantly used for diagnosis, while Machine Learning algorithms, such as Support Vector Machine (SVM), were mainly used for prevention. The most frequent International Patent Classification Codes were related to computing arrangements based on specific computational models, information, and communication technology for detecting, monitoring, or modeling epidemics or pandemics, and methods or arrangements for pattern recognition using electronic means. The most central algorithms of the two-mode network were CNN, SVM, and Random Forest (RF), while the most central application fields were diagnosis, prevention, and forecast. The most significant connection between algorithms and application fields occurred between CNN and diagnosis. Our findings contribute to a better understanding of the technological landscape involving AI and COVID-19, and we hope they can inform future research and development’s decision making and planning.

Computational modeling of near-fault earthquake-induced landslides considering stochastic ground motions and spatially varying soil

Landslides represent a large-deformation process influenced by various factors of uncertainty, such as types of ground motion (GM), randomness of GMs, and spatial variability of soils. The behavior of landslides is profoundly affected, making their assessment and measurement challenging. This paper proposes a computational approach for simulating the large-deformation process of landslides based on three-dimensional (3D) non-ordinary state-based peridynamics (NOSBPD). The analysis results of NOSBPD indicate that the mean runout distance triggered by pulse-like ground motions (PLGMs) is 16% greater than that induced by non-pulse ground motions (NPGMs), suggesting that PLGMs exhibit higher destructiveness. Besides, this paper establishes a runout distance assessment framework by considering the stochastic nature of PLGMs. This framework allows for the evaluation of the specific risk probability of landslides caused by stochastic PLGMs that match the target spectrum specified by codes. By introducing the theory of random fields and implementing a coupled procedure in peridynamics, we conducted a detailed analysis of the impact of spatial heterogeneity on the evolution process and consequences of landslides. Additionally, compared to two-dimensional (2D) analysis, the mean runout distance obtained from 3D analysis increased by 27.5%. This suggests that 2D analysis may underestimate the consequences of landslides. The findings of this study can serve as a scientific foundation for predicting the extent and scope of landslides triggered by near-fault earthquakes.

Understanding magnetic hyperthermia performance within the "Brezovich criterion": beyond the uniaxial anisotropy description.

Careful determination of the heating performance of magnetic nanoparticles under AC fields is critical for magnetic hyperthermia applications. However, most interpretations of experimental data are based on the uniaxial anisotropy approximation, which in the first instance can be correlated with the particle aspect ratio. This is to say, the intrinsic magnetocrystalline anisotropy is discarded, under the assumption that the shape contribution dominates. We show in this work that such a premise, generally valid for large field amplitudes, does not hold for describing hyperthermia experiments carried out under small field values. Specifically, given its relevance for in vivo applications, we focus our analysis on the so-called "Brezovich criterion", H·f = 4.85 × 108 A m-1 s-1. By means of a computational model, we show that the intrinsic magnetocrystalline anisotropy plays a critical role in defining the heat output, determining also the role of the shape and aspect ratio of the particles on the SLP. Our results indicate that even small deviations from spherical shape have an important impact on optimizing the heating performance. The influence of interparticle interactions on the dissipated heat is also evaluated. Our results call, therefore, for an improvement in the theoretical models used to interpret magnetic hyperthermia performance.