Computational Model Research Articles

Integrated substrate and adsorbent layer additive manufacturing for asymmetrically operating minichannel adsorbent bed

Adsorption heat pumps (AHP) have conventionally used packed bed design, making their operation sluggish leading to difficulty in using heat for the adsorbent regeneration. While thin adsorbent coatings improve the performance of AHPs when implemented in microscale geometries like minichannels, integrating the adsorbent coating procedure into the scaled-up adsorbent bed manufacturing has been impractical because of the finite time required for the coating process. Meanwhile, separately coated plates manifest sealing issues. This research details an innovative avenue to fabricate a unified adsorbent bed integrated with thin adsorbent coatings. Here, a compact adsorbent bed was fabricated through the integration of additive manufacturing using a fused deposition modeling (FDM) 3D printer for the substrate and resin curing for the MIL-101 (Cr) adsorbent layer. The multi-layered bed was designed to exchange heat between heat transfer fluid (HTF) and the passages through which the refrigerant vapor flows. The vapor channels were coated with a MIL-101 (Cr) adsorbent. The research incorporated both experiments and computational models to evaluate the operation of this adsorbent bed through the measurement of pressure during the adsorption and desorption stages for this component. Various HTF temperatures (80 °C − 90 °C) and flow rates (0.125 kg min−1 – 0.25 kg min -1) were tested. The findings revealed the much-desired asymmetrical operation with long adsorption (75–80 % of the cycle time) and short desorption stages (20 %-25 % of the cycle time), opening the possibility of employing a single bed in adsorption cooling systems to produce a near continuous cooling effect leading to more compact AHPs. The computational model developed for this test setup using MATLAB strongly aligned with experimental findings, with an error margin of 4–12 %.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells

AbstractCells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non‐heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2’s catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2’s gas tunnel. Using kinetic stopped‐flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5‐fold enhancement in its association rate to the iron center of tunnel‐engineered mutants. Our most effectively designed mutant displays 9‐fold enhanced catalytic efficiency (kcat/KM=830±40 M−1 s−1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF‐1α, as compared to WT PHD2 (kcat/KM=90±9 M−1 s−1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK‐293T mammalian cells reveal significant reduction of HIF‐1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Comparing the hydrological performance of blue green infrastructure design strategies in urban/semi-urban catchments for stormwater management

ABSTRACT Blue green infrastructure (BGI), in recent decades, have been increasingly recognized as robust stormwater control measures to reduce urban flooding, promote infiltration, and restore a catchment's flow to its pre-development stage. However, studies comparing the hydrological benefits of BGI alternatives at catchment scale are often limited to single catchment or single/few BGI options scaled over a catchment. This study designed a set of BGI alternatives as a combination of different BGI facilities in terms of the following: (a) spatial distribution scale (end-of-pipe vs. decentralized) and (b) naturalness scale (less engineered vs. more engineered), in three different urban catchments representing an inner city, a residential suburb, and a new urban housing. In addition, their hydrological performances were compared. A 10-year return period design rain and a continuous rain series of 11 years were modelled for each BGI alternative using the computer model (stormwater management model). It was observed that in most catchments, decentralized alternatives (both engineered and natural) showed better potential to reduce the magnitude and frequency of flooding than centralized measures. Similarly, the tested decentralized natural, less engineered alternatives showed higher potential to increase infiltration than the decentralized engineered alternatives in all three catchments. Meanwhile, infiltration-based BGI alternatives showed similar potential to mimic pre-development flow as other decentralized BGI alternatives.

3D printed polymeric stent design: Mechanical testing and computational modeling

Polymer-based bioresorbable scaffolds (BRS) aim to reduce the long-term issues associated with metal stents. Yet, first-generation BRS designs experienced a significantly higher rate of clinical failures compared to permanent implants. This prompted the development of alternative scaffolds, such as the poly(L-lactide-co-ε-caprolactone) (PLCL) solvent-casted stent, whose mechanical performance has yet to be addressed. This study examines the mechanical behavior of this novel scaffold across a wide range of parallel and radial compression diameters. The analysis highlights the scaffold’s varying responses under different loading conditions and provides insights into interpreting simulation model parameters to accurately reflect experimental results.Stents demonstrated a parallel crush resistance of 0.11 N/mm at maximum compression, whereas the radial forces were significantly higher, reaching up to 1.80 N/mm. Additionally, the parallel test keeps the stent in the elastic regime, with almost no regions exceeding 50 MPa of stress, while the radial test causes significant structural deformation, with localized plastic strain reaching up to 30 %. Results showed that underestimating yield strain in computational models leads to discrepancies with experimental results, being 5 % the most accurate value for matching computational and experimental results for PLCL solvent-casted stents.This comprehensive approach is vital for optimizing BRS design and predicting clinical performance.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells.

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2's catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2's gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM=830±40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1α, as compared to WT PHD2 (kcat/KM=90±9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Constructing a picture of fatigue in the context of cancer: assessment of construct overlap in common fatigue scales.

Individuals diagnosed with cancer experience multiple inter-related short- and long-term side effects. Chief among such symptomology is cancer-related fatigue (CRF), which, if left unmanaged, can become chronic and result in increased disability and healthcare utilization. A growing number of self-report scales have been developed to measure CRF symptoms based on various theoretical conceptualizations with the aim of promoting targeted assessment and intervention efforts. It may be, however, unwise to assume that the various measures are conceptually similar (i.e., that they assess for the same constructs). Accordingly, we aimed to characterize item content among nine self-report scales, using a Jaccard index to quantify content overlap among scales. We characterized construct assessment among nine self-report scales recommended to assess CRF by a recent clinical practice guideline, and used a Jaccard index to quantify content overlap among scales. Analysis of 208 items across nine rating scales resulted in 20 distinct symptoms of CRF assessed. The most common symptoms were energy level (captured in all nine scales), cognitive function, impaired task performance (in eight scales), sleepiness, and physical function (in seven scales). Mean overlap among all scales was low (Jaccard index = 0.455). Only one construct (duration of fatigue; 5.0%) was captured by a single scale, and one symptom (energy level; 5.0%) was common across all scales. The PFS, MFSI, and BFI each captured at least one symptom from each of the NCCN domains of CRF. CRF scales are heterogeneousin the content they measure, critically impairing integration of knowledge across studies using disparate scales. Future work is urgently needed to build more integrated theoretical and/or computational models of CRF based on relevant mechanisms.

Long sequence Hopfield memory*

Abstract Sequence memory is an essential attribute of natural and artificial intelligence that enables agents to encode, store, and retrieve complex sequences of stimuli and actions. Computational models of sequence memory have been proposed where recurrent Hopfield-like neural networks are trained with temporally asymmetric Hebbian rules. However, these networks suffer from limited sequence capacity (maximal length of the stored sequence) due to interference between the memories. Inspired by recent work on Dense Associative Memories, we expand the sequence capacity of these models by introducing a nonlinear interaction term, enhancing separation between the patterns. We derive novel scaling laws for sequence capacity with respect to network size, significantly outperforming existing scaling laws for models based on traditional Hopfield networks, and verify these theoretical results with numerical simulation. Moreover, we introduce a generalized pseudoinverse rule to recall sequences of highly correlated patterns. Finally, we extend this model to store sequences with variable timing between states’ transitions and describe a biologically-plausible implementation, with connections to motor neuroscience.

Viscous shear is a key force in Drosophila ventral furrow morphogenesis.

Ventral furrow (VF) formation in Drosophila melanogaster is an important model of epithelial folding. Previous models of VF formation require cell volume conservation to convert apically localized constriction forces into lateral cell elongation and tissue folding. Here, we investigated embryonic morphogenesis in anillin knockdown (scra RNAi) embryos, where basal cell membranes fail to form and therefore cells can lose cytoplasmic volume through their basal side. Surprisingly, the mesoderm elongation and subsequent folding that comprise VF formation occurred essentially normally. We hypothesized that the effects of viscous shear may be sufficient to drive membrane elongation, providing effective volume conservation, and thus driving tissue folding. Since this hypothesis may not be possible to test experimentally, we turned to a computational approach. To test whether viscous shear is a dominant force for morphogenesis in vivo, we developed a 3D computational model incorporating both accurate cell and tissue geometry and experimentally measured material parameters. Results from this model demonstrate that viscous shear generates sufficient force to drive cell elongation and tissue folding in vivo.

Numerical Methods in Modelling of Red Blood Cellflow Behaviour Through a Specific Stacking Pattern

Abstract: The dynamics of red blood cells (RBCs) is one of the major aspects of the cardiovascular system that has been studied intensively in the past few decades. Using computational fluid dynamics, complex nonlinear fluid flows have been modeled. The dynamics of biconcave RBCs are thought to have major influences in cardiovascular diseases and other problems associated with cardiovascular flow behaviour, and the determination of blood rheology and properties. Most reported computational models have been confined to the behaviour of a single RBC in 2-dimensional domains, under physiological flow conditions. This work investigates a particular stacking pattern in analyzing the RBC flow behavior under physiological flow conditions, using the D2Q9 lattice Boltzmann numerical method created using Matlab. Prior to the analysis the Matlab script was benchmarked using the Poiseuille flow and the flow around the cross-section of a cylinder, after which the accuracy of the method used was determined. The benchmarks showed that the lattice Boltzmann code had minimal error. The accuracy was determined using the data obtained from Matlab and a created excel program. It also showed that the lattice Boltzmann method was of the first order, which corresponds with results existing in literatures. The analysis of the stacking pattern showed how RBC flows through the chosen stacking pattern, and the results are shown.

Molecular simulations of increased thermal energy storage in metal–organic heat carriers with R1234ze(E)/R32 mixtures combined with IRMOF-1

Metal-organic heat carrier (MOHC) nanofluids offer a promising solution to enhance the efficiency of Organic Rankine Cycle systems. In this study, we developed a computational model to assess the thermal energy storage capacity of MOHC nanofluids using a base fluid mixture of R1234ze(E) and R32 refrigerants. Through Grand Canonical Monte Carlo and molecular dynamics simulations, we examined the adsorption characteristics, desorption heat, and thermodynamic properties of MOHCs. Our findings reveal that R32 molecules are preferentially adsorbed in IRMOF-1 compared to R1234ze(E), with adsorption selectivity of R32 over R1234ze(E) increasing under higher adsorption pressures. When the temperature during the endothermic process surpasses the phase transition point of the base fluid, the latent heat of phase transition can be fully exploited to enhance energy storage. Moreover, the inclusion of IRMOF-1 nanoparticles significantly improves the energy storage properties of both R32 and R1234ze(E), as well as their mixtures. The energy storage enhancement provided by IRMOF-1 is particularly pronounced under high working pressures and low temperature differences. These insights offer valuable guidance for selecting appropriate base fluids in MOHC systems, thereby optimizing the utilization of low-grade energy sources.

Exploring Fatigue Crack Initiation in Helical Gears: Impact of Carburization and Frictional Influences

A computational model is presented to analyze the fatigue crack initiation period in helical gears, incorporating considerations for heat treatment via carburization and friction effects. The aim is to determine the number of stress cycles necessary for the initiation and growth of initial cracks, while investigating the impact of dynamic behavior. This study employs a dynamic gear model with two degrees of freedom in torsion, developed using MATLAB, and incorporates the Papadopoulos criterion for fatigue analysis. The computational results are benchmarked against the strain-life method. The findings underscore the significant influence of the friction coefficient between surfaces, heat treatment, and dynamic loads on the growth of initial fatigue cracks. For instance, with a friction coefficient (μ) of 0.5, the initial crack forms on the tooth surface (y=0) in both methods, with the (ε-N) method suggesting 28-65 cycles and the Papadopoulos criterion indicating 101-156 cycles. The latter accounts for the greater influence of residual stresses. Additionally, untreated gears exhibit a minimum number of loading cycles for initial crack appearance at 1.07 × 104 cycles. In contrast, carburized gears demonstrate an extended lifespan, requiring 1.45 × 105 loading cycles for crack initiation in our example. This emphasizes the efficacy of carburization in enhancing gear durability.

Картографирование пещер по фотограмметрическим данным

The authors present a new approach to mapping hard-to-reach caves. It relies on a modern framework of ground-based stereo photogrammetric and geodetic surveying, aerial photography from UAVs and computer modelling. A stereo system was designed and assembled, consisting of two GoProHero9 Black cameras and a powerful artificial light source with diffusing lens, mounted on the operator`s helmet. The site for underground filming was the Syanovskaya quarry (Moscow oblast, Domodedovo rayon). The aerial survey of the cave ground part was also made with DJIAir 2S UAV; it included a ravine with a concrete well, which was the entrance to the quarry. The article deals with the processes of geodetic georeferencing, external orientation of the surface model and underground parts of the cave, as well as the cartographic materials obtained in the course of the work

An Overview of Software Sensor Applications in Biosystem Monitoring and Control

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

Nature of Reactive Sites in TS-1 from 15N Solid-State NMR and Ti K-Edge X-Ray Absorption Spectroscopic Signatures Upon Pyridine Adsorption.

Ti-containing zeotypes, notably titanosilicalite-1 (TS-1), are prominent examples of heterogeneous catalysts that have found applications in selective oxidation processes with hydrogen peroxide. Despite extensive characterization studies including using various probe molecules to interrogate the nature and the local environment of Ti sites, their detailed structure (as well as reactivity) remains elusive. Here, we demonstrate that using low temperature 15N magic angle spinning (MAS) ssNMR spectroscopy of adsorbed pyridine on TS-1 combined with Ti K-edge XANES on a range of samples (dehydrated, hydrated, contacted with H2O2 and pyridine) provides unique information regarding the Ti sites, highlighting their reactivity and dynamic nature. While dehydrated TS-1 shows only Lewis acid sites, the presence of H2O generates Brønsted acid sites, whose amount correlates with water loading. Moreover, the methodology─based on 15N ssNMR and Ti K-edge XANES─applied to a library of samples with various Ti-loadings and absence of extraframework TiO2 also enables quantification of the amount of Lewis acid sites and to establish a structure-activity descriptor (ratio of pyridine adsorbed on silanols vs titanium). Complementary analysis including computational modeling reveals that the reaction of Ti sites with H2O generates an acidic bridging silanol Ti-(OH)-Si, upon hydrolysis of one Ti-O-Si linkage, where Ti expands its coordination from four to pentacoordinated according to XAS.

SwinUNeCCt: bidirectional hash-based agent transformer for cervical cancer MRI image multi-task learning

Cervical cancer is the fourth most common malignant tumor among women globally, posing a significant threat to women’s health. In 2022, approximately 600,000 new cases were reported, and 340,000 deaths occurred due to cervical cancer. Magnetic resonance imaging (MRI) is the preferred imaging method for diagnosing, staging, and evaluating cervical cancer. However, manual segmentation of MRI images is time-consuming and subjective. Therefore, there is an urgent need for automatic segmentation models to identify cervical cancer lesions in MRI scans accurately. All MRIs in our research are from cervical cancer patients diagnosed by pathology at Tongren City People’s Hospital. Strict data selection criteria and clearly defined inclusion and exclusion conditions were established to ensure data consistency and accuracy of research results. The dataset contains imaging data from 122 cervical cancer patients, with each patient having 100 pelvic dynamic contrast-enhanced MRI scans. Annotations were jointly completed by medical professionals from Universiti Putra Malaysia and the Radiology Department of Tongren City People’s Hospital to ensure data accuracy and reliability. Additionally, a novel computer-aided diagnosis model named SwinUNeCCt is proposed. This model incorporates (i) A bidirectional hash-based agent multi-head self-attention mechanism, which optimizes the interaction between local and global features in MRI, aiding in more accurate lesion identification. (ii) Reduced computational complexity of the self-attention mechanism. The effectiveness of the SwinUNeCCt model has been validated through comparisons with state-of-the-art 3D medical models, including nnUnet, TransBTS, nnFormer, UnetR, UnesT, SwinUNetR, and SwinUNeLCsT. In semantic segmentation tasks without a classification module, the SwinUNeCCt model demonstrates excellent performance across multiple key metrics: achieving a 95HD of 6.25, an IoU of 0.669, and a DSC of 0.802, all of which are the best results among the compared models. Simultaneously, SwinUNeCCt strikes a good balance between computational efficiency and model complexity, requiring only 442.7 GFLOPs of computational power and 71.2 M parameters. Furthermore, in semantic segmentation tasks that include a classification module, the SwinUNeCCt model also exhibits powerful recognition capabilities. Although this slightly increases computational overhead and model complexity, its performance surpasses other comparative models. The SwinUNeCCt model demonstrates excellent performance in semantic segmentation tasks, achieving the best results among state-of-the-art 3D medical models across multiple key metrics. It balances computational efficiency and model complexity well, maintaining high performance even with the inclusion of a classification module.

Contribution of the plantar fascia and long plantar ligaments to the stability of the longitudinal arch of the foot

The contribution of the Plantar Fascia (PF) and Long Plantar Ligament (LPL), two ligaments extending from the hindfoot to the forefoot, to arch stability has been studied in the past using in vivo, in vitro, and in silico methodologies. In silico studies were based on one single model obtained from one single subject and did not account for the known inter-subject morphological and biomechanical variations. In the present study, we developed computational dynamic models of nine different legs obtained from nine different individuals to evaluate the role of the LPL and PF in arch support, accounting for biological differences between subjects. These models were validated by comparing the simulation results against experimental results from the corresponding cadaver legs. After validation, we simulated body weight conditions for each model by applying a vertical load to the tibia, starting from zero and increasing linearly to 720 N. Kinematic and dynamic parameters, including the variation of the medial arch angle and of the navicular height, as well as the passive forces developed by the LPL and PF, were used to evaluate the contribution of these ligaments to arch support under body weight. The results indicate that a total collapse of the medial longitudinal arch occurred only when both the LPL and PF were absent, but a stable arch was maintained when either one of these two ligament structures were present. The results varied significantly among the specific models, highlighting the importance of using multiple models to account for inter-subject morphological differences.

Iconic models in science and psychology

Scientists use a wide variety of models to formally represent their theories, including computational, quantitative, and mathematical models. A type of model often overlooked in discussions of modeling is an iconic model, which is a visual or physical representation of the structures and processes comprising some natural phenomenon. In the current paper, we present a brief history of iconic models as they have been employed in other sciences such as astronomy, physics, biology, and chemistry. We then present several iconic models that are well known to psychologists and show how they played important roles in theory construction and evaluation. Modern methods of iconic modeling are next presented and demonstrated with several examples. Finally, we close with a discussion of how iconic models can be used in tandem with computational, quantitative, or mathematical modeling procedures in the quest for rigorous theories of the human psyche.

A computational modeling of airflow and radon progeny deposition in human respiratory system

A computational modeling of airflow and radon progeny deposition in human respiratory system

The accordion technique did not improve bone healing in a mouse model of distraction osteogenesis.

Distraction osteogenesis (DO) is a valuable surgical method for limb lengthening and bone defect correction, but its lengthy consolidation phase presents challenges. The accordion technique (AT), involving compression and distraction of bone segments, has shown potential for enhancing healing. This study aimed to investigate the effectiveness of the AT conducted at three different time points (distraction phase, early consolidation phase, or late consolidation phase) compared to conventional DO in a mouse osteotomy model. Healing was evaluated using in vivo microCT, histology, and computational modeling. Results showed that bridging frequency, BV, and callus tissue composition were similar between conventional DO and late consolidation AT. In contrast, distraction phase AT led to delayed healing at day 15 with a 72% reduction in BV compared to DO, but no significant differences by the endpoint. Early consolidation AT showed significantly impaired healing compared to DO, with only 29% of mice achieving bony bridging, and significantly reduced bone marrow area of the endpoint callus. In silico modeling was generally predictive of in vivo findings and suggested that application of the AT during early consolidation results in destruction of newly-formed vascular tissue. Overall, no benefit was observed for the AT compared to conventional DO with the parameters employed in this study.

Integrating an urban fire model into an operational wildland fire model to simulate one dimensional wildland–urban interface fires: a parametric study

Background Wildland fires that occur near communities, in the wildland–urban interface (WUI), can inflict significant damage to urban structures. Although computational models are vital in wildfires, they often focus solely on wildland landscapes. Aim We conducted a computational study to investigate WUI fire spread, encompassing both urban and wildland landscapes. Methods We developed a 1D landscape-scale semi-physical model by integrating a semi-physical urban fire spread model into an Eulerian level set model of wildfire. The model includes ignition and spread through radiation, direct flame contact and ember deposition. Key results Through a parametric study, we compare the relative change of spread rate from various structural properties and landscape layouts represented by model parameters, highlighting the significant impact of fire-resistant structure materials over surface treatments. Layout configurations play a pivotal role in fire spread, with isolated islands of combustibles effective in reducing spread rate, aligning with existing mitigation strategies. Conclusion Despite using a 1D domain and limitations on spatial and temporal variability, our model provides insights into underlying phenomena observed in WUI fires and their mitigation. It offers early-stage development of strategies for managing structure materials and landscape layouts. Implications Our model and findings provide insights into WUI fire dynamics, paving the way for advanced mitigation strategies.