Combination Of Simulations Research Articles

Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms

The most challenging task in the agricultural sector is to accurately predict crop yield. A typical machine learning algorithm often uses real data to predict crop yield. In this study, we used data generated by the Wild Blueberry Pollination Model, a spatially explicit simulation model validated by field observation and experimental data collected in Maine USA during the last 30 years. The main aim of this study is to evaluate the relative importance of bee species composition and weather factors in regulating wild blueberry agroecosystems. Specifically, we sought to reveal how bee species composition and weather affect yield and to predict optimal bee species composition and weather conditions that achieve the best yield using computer simulation and machine learning algorithms. Multiple linear regression (MLR), boosted decision trees (BDT), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as predictive tools. We also performed a predictor selection before submitting our data to the learning algorithms. In this way, we are able to reduce the dimension of the input without a significant drop in prediction accuracy. As a result, clone size, honeybee, bumblebee, Andrena bee species, Osmia bee species, maximum of upper-temperature ranges, and the number of days with precipitation were chosen as the best predictor variable subset. The results showed that the XGBoost outperformed other algorithms in all measures of model performance for predicting the yield of wild blueberry by achieving a coefficient of determination (R2) of 0.938, root mean square error (RMSE) of 343.026, mean absolute error (MAE) of 206 and relative root mean square error (RRMSE) of 5.444%. The results are consistent with previous work on predicting wild blueberry fruit yield using digital color photography by (Zaman et al., 2008). This study showed that crop yield predictions can be based on computer simulation modeling datasets. Therefore, if a reasonable prediction can be reached, this study should have a significant impact, especially when data collection in the field is challenging.

Characterization of antioxidant, α-glucosidase and tyrosinase inhibitors from the rhizomes of Potentilla anserina L. and their structure–activity relationship

Five new flavonoids (1–5), along with 25 known compounds, were isolated from the rhizomes of Potentilla anserina L. and their structures were identified using spectroscopic and chemical evidence. The extract, all fractions, and all isolated compounds were evaluated for their antioxidant, α-glucosidase, and tyrosinase inhibitory activities, and their structure–activity relationship was interpreted. The biflavanols and quercetin-3-O-α-l-rhamnopyranoside-2″-gallate (14) exhibited significant antioxidant and α-glucosidase inhibition activities. In this study, anti-tyrosinase activity and its mechanism of active compounds (potenserin C (4), potenserin D (5), and quercetin-3-O-α-l-rhamnopyranoside-2″-gallate (14)) were explored by a combination of computational simulations and kinetic studies. Kinetic studies indicated that potenserin C (4) and quercetin-3-O-α-l-rhamnopyranoside-2″-gallate (14) inhibited tyrosinase in a competitive manner, whereas potenserin D (5) acted in a reversible noncompetitive manner. The molecular docking result indicated that the substitution of the glucose moiety with galloyl and the presence of 3′, 4′, 5′-OH in flavonoid aglycones played a crucial role for the tyrosinase inhibiting effect. Moreover, the presence of biflavanols increased the activity against tyrosinase because of strong hydrogen binding, π-alkyl binding, and electrostatic interaction. Thus, the presented experiments developed several new lead compounds that could act as antioxidants and α-glucosidase inhibitors. Furthermore, biflavanols and quercetin-3-O-α-l-rhamnopyranoside-2″-gallate played important roles in the anti-browning activity during food processing.

The effects of air pollution sources / sensor array configurations on the likelihood of obtaining accurate source term estimations

Estimating the source term in the case of multiple leaks using a sparse sensor array is a challenging task. Here, the effect of sensor array/leak configurations on the reliability of the source term estimation is studied using two new measures. The first describes the overall change in the sensor array response to different source terms. The second represents the effect of the source term on the readout of each sensor in the array. These measures are subjected to several model cases differing in sensor array/leak configurations. Then, the source term is estimated using a self-adaptive multiobjective evolutionary (MOEA) search algorithm combined with a gas dispersion model. The method searches for a set of leaks, each one of which has a typical emission rate and location that results in a minimal difference between the sensors' actual and computed pollution concentration. This objective, which is often used for source term estimation, is traded off against the second objective of maintaining a minimum number of active sources, which follows Occam's razor principle of parsimony. Analysis of the results obtained for these model cases suggests that the measures can be implemented as a design tool using a combination of computer simulation and field experiments before operational deployment.

Analysis and modeling of a landslide-induced tsunami-like wave across the Truong river in Quang Nam province, Vietnam

Landslide-induced waves are one of the most disastrous hazards that can post a great threat to human lives and properties. At about 4:00 pm, 5 November 2017, a landslide-induced tsunami-like wave suddenly occurred across the Truong river in Bac Tra My District, Quang Nam province, Vietnam. The water wave destroyed six houses at the opposite bank and caused one person dead and three others injured. This study seeks to investigate the initiation mechanism and process of the landslide and its impulse wave. First, we examined landslide characteristics through site investigations, unmanned aerial vehicle (UAV) surveys, and laboratory testing with a series of standard geotechnical tests on collected soil samples. Then, the initiation and motion of the rainfall-induced landslides were reproduced by the integrated landslide simulation model (LS-RAPID). Finally, a combined computer simulation of the landslide motion and its impulse wave was performed by using a landslide-induced tsunami simulation model (LS-Tsunami). In which, output data from the LS-RAPID was used as input parameters for LS-Tsunami. The analysis shows that the rainfall with very high intensity in a short-time period was the triggering factor of the landslide, which is common factor in the study area. The 12-, 24-, and 48-h accumulative precipitation prior to the landslide recorded to 530, 760, and 950 mm, respectively. In addition, the rainfall trigger presented a typical pattern of rainstorm events in a long duration. Simulation results show that the impulse wave was generated by the landslide mass rapidly entering the river, crossing the river, and directly causing the disastrous damage to the resident area opposite site of the fail slope. The landslide moved down at a maximum speed of 16.4 m/s when its body approached the water surface and generated a maximum wave height of 5 m. There is good agreement between the observed geomorphic evidences and water traces on the site and simulation results of the landslide and its impulse wave. The paper provides a good case study on the understanding of the mechanism and dynamic process of the whole event that significantly contribute to potential landslide hazard assessment and future disaster mitigation in the area.

Spectroscopic and Structural Probing of Excited-State Molecular Dynamics with Time-Resolved Photoelectron Spectroscopy and Ultrafast Electron Diffraction

The combination of computer simulations and two powerful experimental methods for following molecular change on femtosecond timescales offers an unprecedented view of how a photoexcited molecule breaks apart.

Salient Evolution and Coastal Protection Effectiveness of Two Large Artificial Reefs

Black, K.P.; Reddy, K.S.K.; Kulkarni, K.B.; Naik, G.B.; Shreekantha, P., and Mathew, J., 2020. Salient evolution and coastal protection effectiveness of two large artificial reefs. Journal of Coastal Research, 36(4), 709–719. Coconut Creek (Florida), ISSN 0749-0208.Two offshore reefs constructed at Ullal, Karnataka, on India's west coast, are exhibiting coastal protection benefits. The reefs are large (275 m and 325 m long) and uniquely placed well offshore (600 m and 760 m) in intermediate depths (6 and 7 m below chart datum). Their crests are between low-tide and midtide level. Bathymetric and beach surveys show that sand has accumulated to form two evolving shoreline salients, which are currently 2–3 times the length of the reefs. The salients have grown underwater on the inner shelf to create a large sand-retention zone up to 1 m thick and stretching from the beach to the distant lee of the reefs. The reefs are the first protection structures that have restored the beaches and enhanced sand storage over this broad, severely eroded region, both inshore and on the inner shelf. Prior rubble-mound rock revetments (seawalls) and 50–70-m-long groynes failed to restore the beach in stormy monsoon conditions, while the inner shelf continued to erode. The reefs were designed using a combination of computer simulations, measurements of natural salients in India, and empirical relationships. The methodology that led to the reef shapes/sizes and their position offshore is discussed, along with monitoring results.

Preliminary injury risk estimation for occupants involved in frontal crashes by combining computer simulations and real crashes.

The fatality rate can be dramatically reduced with the help of emergency medical services. The purpose of this study was to establish a computational algorithm to predict the injury severity, so as to improve the timeliness, appropriateness, and efficacy of medical care provided. The computer simulations of full-frontal crashes with rigid wall were carried out using LS-DYNA and MADYMO under different collision speeds, airbag deployment time, and seatbelt wearing condition, in which a total of 84 times simulation was conducted. Then an artificial neural network is adopted to construct relevance between head and chest injuries and the injury risk factors; 37 accident cases with Event Data Recorder data and information on occupant injury were collected to validate the model accuracy through receiver operating characteristic analysis. The results showed that delta-v, seatbelt wearing condition, and airbag deployment time were important factors in the occupant's head and chest injuries. When delta-v increased, the occupant had significantly higher level of severe injury on the head and chest; there is a significant difference of Head Injury Criterion and Combined Thoracic Index whether the occupant wore seatbelt. When the airbag deployment time was less than 20 ms, the severity of head and chest injuries did not significantly vary with the increase of deployment time. However, when the deployment time exceeded 20 ms, the severity of head and chest injuries significantly increased with increase in deployment time. The validation result of the algorithm showed that area under the curve = 0.747, p < 0.05, indicating a medium level of accuracy, nearly to previous model. The computer simulation and artificial neural network have a great potential for developing injury risk estimation algorithms suitable for Advanced Automatic Crash Notification applications, which could assist in medical decision-making and medical care.

TRIM5\u03b1 self-assembly and compartmentalization of the HIV-1 viral capsid

The tripartite-motif protein, TRIM5α, is an innate immune sensor that potently restricts retrovirus infection by binding to human immunodeficiency virus capsids. Higher-ordered oligomerization of this protein forms hexagonally patterned structures that wrap around the viral capsid, despite an anomalously low affinity for the capsid protein (CA). Several studies suggest TRIM5α oligomerizes into a lattice with a symmetry and spacing that matches the underlying capsid, to compensate for the weak affinity, yet little is known about how these lattices form. Using a combination of computational simulations and electron cryo-tomography imaging, we reveal the dynamical mechanisms by which these lattices self-assemble. Constrained diffusion allows the lattice to reorganize, whereas defects form on highly curved capsid surfaces to alleviate strain and lattice symmetry mismatches. Statistical analysis localizes the TRIM5α binding interface at or near the CypA binding loop of CA. These simulations elucidate the molecular-scale mechanisms of viral capsid cellular compartmentalization by TRIM5α.

Safety benefit assessment of autonomous emergency braking and steering systems for the protection of cyclists and pedestrians based on a combination of computer simulation and real-world test results

Cyclists and pedestrians account for a significant share of fatalities and serious injuries in the road transport system. In order to protect them, advanced driver assistance systems are being developed and introduced to the market, including autonomous emergency braking and steering systems (AEBSS) that autonomously perform braking or an evasive manoeuvre by steering in case of a pending collision, in order to avoid the collision or mitigate its severity.This study proposes a new prospective framework for quantifying safety benefit of AEBSS for the protection of cyclists and pedestrians in terms of saved lives and reduction in the number of people suffering serious injuries. The core of the framework is a novel application of Bayesian inference in such a way that prior information from counterfactual simulation is updated with new observations from real-world testing of a prototype AEBSS.As an illustration of the method, the framework is applied for safety benefit assessment of the AEBSS developed in the European Union (EU) project PROSPECT. In this application of the framework, counterfactual simulation results based on the German In-Depth Accident Study Pre-Crash Matrix (GIDAS-PCM) data were combined with results from real-world tests on proving grounds.The proposed framework gives a systematic way for the combination of results from different sources and can be considered for understanding the real-world benefit of new AEBSS. Additionally, the Bayesian modelling approach used in this paper has a great potential to be used in a wide range of other research studies.

The benefits of insect-swarm hunting to echolocating bats, and its influence on the evolution of bat echolocation signals.

Predation on swarms of prey, especially using visual information, has drawn much interest in studies of collective movement. Surprisingly, in the field of biosonar this aspect of prey detection, which is probably very common, has received little to no attention. Here, we combine computer simulations and actual echo measurements to accurately estimate the echo sound pressure of insect swarms of different size and density. We show that swarm echo sound pressure increases with 3dB for every doubling of insect number, irrespective of swarm density. Thus swarms will be much easier to detect than single insects. Many of the insects bats eat are so small that they are only detectable by echolocation at very short distances. By focusing on detection of swarms of insects, a bat may increase its operating range and diversify its diet. Interestingly, interference between the sound waves reflected from a swarm of insects can sometimes result in echoes that are much weaker than echoes from single insects. We show that bats can reduce this problem by increasing the bandwidth of their echolocation calls. Specifically, a bandwidth of 3–8 kHz would guarantee receiving loud echoes from any angle relative to the swarm. Indeed, many bat species, and specifically bats hunting in open spaces, where swarms are abundant, use echolocation signals with a bandwidth of several kHz. Our results might also explain how the first echolocating bats that probably had limited echolocation abilities, could detect insects through swarm hunting.

The Mechanistic Basis for Successful Spinal Cord Stimulation to Generate Steady Motor Outputs.

Electrical stimulation of the spinal cord is a promising rehabilitation intervention to restore/augment motor function after spinal cord injury (SCI). Combining sensory feedback with stimulation of remaining motor circuits has been shown to be a prerequisite for the functional improvement of SCI patients. However, little is known about the cellular mechanisms potentially underlying this functional benefit in the injured spinal cord. Here, we combine computer simulations with an isolated whole-tissue adult mouse spinal cord preparation to examine synaptic, cellular, and system potentials measured from single motoneurons and ventral roots. The stimulation protocol included separate and combined activation of the sensory inputs (evoked by dorsal root stimulation) and motor inputs (evoked by stimulation of spinal cord tissue) at different frequencies, intensities, and neuromodulatory states. Our data show that, while sensory inputs exhibit short-term depression in response to a train of stimulation, motor inputs exhibit short-term facilitation. However, the concurrent activation of both inputs elicits a stronger and steadier motor output. This effect is enhanced by the application of pharmacological neuromodulators. Furthermore, sensorimotor excitatory postsynaptic potentials (EPSPs) summate sublinearly (i.e., their combination produces an excitatory potential smaller than the sum of the excitatory potentials they would individually produce). However, ventral root compound action potentials (CoAPs) summate supralinearly generating much higher outputs. Computer simulations revealed that the contrasting summation and disproportionality in plasticity between the excitatory postsynaptic potentials (EPSPs) and CoAPs result from the motoneuronal firing threshold acting as an amplitude-selective filter. Together, these results provide the mechanistic basis for the cellular processes contributing to the generation of steady motor outputs using spinal stimulation. This data has great potential to guide the design of more effective stimulation protocols in SCI patients.

Automated shape optimisation of a plane asymmetric diffuser using combined Computational Fluid Dynamic simulations and multi-objective Bayesian methodology

An approach for shape optimisation of the flow through a diffuser is presented in this work. This multi-objective problem focuses on maximising the diffuser performance by simultaneously increasing the static pressure recovery across the geometry and the flow uniformity at the outflow. The hydrodynamic analysis of the geometry was conducted using the Computational Fluid Dynamics (CFD) software OpenFOAM, while a recently proposed multi-objective Bayesian approach was used for optimisation. The CFD and Bayesian methodology have been combined for fully automated operation using a Python-based framework. The proposed design parameterisation focuses on reshaping the diffuser in the expansion region. Catmull–Clark subdivision curves were employed to represent the shape of the diffuser wall; the influence of the number of control points (design points) for the curves on the optimum design was investigated. The optimal designs exhibit a reasonable performance improvement compared with the base design.

Determining the tractional forces on vitreoretinal interface using computer simulation and animal models to elucidate the retinal hemorrhage patterns in abusive head trauma

Pathophysiology of the retinal findings in abusive head trauma (AHT) is not clearly understood, but the role of vitreoretinal traction has been hypothesized. Combining computer simulation (CS) and animal models, we test the hypothesis that vitreo-retinal traction during forcible shaking can exceed the vitreo-retinal adhesion.

Research on dynamic compensation method for nonlinear characteristics of large aperture antenna

The nonlinear characteristics are widely existed in all aspects of the Servo Drive System. The influence on pointing and tracking accuracy of high precision of large aperture radio telescope cannot be neglected. In this paper, the common non-linear characteristics are discussed, and the negative effects of these characteristics on the system are analyzed as well as the advantages and disadvantages of common non-linear compensation methods. Xinjiang 110-meter Radio Telescope (QTT) requires high pointing accuracy, which has high requirements for stability and reliability of equipment. Here, a non-linear dynamic compensation method is proposed to prevent the non-linear characteristics of antenna Servo Drive System, so as to reduce or eliminate the lag and error of tracking and pointing, improve the stability of the system and optimize the motion performance of large aperture antenna. Nonlinear dynamic compensation method has simple structure, strong robustness and high applicability. In this paper, the saturated non-linearity of large aperture antenna is simulated by combining computer simulation with semi-physical platform experiment. The actual compensation effect of the method is verified by taking the QTT antenna to be the controlled object. Finally, the further improvement direction of the non-linear dynamic compensation method is also discussed.

The Innovative Integration of SBIRT raining Using Standardized Clients and Computer Simulation in Social Work Education

Through a Screening, Brief Intervention and Referral to Treatment (SBIRT) grant from NORC and the Conrad Hilton Foundation, the Department of Social Work at California University of Pennsylvania (Cal U) integrated SBIRT throughout the social work curriculum. A combination of computer simulation and standardized clients was developed to evaluate students’ skills in assessing for substance misuse and engaging with clients. Along with the development of students’ skills with assessment, motivational interviewing and active listening were an unintended outcome of student activism. Students, drawing from their new knowledge, training, and experiences, organized advocacy events on campus to promote awareness of the opioid epidemic impact on surrounding rural communities.

Symposium Speakers

Metabolic derailments associated with metabolic syndrome and type 2 diabetes can be studied with mixed meal tests (MMT’s). The plasma metabolome enriched with measurements of other biomarkers, such as hormones and cytokines, provide valuable information about the physiological state of an individual. An increasingly important, but complex task is to extract biomedical parameters with diagnostic value from large and multivariate datasets. We applied model-based data processing and analysis, combining computer simulation models of the human physiological system, stochastic models of uncertainties and machine learning of time-series data obtained from repeated blood sampling during MMT’s. In a mouse model of metabolic syndrome we identified differences in lipid metabolism to be associated with variation in weight gain and development of NAFLD (fatty liver disease). The computational model predicted the progression of dyslipidemia to be linked to bile acids, which was confirmed in a validation study including a larger group of mice that were followed for a longer period of time. To investigate the role of bile acids in humans with metabolic syndrome a detailed simulation model of bile acid metabolism and physiology was developed. The dozens of different bile acid species present in blood are to a large extent produced by gut bacteria. Model-based analysis of plasma bile acids provides a metabolic ‘window’ on the gut microbiome and other digestive processes in the gastrointestinal tract. The model was applied to simulate bariatric surgery in patients with metabolic syndrome. The model predicts changes in bile acid concentrations and dynamics in the small intestine to result in a stronger and faster GLP-1 response, hence insulin secretion, explaining observations of rapid glycemic improvement after surgery. The simulation model turned out to be sufficiently robust that personalized variants for individual patients could be made, using MMT plasma bile acid metabolomics as input.

Polygenic adaptation: From sweeps to subtle frequency shifts.

Evolutionary theory has produced two conflicting paradigms for the adaptation of a polygenic trait. While population genetics views adaptation as a sequence of selective sweeps at single loci underlying the trait, quantitative genetics posits a collective response, where phenotypic adaptation results from subtle allele frequency shifts at many loci. Yet, a synthesis of these views is largely missing and the population genetic factors that favor each scenario are not well understood. Here, we study the architecture of adaptation of a binary polygenic trait (such as resistance) with negative epistasis among the loci of its basis. The genetic structure of this trait allows for a full range of potential architectures of adaptation, ranging from sweeps to small frequency shifts. By combining computer simulations and a newly devised analytical framework based on Yule branching processes, we gain a detailed understanding of the adaptation dynamics for this trait. Our key analytical result is an expression for the joint distribution of mutant alleles at the end of the adaptive phase. This distribution characterizes the polygenic pattern of adaptation at the underlying genotype when phenotypic adaptation has been accomplished. We find that a single compound parameter, the population-scaled background mutation rate Θbg, explains the main differences among these patterns. For a focal locus, Θbg measures the mutation rate at all redundant loci in its genetic background that offer alternative ways for adaptation. For adaptation starting from mutation-selection-drift balance, we observe different patterns in three parameter regions. Adaptation proceeds by sweeps for small Θbg ≲ 0.1, while small polygenic allele frequency shifts require large Θbg ≳ 100. In the large intermediate regime, we observe a heterogeneous pattern of partial sweeps at several interacting loci.

A unified model for microtubule rescue.

How microtubules transition from depolymerization to polymerization, known as rescue, is poorly understood. Here we examine two models for rescue: 1) an “end-driven” model in which the depolymerizing end stochastically switches to a stable state; and 2) a “lattice-driven” model in which rescue sites are integrated into the microtubule before depolymerization. We test these models using a combination of computational simulations and in vitro experiments with purified tubulin. Our findings support the “lattice-driven” model by identifying repeated rescue sites in microtubules. In addition, we discover an important role for divalent cations in determining the frequency and location of rescue sites. We use “wash-in” experiments to show that divalent cations inhibit rescue during depolymerization, but not during polymerization. We propose a unified model in which rescues are driven by embedded rescue sites in microtubules, but the activity of these sites is influenced by changes in the depolymerizing ends.

A folding switch regulates interleukin 27 biogenesis and secretion of its α-subunit as a cytokine

A common design principle of heteromeric signaling proteins is the use of shared subunits. This allows encoding of complex messages while maintaining evolutionary flexibility. How cells regulate and control assembly of such composite signaling proteins remains an important open question. An example of particular complexity and biological relevance is the interleukin 12 (IL-12) family. Four functionally distinct αβ heterodimers are assembled from only five subunits to regulate immune cell function and development. In addition, some subunits act as independent signaling molecules. Here we unveil key molecular mechanisms governing IL-27 biogenesis, an IL-12 family member that limits infections and autoimmunity. In mice, the IL-27α subunit is secreted as a cytokine, whereas in humans only heterodimeric IL-27 is present. Surprisingly, we find that differences in a single amino acid determine if IL-27α can be secreted autonomously, acting as a signaling molecule, or if it depends on heterodimerization for secretion. By combining computer simulations with biochemical experiments, we dissect the underlying structural determinants: a protein folding switch coupled to disulfide bond formation regulates chaperone-mediated retention versus secretion. Using these insights, we rationally change folding and assembly control for this protein. This provides the basis for a more human-like IL-27 system in mice and establishes a secretion-competent human IL-27α that signals on its own and can regulate immune cell function. Taken together, our data reveal a close link between protein folding and immunoregulation. Insights into the underlying mechanisms can be used to engineer immune modulators.

Enhancing simulations with intra-subject variability for improved psychophysical assessments.

Psychometric properties of perceptual assessments, like reliability, depend on stochastic properties of psychophysical sampling procedures resulting in method variability, as well as inter- and intra-subject variability. Method variability is commonly minimized by optimizing sampling procedures through computer simulations. Inter-subject variability is inherent to the population of interest and cannot be influenced. Intra-subject variability introduced by confounds (e.g., inattention or lack of motivation) cannot be simply quantified from experimental data, as these data also include method variability. Therefore, this aspect is generally neglected when developing assessments. Yet, comparing method variability and intra-subject variability could give insights on whether effort should be invested in optimizing the sampling procedure, or in addressing potential confounds instead. We propose a new approach to estimate intra-subject variability of psychometric functions by combining computer simulations and behavioral data, and to account for it when simulating experiments. The approach was illustrated in a real-world scenario of proprioceptive difference threshold assessments. The behavioral study revealed a test-retest reliability of r = 0.212. Computer simulations without considering intra-subject variability predicted a reliability of r = 0.768, whereas the new approach including an intra-subject variability model lead to a realistic estimate of reliability (r = 0.207). Such a model also allows computing the theoretically maximally attainable reliability (r = 0.552) assuming an ideal sampling procedure. Comparing the reliability estimates when exclusively accounting for method variability versus intra-subject variability reveals that intra-subject variability should be reduced by addressing confounds and that only optimizing the sampling procedure may be insufficient to achieve a high reliability. This new approach allows computing the intra-subject variability with only two measurements per subject, and predicting the reliability for a larger number of subjects and retests based on simulations, without requiring additional experiments. Such a tool of predictive value is especially valuable for target populations where time is scarce, e.g., for assessments in clinical settings.