Well-defined Input Research Articles

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Several computational models have been introduced in recent years to yield comprehensive insights into microstructural evolution analyses. However, the identification of the correct input parameters to a simulation that corresponds to a certain experimental result is a major challenge on this length scale. To complement simulation results with experimental data (and vice versa) is not trivial since, e.g. simulation model parameters might lack a physical understanding or uncertainties in the experimental data are neglected. Computational costs are another challenge mesoscale models always have to face, so comprehensive parameter studies can be costly. In this paper, we introduce a surrogate model to circumvent continuum dislocation dynamics simulation by a data-driven linkage between well-defined input parameters and output data and vice versa. We present meaningful results for a forward surrogate formulation that predicts simulation output based on the input parameter space, as well as for the inverse approach that derives the input parameter space based on simulation as well as experimental output quantities. This enables, e.g. a direct derivation of the input parameter space of a continuum dislocation dynamics simulation based on experimentally provided stress–strain data.

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

STOCHASTIC POLYNOMIAL CHAOS EXPANSIONS TO EMULATE STOCHASTIC SIMULATORS

In the context of uncertainty quantification, computational models are required to be repeatedly evaluated. This task is intractable for costly numerical models. Such a problem turns out to be even more severe for stochastic simulators, the output of which is a random variable for a given set of input parameters. To alleviate the computational burden, surrogate models are usually constructed and evaluated instead. However, due to the random nature of the model response, classical surrogate models cannot be applied directly to the emulation of stochastic simulators. To efficiently represent the probability distribution of the model output for any given input values, we develop a new stochastic surrogate model called stochastic polynomial chaos expansions. To this aim, we introduce a latent variable and an additional noise variable, on top of the well-defined input variables, to reproduce the stochasticity. As a result, for a given set of input parameters, the model output is given by a function of the latent variable with an additive noise, thus a random variable. Because the latent variable is purely artificial and does not have physical meanings, conventional methods (pseudo-spectral projections, collocation, regression, etc.) cannot be used to build such a model. In this paper, we propose an adaptive algorithm that does not require repeated runs of the simulator for the same input parameters. The performance of the proposed method is compared to the generalized lambda model and a state-of-the-art kernel estimator on two case studies in mathematical finance and epidemiology and on an analytical example whose response distribution is bimodal. The results show that the proposed method is able to accurately represent general response distributions, i.e., not only normal or unimodal ones. In terms of accuracy, it generally outperforms both the generalized lambda model and the kernel density estimator.

A Reversible Hydropump–Turbine System

Water-pumped storage systems have become an ideal alternative to regulate the intermittent power delivered by renewable energy sources. For small-scale operations, a type of centrifugal pump coupled to asynchronous machines represents an adequate solution due to their techno-economic feasibility in addition to their ability to operate as reversible systems. This work provides a novel port-Hamiltonian modelling approach to an integrated reversible hydropump–turbine system, that can be switched from motor pump to turbine-generator by employing a conventional hydraulic switch. Our modelling strategy provides a clear physical interpretation of the energy flow from the mechanical to electrical domains. Then, the model was built with multi-domain storing and dissipating elements and the interconnection of well-defined input–output port pairs. The system’s internal energy, i.e., Hamiltonian function, can be exploited for energy-shaping control strategies. The performance of our modelling approach is validated via numerical simulations.

ILDM: An Interoperable Graph-Based Local Dynamic Map

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development.

Multi-criterion Intelligent Decision Support system for COVID-19

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is “if the disease can travel faster information has to travel even faster”. The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.

The Perspective of Total Lighting as a Key Factor to Increase the Sustainability of Strategic Activities

In the last decades, lighting has evolved from a branch of engineering ensuring safety and performance in indoor and outdoor installations, to a key discipline interacting with a wide spectrum of fields and having a deep impact on our daily lives. Although this evolution also applies to other areas of knowledge, the special features of lighting make its potential and also its limitations different. It is not the typical field where a well-established mathematical framework allows a departure from well-defined input and identifying clear effects and conclusions. The reason is that lighting is a field dealing with the interaction between a physical phenomenon and a physiological and psychological system, the human being. In addition to the complexity of its basis, the relationship between lighting and sustainability has become stronger in recent years. This relationship is bi-directional in some cases: on one hand, advanced societies require more and more complex lighting installations, which means high energy consumption, use of raw materials, financial costs, manufacturing and maintenance processes, waste and emissions to the atmosphere. On the other hand, good lighting has an impact on issues like productivity, well-being, happiness, disease avoidance, safety, and many other qualitative aspects whose direct or indirect impact on sustainability is remarkable. This work will analyze how lighting can give answers to questions related to sustainability, not only from the classic topics of energy consumption and waste management, but from a wider and global perspective. The results of these works are analyzed, and the basis of the new framework of total lighting, discussed.

Hardening model of severe plastically deformed AA2024 by high-pressure torsion

PurposeThis paper aims to evaluate the validity of bilinear hardening model to represent the stress flow of high-pressure torsion (HPT)-strengthened lightweight material, AA2024.Design/methodology/approachFinite-element HPT simulation was performed by applying a simultaneous prescribed displacement on the axial and rotational axis that is equivalent to 4 GPa pressure and 30° torsion. The material behaviour incorporates plasticity attributes with a bilinear constitutive equation that consists of elastic and tangent modulus.FindingsAs a result, the von Mises stress generated from the simulation is in good agreement with the experiment, indicating that the assumptions of plasticity properties applied for the FEM simulation model are acceptable. The model verification confirms the anticipated plasticity parameters’ effect on the generated von Mises stress. The disc centre also evidenced an insignificant stress increment due to the limited shear straining.Research limitations/implicationsA reliable hardening model would assist in understanding the stress flow associated with mechanical properties enhancement.Practical implicationsThe bilinear hardening model exhibits a satisfactory stress estimation. It simplifies the ideal strain variable hardening procedures and lessens the total computation time that is valuable in solving severe plastic deformation problems.Originality/valueAn integration of well-defined input parameters, concerning the hardening behaviour and the plasticity properties, contributes to the establishment of a validated HPT simulation model, particularly for AA2024. This study also proved that perfectly plastic behaviour is inappropriate to represent hardening in the HPT-strengthened materials due to the remarkable stress deviation from the experimental data.

Mathematical Models of Protease-Based Enzymatic Biosensors.

An important goal of synthetic biology is to build biosensors and circuits with well-defined input-output relationships that operate at speeds found in natural biological systems. However, for molecular computation, most commonly used genetic circuit elements typically involve several steps from input detection to output signal production: transcription, translation, and post-translational modifications. These multiple steps together require up to several hours to respond to a single stimulus, and this limits the overall speed and complexity of genetic circuits. To address this gap, molecular frameworks that rely exclusively on post-translational steps to realize reaction networks that can process inputs at a time scale of seconds to minutes have been proposed. Here, we build mathematical models of fast biosensors capable of producing Boolean logic functionality. We employ protease-based chemical and light-induced switches, investigate their operation, and provide selection guidelines for their use as on-off switches. As a proof of concept, we implement a rapamycin-induced switch in vitro and demonstrate that its response qualitatively agrees with the predictions from our models. We then use these switches as elementary blocks, developing models for biosensors that can perform OR and XOR Boolean logic computation while using reaction conditions as tuning parameters. We use sensitivity analysis to determine the time-dependent sensitivity of the output to proteolytic and protein-protein binding reaction parameters. These fast protease-based biosensors can be used to implement complex molecular circuits with a capability of processing multiple inputs controllably and algorithmically. Our framework for evaluating and optimizing circuit performance can be applied to other molecular logic circuits.

On the Design of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math> </inline-formula>th-Order Polyphase All-Pass Filters

A structure for $n$ th-order polyphase all-pass filters (PAFs) is proposed that improves the tradeoff between output phase accuracy and filter loss. The filter also has a well-defined input impedance, which is independent of the phase accuracy and can generate the exact desired phase at $n$ distinct frequencies. The proposed structure along with its design approach is analyzed. The design procedure to design a second, third, and fourth-order PAFs is presented. It is shown that by increasing the order of the PAF structure, one can either improve the phase accuracy of the structure over the same bandwidth of interest or achieve the same phase accuracy over a wider bandwidth. Practical design considerations, including the effect of the parasitic capacitances, component tolerance, and inductor quality factors on the output amplitude imbalance and input impedance, are analyzed and discussed. As a proof-of-concept, experimental results are presented to show that the fourth-order structure is able to generate quadrature signals at four distinct frequencies.

A Hybrid Artificial Neural Network Model for Option Pricing

In the absence of a well-defined input selection technique associated with the pure ANN models, Option pricing using pure ANN models while relaxing the assumption of constant volatility remains a challenge. The conservative drill espoused has been to make allowances for a large number of input lags with the confidence that the ability of ANN to integrate suppleness and redundancy generates a more robust model. This is to say that the nonexistence of input selection criteria notwithstanding, the models have been developed without due consideration to the effect that the choice of input selection technique would have on model complexity, learning difficulty and performance measures. In this study, we deviate from the conventional techniques applied by the pure ANN option price models and adopt the hybrid model in which the volatility component is handled using some celebrated time series models, with speci?city to the ANN-GJR-GARCH model - a hybrid of the ANN and a time series hybrid. The hybrid ANN option pricing model is then framed and tested with the forecasts of the ANN-GJR-GARCH model as a volatility input alongside two other inputs - time to maturity and moneyness. Finally, we compare the performance of the hybrid model developed with that of a pure ANN model. Results indicate that the hybrid model outperforms the pure ANN model not only in forecasting but also in the training time and model complexity.

Shaping the vibro-isolation properties of horizontal seat suspension

In this paper, an effective procedure of shaping the vibro-isolation properties of seat suspension system is presented. The simulation model is created to determine the dependence of evaluation criteria to the design parameters by means of which the system characteristics are formed. The developed optimisation procedure allows to find Pareto-optimal system configuration for the conflicted vibro-isolating criteria, i.e. the frequency weighted transmissibility factor used to evaluate dynamic seat comfort based on acceleration signals and the suspension travel. In order to optimise both the conflicted vibro-isolation criteria, a minimising of the transmissibility factor (primary criterion) is proposed taking into account the suspension travel that is transferred to a non-linear inequality constraint. The correctness of proposed procedure is evaluated using experimental research of the best solution of horizontal seat suspension. This research is performed using the passive system with optimal visco-elastic characteristics, which are selected especially for the well-defined input vibration. The satisfactory agreement of experimental and numerical results is obtained for the analysed vibration reduction system.

Inter-comparison of Hauser-Feshbach model codes toward better actinide evaluations

The Hauser-Feshbach codes, EMPIRE, TALYS, CCONE, and CoH3 , which are widely utilized in nuclear data evaluations, are compared, with a particular focus on neutron-induced reactions on major actinides. We report the results of Hauser-Feshbach calculations using well-defined input parameters, and discuss the differences among these codes.

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and particle size. In conclusion, it was shown that all three software packages are useful to guide formulation development. However, as a consequence of the high fraction of inaccurate predictions (prediction error >2-fold) and the clear trend toward decreased accuracy with decreased predicted fabs observed with Simcyp, the results indicate that GI-Sim and GastroPlus perform better than Simcyp in predicting the intestinal absorption of the incompletely absorbed drugs when a higher degree of accuracy is needed. In addition, this study suggests that modeling and simulation research groups should perform systematic model evaluations using their own input data to maximize confidence in model performance and output.

Exciton-mediated quantum search on a star graph

A fast and efficient quantum search algorithm is established by using the ability of an exciton to propagate along a star graph that exhibits two identical energetic defects. The first defect lies on the well-defined input site where the exciton is initially created, whereas the second defect occupies the target site whose unknown position must be determined. It is shown that when the energetic defects are judiciously chosen, specific quantum interferences arise so that the probability to observe the exciton on the target site becomes close to unity at a very short time $$t^{*}$$t?. Consequently, a measurement of the exciton quantum state at time $$t^{*}$$t? will reveal the identity of the position of the target site. The key point is that $$t^{*}$$t? is the shortest time independent on the size of the graph that is physically accessible to the exciton to tunnel.

Multi-mode Control in a Two-stage Piezoelectric Transducer for an Application of Physical Therapy

A novel as well as unique piezoelectric-driven system constructed with two sets of ceramic actuators connected in a series is able to produce both mechanical vibration and electric power at the output end. With power input to the first ceramic actuator to oscillate, the connected second one makes use of the transmitted mechanical vibration energy to generate electricity via piezoelectric direct effect. An application example of physical therapy including ultrasonic diathermy and electrotherapy is selected for further investigation in this article. Different from the traditional ultrasonic diathermy and electrotherapy systems that are built with their own specific method and independent structure, the developed two-stage piezoelectric transducer can achieve the same application purpose alone. Multi-mode control scheme is proposed by using Frequency- Hopping method to oscillate the first ceramic actuator with different vibration amplitude at various resonance frequencies under schematic switching process. Hence, the second ceramic actuator receives the transmitted energy and generates equivalent electricity with modulation amplitude signal for electrotherapy. The residual energy in the first ceramic actuator is also collected at the output end for ultrasonic diathermy. From the satisfactory experimental outcome, it indicates such a two-stage system not only can generate vibration and electricity but produce composite effect for desired application purpose under well-defined input control signal.

Performance of SEM scintillation detector evaluated by modulation transfer function and detective quantum efficiency function

In the paper, the SEM detector is evaluated by the modulation transfer function (MTF) which expresses the detector's influence on the SEM image contrast. This is a novel approach, since the MTF was used previously to describe only the area imaging detectors, or whole imaging systems. The measurement technique and calculation of the MTF for the SEM detector are presented. In addition, the measurement and calculation of the detective quantum efficiency (DQE) as a function of the spatial frequency for the SEM detector are described. In this technique, the time modulated e-beam is used in order to create well-defined input signal for the detector. The MTF and DQE measurements are demonstrated on the Everhart-Thornley scintillation detector. This detector was alternated using the YAG:Ce, YAP:Ce, and CRY18 single-crystal scintillators. The presented MTF and DQE characteristics show good imaging properties of the detectors with the YAP:Ce or CRY18 scintillator, especially for a specific type of the e-beam scan. The results demonstrate the great benefit of the description of SEM detectors using the MTF and DQE. In addition, point-by-point and continual-sweep e-beam scans in SEM were discussed and their influence on the image quality was revealed using the MTF.

Image Enhancement for the 3-D Reconstruction in the Uncontrolled Environment using Shape from Silhouette

Among the multiple models for 3-D shape reconstruction, Shape from silhouette (SFS) is one of the fast and simple 3D shape rendering techniques as compare to other approaches. In SFS model multiple images captured from different viewpoints in a controlled environment are used as input data at the front end to extract silhouette and are free of noises. Silhouette extraction from such well-defined input data is easy and accurate, having no loss of information while extracting the silhouette. On the other hand images from uncontrolled environment involve many degradation factors. Common and frequently degradation factors are motion blur and noise addition which effects acquired image quality, illumination and resolution seriously. The proposed work is an effort to extract useful information from such environmentally variant images. The successful reconstruction of the image is main emphasis.

Detection and tracing of the medical radioisotope131I in the Canberra environment

The transport and radioecology of the therapeutical radioisotope 131 I has been studied in Canberra, Australia. The isotope has been detected in water samples and its activity quantified via characteristic J-ray photo peaks. A comparison of measurements on samples from upstream and downstream of the Canberra waste water treatment plant shows that 131 I is discharged from the plant outflow into the local Molonglo river. This is consistent with observations in other urban environments. A time-correlation between the measured activities in the outflow and the therapeutical treatment cycle at the hospital identifies the medical treatment as the source of the isotope. Enhanced activity levels of 131 I have been measured for fish samples. This may permit conclusions on 131 I uptake by the biosphere. Due to the well-defined and intermittent input of 131 I into the sewage, the Canberra situation is ideally suited for radioecological studies. Furthermore, the 131 I activity may be applied in tracer studies of sewage transport to and through the treatment plant and as an indicator of outflow dilution following discharge to the environment.

Robust Design of Advanced Thermoelectric Conversion Systems: Probabilistic Design Impacts on Specific Power and Power Flux Optimization

AbstractAdvanced, direct thermal energy conversion technologies are receiving increased research attention in order to recover waste thermal energy in advanced vehicles and industrial processes. Advanced thermoelectric (TE) systems necessarily require integrated system-level analyses to establish accurate optimum system designs. Past system-level design and analysis has relied on well-defined deterministic input parameters even though many critically important environmental and system design parameters in the above mentioned applications are often randomly variable, sometimes according to complex relationships, rather than discrete, well-known deterministic variables. This work describes new research and development creating techniques and capabilities for probabilistic design and analysis of advanced TE power generation systems to quantify the effects of randomly uncertain design inputs in determining more robust optimum TE system designs and expected outputs. Selected case studies involving stochastic TE .material properties demonstrate key stochastic material impacts on power, optimum TE area, specific power, and power flux in the TE design optimization process. Magnitudes and directions of these design modifications are quantified for selected TE system design analysis cases.