Optimal Parameter Space Research Articles

A new efficient imaging reconstruction method for muon scattering tomography

Muon scattering tomography (MST) is a promising technique in nuclear security inspection, utilizing its multiple scattering property which is sensitive to elements with high atomic numbers, and is also correlated with the material density. A proper imaging reconstruction method is essential for revealing the spatial distribution of materials within an object from measurements. This work proposes a new imaging reconstruction method called “big voxel and angle capping”. The scattering angle is reconstructed using the conventional Point of Closest Approach (PoCA) method. In the new method, reconstructed scattering angles belonging to voxels in an l×m×n matrix are aggregated into the center voxel, termed the “big voxel”. This significantly reduces statistical fluctuations in each big voxel. Subsequently, the influence of large scattering angles on determining material densities in big voxels is mitigated using a capping angle. The proposed algorithm is tested on the experimental data comprising approximately 1.8×104 muon scattering events detected in ∼ 24 h. The mean square error (MSE) and the structural similarity index measure (SSIM) indices are employed to quantitatively assess the imaging quality and optimize the new method. After scanning the two major parameters, the optimal parameter space is achieved with the big voxel size of ∼ 9, and the capping angle of ∼4∘ for tungsten blocks. The imaging quality is more sensitive to the capping angle than the big voxel size. An appropriate capping angle effectively removes large artifacts in reconstructed images caused by muon events with large scattering angles. This method is efficient in terms of both time and storage consumption.

Finding synchronization state of higher-order motif networks by dynamic learning

Synchronization of action potentials is one of the important phenomena for neural networks to achieve biological functions. How to find the optimal parameter space of neural networks in a synchronized state is of great significance for studying their synchronization. This study explores the parameter space of triplet motif-based higher-order networks in a synchronized state through the dynamic learning of synchronization (DLS) technique, which dynamically modulates the connection weights between motifs to alter their firing patterns. Our study delves into regular, Erdós-Rënyi random graphs, small-world, and scale-free networks, emphasizing the high-order motif interactions that characterize these networks. Our key findings indicate that the DLS technique successfully promotes synchronization within high-order motif networks with various connection patterns, although the degree of synchronization in networks where motifs are interconnected by chemical synapses is slightly weaker than those interconnected by electrical synapses. Additionally, we demonstrate the pattern of weight changes during the regulation of network firing states by DLS, finding that the evolution of weight distributions correlates with the network's topological structure. This work might provide new insights into complex network synchronization and lays the foundation for further exploration of using DLS technology to synchronize higher-order networks through external factors. Published by the American Physical Society 2024

Dual-indicators machine learning assisted processing high-quality laser-induced fluorine-doped graphene and its application on droplet velocity monitoring sensor

Laser-induced graphene (LIG) is widely used in electron devices owing to its characteristics of fine patterning and high precision. Doping is a commonly used approach to improve the quality of LIG. Thanks to the excellent superhydrophobicity and high electrical conductivity of laser-induced fluorine-doped graphene (F-LIG), it shows promising potential in developing electrodes for electron devices. However, it remains a major challenge to rapidly search for the optimal laser processing parameter space that can process high-quality F-LIG. In this work, a dual-indicators predictive machine learning (ML) method for optimizing the F-LIG process was proposed, utilizing a Gaussian process regression (GPR) algorithm. The mean square error (MSE) between the actual and predicted values of the data used for the model test was 0.00814, while the mean absolute percentage error (MAPE) was 10.33 %, demonstrating a certain degree of generalization without overfitting. Importantly, the F-LIG processed using parameters predicted by the ML model achieved excellent superhydrophobicity and conductivity simultaneously. Based on this high-quality F-LIG, a self-powered droplet velocity monitoring sensor was developed. This sensor can accurately work after 20,000 cycles, showing excellent stability. The proposed dual-indicator ML model assisted the optimization of the F-LIG process, which will provide a feasible and economical way to process high-quality graphene and related electron devices.

Calculation and prediction of divertor detachment via impurity seeding by using one-dimensional model

Abstract Achieving the divertor detachment helps to alleviate excessive heat load and sputtering problems on the target plates, thereby prolonging the lifetime of divertor components for fusion devices. In order to provide a rapid but relatively reliable prediction of plasma parameters along the flux tube for future devices design, a one-dimensional (1D) modeling code for the impurity seeded detached divertor operational point has been developed based on the Python language, which is a fluid model based on the previous work.[1] The experimental observation of the onset of divertor detachment by neon (Ne) and argon (Ar) seeding in EAST is well reproduced by using the 1D modeling code. The comparison of the 1D modeling and 2D simulation by SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows good consistency. The predictions of the radiative power loss and requirement of the corresponding impurity concentration for achieving divertor detachment via different impurity seeding for the high heating power scenarios on EAST and CFETR Phase II by the 1D model are also presented. Based on the predictions, the optimized parameter space for divertor detachment operation on EAST and CFETR has also been determined. Such a simple but reliable 1D model can provide a reasonable parameter inputs for a detailed and accurate analysis by 2D or 3D modeling tools through rapid parameter scanning.

Forming giant planets around late-M dwarfs: Pebble accretion and planet–planet collision

We propose a pebble-driven core accretion scenario to explain the formation of giant planets around the late-M dwarfs of M★=0.1– 0.2 M⊙. In order to explore the optimal disk conditions for giant planet, we performed N-body simulations to investigate the growth and dynamical evolution of both single and multiple protoplanets in the disks with both inner viscously heated and outer stellar irradiated regions. The initial masses of the protoplanets are either assumed to be equal to 0.01 M⊕ or calculated based on the formula derived from streaming instability simulations. Our findings indicate that massive planets are more likely to form in disks with longer lifetimes, higher solid masses, moderate to high levels of disk turbulence, and larger initial masses of protoplanets. In the single protoplanet growth cases, the highest planet core mass that can be reached is generally lower than the threshold necessary to trigger rapid gas accretion, which impedes the formation of giant planets. Nonetheless, in multi-protoplanet cases, the cores can exceed the pebble isolation mass barrier aided by frequent planet–planet collisions. This consequently speeds their gas accretion up and promotes giant planet formation, making the optimal parameter space to grow giant planets substantially wider. Taken together, our results suggest that even around very-low-mass stellar hosts, the giant planets with orbital periods of ≲100 days are still likely to form when lunar-mass protoplanets first emerge from planetesimal accretion and then grow rapidly by a combination of pebble accretion and planet–planet collisions in disks with a high supply of a pebble reservoir >50 M⊕ and a turbulent level of αt ~ 10−3−10−2.

Modelling of a Lyot filter based Mamyshev oscillator

Utilizing a Lyot filter (LF) as one of the spectral filters, a new architecture of Mamyshev Oscillator (MO) has been presented at the 1.06-µm region. The Ginzburg-Landau equation (GLE) has been solved numerically and the feasibility of obtaining a stable single pulse state from the oscillator has been checked. A possible architecture of the LF is presented and the tunability of the LF is achieved by introducing stress-induced birefringence (SIB). As the transmission window of the LF shifts, the spectral overlap between two filters also varies. Various types of states have been obtained as the solution of GLE depending on the spectral overlap between two filters. Attributes of each pulsing state have been presented and the intra-cavity spectral and temporal evolution has also been shown and discussed accordingly. The results confirm that in an optimized parameter space, stable single pulse state can be achieved from an LF-based MO and the pulse possesses properties of ‘amplifier similariton’. The compressibility of the output pulse has also been checked numerically and it is worth mentioning that the pulse can be compressed near to its transform-limited duration.

Impact of deoxygenation/reoxygenation processes on the superconducting properties of commercial coated conductors

We report the evolution of the superconducting properties of a commercial coated conductor during deoxygenation and reoxygenation processes. By analyzing the changes on the critical temperature, Tc, and critical current density, Jc, at 4 and 77 K, we have identified the conditions that cause a complete deoxygenation of the coated conductor and, also, the reoxygenation conditions that allow a recovery of the superconducting properties. A complete suppression of superconductivity happens at ~ 500–550 °C under a pure argon flow. After a complete deoxygenation, we observed that a reoxygenation process at ~ 400–450 °C in pure oxygen flow allows, not only a full recovery, but even an improvement in Jc, both at 4 and 77 K. Such an increase of Jc is kept or even enhanced, especially at 77 K, in the presence of magnetic fields up to ~ 6 T. A microstructural analysis by transmission electron microscopy did not give evidence of major differences in the densities of Y2O3 nanoparticles and stacking faults between the pristine and reoxygenated samples, suggesting that these defects should not be the cause of the observed enhancement of Jc. Therefore, the combined action of other types of defects, which could appear as a consequence of our reoxygenation process, and of a new level of oxygen doping should be responsible of the Jc enhancement. The higher Jc that can be achieved by using our simple reoxygenation process opens new parameter space for CCs optimization, which means choosing a proper pO2-temperature–time trajectory for optimizing Jc.

A universal rheological constitutive equation of magnetorheological fluids with a wide shear rate range

As an intelligent material, the magnetorheological fluid (MRF) has increased use in various fields, e.g., the semi-active suspension of vehicles. However, the efficient utilization and optimization of MRFs depend on a reliable constitutive relationship over a wide shear rate range, which existing models cannot fully and satisfactorily explain. This paper proposes a new non-Newtonian fluid constitutive equation that combines exponential and linear terms to describe the general characteristics of MRFs to tackle this problem. An effective parameter identification approach is realized by adopting model splitting to reduce the dimension of the optimized parameter space combined with a reasonable initial guess. A well-designed experiment for a commercial MRF is conducted to verify the proposed constitutive equation, indicating an improvement in accuracy of approximately 43 % compared with the Herschel-Bulkley model and its capability to reveal the rheological behaviors of MRFs in the wide shear rate range of 0.3–30000 s−1. The high compatibility between the proposed model and the Bingham model is supported by the high correlation coefficients of the dynamic yield stress and viscosity coefficient identified by this model and the existing data processing method based on the Bingham model, 0.9949 and 0.9160, respectively. Owing to its decoupled parameter structure, the extension to a multivariable framework has been implemented to consider the magnetorheological effect with good agreement. Finally, the application of magnetorheological device design has demonstrated the versatility and capacity of the proposed model for developing and utilizing a new generation of MRFs.

Extended depth of focus by self-imaging wavefront division with the mirror tunnel.

The mirror tunnel is a component used to extend the depth of focus for compact imaging probes used in endoscopic optical coherence tomography (OCT). A fast and accurate method for mirror tunnel probe simulation, characterization, and optimization is needed, with the aim of reconciling wave- and ray-optics simulation methods and providing a thorough description of the physical operating principle of the mirror tunnel. BeamLab software, employing the beam propagation method, was used to explore the parameter space and quantify lateral resolution and depth of focus extension. The lateral resolution performance was found to depend heavily on the metric chosen, implying that care should be taken in the interpretation of optimization and simulation results. Interpreting the mirror tunnel exit face as an extended object gives an understanding of the probe operation, decoupling it from the focusing optics and potentially helping to reduce the parameter space for future optimization.

Study of a new receiver-reactor cavity system with multiple mobile redox units for solar thermochemical water splitting

Solar thermochemical redox cycles could be a path to efficient, large-scale renewable hydrogen production. A new receiver-reactor concept is presented that combines characteristics of the most successful receiver-reactor systems to date, with features of concepts showing the highest efficiency potential. The key features of the system are movable reactive structures in combination with linear transportation systems and dedicated oxidation reactors. The application of several of these units allows the continuous operation of the receiver-reactor and permits the implementation of a solid–solid heat recovery system. Both of these characteristics are important to increase the system efficiency beyond the current state of technology. A numerical model is developed to simulate a basic cubic design version of the new concept and to analyse its performance. Parameter variations are studied amongst others for different cavity sizes, solar concentration factors and numbers of movable reactive structures. By avoiding the cyclic heating of the inert reactor vessel, the model predicts high efficiency values above 14% even for non-optimized designs. Furthermore, the basic concept of the heat recovery system is modelled with heat recovery rates of up to 20% in its most simple implementation. The new concept has the features required for highly performant systems and opens up a large parameter space for reactor design and operation optimization.

Quick approach for optimization of monodisperse microsphere synthesis with a knowledge sharing strategy powered by machine learning

In this communication, we proposed Bayesian optimization acceleration strategy by allowing machine-learned knowledge to flow across different reaction scales. Dispersion polymerization was conducted to validate this knowledge sharing approach. By learning a product uniformity landscape and performing virtual optimization using high-throughput small-scale reaction data, the most promising recipe subspace was extracted from the full-factorial parameter space for batch-size synthesis optimization. The subsequent Bayesian optimizer was hereby able to traverse the subspace and locate a recipe giving large uniform particles (>10 µm). This highly flexible and efficient strategy can be extended to a diverse library of reactions beyond microsphere synthesis.

Optimizing time-limited non-pharmaceutical interventions for COVID-19 outbreak control.

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible–infectious–recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies.This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model.

This paper presents a framework for automated optimization of double-heater convective PCR (DH-cPCR) devices by developing a computational fluid dynamics (CFD) simulation database and artificial neural network (ANN) model. The optimization parameter space that includes the capillary tube geometries and the heater sizes of DH-cPCR is established, and a database consisting of nearly 10,000 CFD simulations is constructed. The database is then used to train a two-stage ANN models that select practically relevant data for modeling and predict PCR device performance. The trained ANN model is then combined with the gradient-based and the heuristics optimization approaches to search for optimal device configuration that possesses the shortest DNA doubling time. The entire design process including model meshing and configuration, parallel CFD computation, database organization, and ANN training and utilization is fully automated. Case studies confirm that the proposed framework can successfully find the optimal device configuration with an error of less than 0.3 s, and hence, representing a cost-effective and rapid solution of DH-cPCR device design.

Neuroimaging Markers of Mal de Débarquement Syndrome.

Mal de débarquement syndrome (MdDS) is a motion-induced disorder of oscillating vertigo that persists after the motion has ceased. The neuroimaging characteristics of the MdDS brain state have been investigated with studies on brain metabolism, structure, functional connectivity, and measurements of synchronicity. Baseline metabolism and resting-state functional connectivity studies indicate that a limbic focus in the left entorhinal cortex and amygdala may be important in the pathology of MdDS, as these structures are hypermetabolic in MdDS and exhibit increased functional connectivity to posterior sensory processing areas and reduced connectivity to the frontal and temporal cortices. Both structures are tunable with periodic stimulation, with neurons in the entorhinal cortex required for spatial navigation, acting as a critical efferent pathway to the hippocampus, and sending and receiving projections from much of the neocortex. Voxel-based morphometry measurements have revealed volume differences between MdDS and healthy controls in hubs of multiple resting-state networks including the default mode, salience, and executive control networks. In particular, volume in the bilateral anterior cingulate cortices decreases and volume in the bilateral inferior frontal gyri/anterior insulas increases with longer duration of illness. Paired with noninvasive neuromodulation interventions, functional neuroimaging with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and simultaneous fMRI-EEG have shown changes in resting-state functional connectivity that correlate with symptom modulation, particularly in the posterior default mode network. Reduced parieto-occipital connectivity with the entorhinal cortex and reduced long-range fronto-parieto-occipital connectivity correlate with symptom improvement. Though there is a general theme of desynchronization correlating with reduced MdDS symptoms, the prediction of optimal stimulation parameters for noninvasive brain stimulation in individuals with MdDS remains a challenge due to the large parameter space. However, the pairing of functional neuroimaging and noninvasive brain stimulation can serve as a probe into the biological underpinnings of MdDS and iteratively lead to optimal parameter space identification.

Benchmarking polarizable and non-polarizable force fields for Ca2+-peptides against a comprehensive QM dataset.

Explicit description of atomic polarizability is critical for the accurate treatment of inter-molecular interactions by force fields (FFs) in molecular dynamics (MD) simulations aiming to investigate complex electrostatic environments such as metal-binding sites of metalloproteins. Several models exist to describe key monovalent and divalent cations interacting with proteins. Many of these models have been developed from ion-amino-acid interactions and/or aqueous-phase data on cation solvation. The transferability of these models to cation-protein interactions remains uncertain. Herein, we assess the accuracy of existing FFs by their abilities to reproduce hierarchies of thousands of Ca2+-dipeptide interaction energies based on density-functional theory calculations. We find that the Drude polarizable FF, prior to any parameterization, better approximates the QM interaction energies than any of the non-polarizable FFs. Nevertheless, it required improvement in order to address polarization catastrophes where, at short Ca2+-carboxylate distances, the Drude particle of oxygen overlaps with the divalent cation. To ameliorate this, we identified those conformational properties that produced the poorest prediction of interaction energies to reduce the parameter space for optimization. We then optimized the selected cation-peptide parameters using Boltzmann-weighted fitting and evaluated the resulting parameters in MD simulations of the N-lobe of calmodulin. We also parameterized and evaluated the CTPOL FF, which incorporates charge-transfer and polarization effects in additive FFs. This work shows how QM-driven parameter development, followed by testing in condensed-phase simulations, may yield FFs that can accurately capture the structure and dynamics of ion-protein interactions.

Methodology for optimizing composite design via biological pattern generation mechanisms

Mechanistic capabilities found in nature have influenced a variety of successful functional designs in engineering. However, the unique combinations of mechanical properties found in natural materials have not been readily adapted into synthetic materials, due to a disconnect between biological principles and engineering applications. Current biomimetic material approaches tend to involve mimicking nature's microstructure geometries or mimicking nature's adaptive design process through brute force element-by-element composite optimization techniques. While the adaptive approach promotes the generation of application-specific microstructure geometries, the element-by-element optimization techniques encompass a large design space that is directly related to the number of elements in the system. In contrast, a novel methodology is proposed in this paper that merges biological pattern generation mechanisms observed in the Gray-Scott model, with an evolutionary-inspired genetic algorithm to create adaptive bio-inspired composite geometries optimized for stiffness and toughness. The results reveal that this methodology significantly reduces the optimization parameter space from tens of thousands of parameters to only four or five. In addition, the resultant composite geometries improved upon the overall combination of stiffness and toughness by a factor of 13, when compared to published brute force element-by-element techniques.

Compact Ho:YLF-pumped ZnGeP2-based optical parametric amplifiers tunable in the molecular fingerprint regime.

We report on a compact mid-infrared laser architecture, comprising a chain of $ {\rm ZnGeP}_2 $ZnGeP2-based optical parametric amplifiers (OPAs), which afford a higher energy yield ($ \mathbin{\lower.3ex\hbox{$\buildrel \lt \over{\smash{\scriptstyle\sim}\vphantom{_x}}$}} 60\;\unicode{x00B5} {\rm J} $∼x<60µJ at 1 kHz) compared to most conventional OPA gain media transparent in the 2-8-µm wavelength range. Specifically, our OPA scheme allows ready tunability in the molecular fingerprint regime and is tailored for strong-field excitation and coherent control of both stretch and bend (or torsional) vibrational modes in molecules. The OPAs are pumped and directly seeded (via supercontinuum generation) by a 2-µm, 3-ps Ho:YLF regenerative amplifier. The compressibility of the OPA output is demonstrated by a representative measurement of the near-Gaussian temporal profile of a dispersion-compensated 105-fs idler pulse at a central wavelength of 5.1 µm, corresponding to ${\sim}6 $∼6 optical cycles. Detailed numerical simulations closely corroborate the experimental measurements, providing a benchmark and a platform to further explore the parameter space for future design, optimization, and implementation of high-energy, ultrafast, mid-infrared laser schemes.

Efficient Exploration of the Composition Space in Ternary Organic Solar Cells by Combining High‐Throughput Material Libraries and Hyperspectral Imaging

AbstractOrganic solar cells based on ternary active layers can lead to higher power conversion efficiencies than corresponding binaries, and improved stability. The parameter space for optimization of multicomponent systems is considerably more complex than that of binaries, due to both, a larger number of parameters (e.g., two relative compositions rather than one) and intricate morphology–property correlations. Most experimental reports to date reasonably limit themselves to a relatively narrow subset of compositions (e.g., the 1:1 donor/s:acceptor/s trajectory). This work advances a methodology that allows exploration of a large fraction of the ternary phase space employing only a few (&lt;10) samples. Each sample is produced by a designed sequential deposition of the constituent inks, and results in compositions gradients with ≈5000 points/sample that cover about 15%–25% of the phase space. These effective ternary libraries are then colocally imaged by a combination of photovoltaic techniques (laser and white light photocurrent maps) and spectroscopic techniques (Raman, photoluminescence, absorption). The generality of the methodology is demonstrated by investigating three ternary systems, namely PBDB‐T:ITIC:PC70BM, PTB7‐Th:ITIC:PC70BM, and P3HT:O‐IDFBR:O‐IDTBR. Complex performance‐structure landscapes through the ternary diagram as well as the emergence of several performance maxima are discovered.

Testing overidentifying restrictions with a restricted parameter space

We show that the standard test for testing overidentifying restrictions, which compares the J-statistic (Hansen, 1982) to χ2 critical values, does not control asymptotic size when the true parameter vector is allowed to lie on the boundary of the (optimization) parameter space. We also propose a modified J-statistic that, under the null hypothesis, is asymptotically χ2 distributed, such that the resulting test does control asymptotic size.

Optimal parameter(s) for the synthesis of nitrogen-vacancy (NV) centres in polycrystalline diamonds at low pressure

Nitrogen-vacancy (NV) centres in diamonds are emerging quantum materials having applications in quantum computing and magnetic field sensing. The ability to synthesize polycrystalline diamond films from chemical vapour deposition technique offers a possibility to grow cheap diamonds with NV centres over large areas. Till date, extensive studies have not been carried out to understand the influence of nitrogen flow rate on the formation of NV centres in polycrystalline diamonds. In this study, we investigate the effect of nitrogen flow rate on the morphology, optical, and electrical properties of polycrystalline diamonds deposited at low pressure. Several samples were prepared in different nitrogen flow regimes using the microwave plasma chemical vapour deposition (MPCVD) technique. The films were characterized using Raman spectroscopy and scanning electron microscopy (SEM). The I–V characteristics of the samples were measured using a point contact method at room temperature. Results obtained showed the formation of both neutral and negatively charged NV centres at an optimum nitrogen flow rate of 10 sccm. An increase in nitrogen flow rate led to a decrease in the electrical resistivity of the films. Furthermore, nitrogen flow rates greater than 20 sccm results to a decrease in the reflectance spectra of samples and a depreciation in the crystalline quality of films. This study is important in benchmarking an optimal parameter space for the growth of nitrogen doped polycrystalline diamonds suitable for sensing applications.