Optimal Sampling Research Articles

Quantitative, real-time scintillation imaging for experimental comparison of different dose and dose rate estimations in UHDR proton pencil beams.

Ultra-high dose rate radiotherapy (UHDR-RT) has demonstrated normal tissue sparing capabilities, termed the FLASH effect; however, available dosimetry tools make it challenging to characterize the UHDR beams with sufficiently high concurrent spatial and temporal resolution. Novel dosimeters are needed for safe clinical implementation and improved understanding of the effect ofUHDR-RT. Ultra-fast scintillation imaging has been shown to provide a unique tool for spatio-temporal dosimetry of conventional cyclotron pencil beam scanning (PBS) deliveries, indicating the potential use for characterization of UHDR PBS proton beams. The goal of this work is to introduce this novel concept and demonstrate its capabilities in recording high-resolution dose rate maps at FLASH-capable proton beam currents, as compared to log-based dose rate calculation, internally developed UHDR beam simulation, and a fast point detector (EDGE diode). The light response of a scintillator sheet located at isocenter and irradiated by PBS proton fields (40-210 nA, 250 MeV) was imaged by an ultra-fast iCMOS camera at 4.5-12 kHz sampling frequency. Camera sensor and image intensifier gain were optimized to maximize the dynamic range; the camera acquisition rate was also varied to evaluate the optimal sampling frequency. Large field delivery enabled flat field acquisition for evaluation of system response homogeneity. Image intensity was calibrated to dose with film and the recorded spatio-temporal data was compared to a PPC05 ion chamber, log-based reconstruction, and EDGE diode. Dose and dose rate linearity studies were performed to evaluate agreement under various beam conditions. Calculation of full-field mean and PBS dose rate maps were calculated to highlight the importance of high resolution, full-field information in UHDRstudies. Camera response was linear with dose (R2 = 0.997) and current (R22 = 0.98) in the range from 2-22 Gy and 40-210 nA, respectively, when compared to ion chamber readings. The deviation of total irradiation time calculated with the imaging system from the log file recordings decreased from 0.07% to 0.03% when imaging at 12 kfps versus 4.5 kfps. Planned and delivered spot positions agreed within 0.2 0.1 mm and total irradiation time agreed within 0.2 0.2 ms when compared with the log files, indicating the high concurrent spatial and temporal resolution. For all deliveries, the PBS dose rate measured at the diode location agreed between the imaging and the diode within 3% 2% and with the simulation within 5% 3% CONCLUSIONS: Full-field mapping of dose and dose rate is imperative for complete understanding of UHDR PBS proton dose delivery. The high linearity and various spatiotemporal metric reporting capabilities confirm the continued use of this camera system for UHDR beam characterization, especially for spatially resolved dose rateinformation.

Boosting the high-temperature graphitization of non-graphitizable carbons by ball-milling

The high crystallinity structure endows carbon materials with exceptional electrical and thermal conductivity, making them suitable for high-end industrial applications, such as graphite for lithium-ion battery. However, the non-graphitizable carbons are hardly converted to graphite even at 3000 °C due to their highly disordered nanotexture. Herein, we propose that re-structuring non-graphitizable carbons through ball-milling can significantly boost their crystallinity after 2800 °C treatment using a activated carbon as a research model. Ball milling-induced order rearrangement of carbon layers promotes the development of the large graphenic crystallites toward graphite structure during the subsequent heat treatment. As a utilization exemplification for lithium storage, the optimal sample shows a greatly improved plateau capacity (<0.2 V, 194.8 vs. 155.1 mAh g−1) compared to the one obtained without ball-milling pre-modulation. Furthermore, the same approach was also applied to three typical non-graphitizable carbons derived from polymers and biomass, showing a similar result. This indicates that ball-milling may be a universal pre-treatment strategy for boosting the high-temperature “graphitization” of non-graphitizable carbon materials for advanced applications.

One-step thermal oxidation synthesis of carbon-doped TiO2 with enhanced photocatalytic activity using CO2 as the carbon source

The study proposed a simplified carbon doping synthesis strategy to enhance the photocatalytic activity of titanium dioxide (TiO2). Utilizing CO2 gas as the carbon source, the material was directly synthesized onto a titanium substrate through a one-step thermal oxidation method. Raman and XPS analyses indicated that with the increase in CO2 partial pressure, the influence of the carbon source was amplified, resulting in carbon doping in a graphite-like structure and the induction of oxygen vacancies. Electrochemical impedance spectroscopy and UV–vis spectra demonstrated that the introduced carbon species acted as a photosensitizer, augmenting the transfer of photogenerated carriers. Photoluminescence spectra suggested that oxygen vacancies could enhance the non-radiative scattering of photogenerated carriers. These two effects synergistically improved the photocatalytic activity of TiO2 significantly, although the degree of enhancement was dependent on the optimized ratio of carbon content to oxygen vacancy content. Only with optimal carbon content and oxygen vacancy levels could the undesired radiative recombination of photogenerated carriers be prevented, ensuring that the energy derived from visible light was effectively utilized to drive chemical reactions. Under these optimal conditions, the TC-O5 and TC-O10 samples exhibited excellent photocatalytic degradation performance of methyl orange under visible light, with degradation rates of 86.27% and 82.54%, respectively, and displayed outstanding cycling stability, with the degradation rate remaining above 90% for three cycles. This method proved to be simple, efficient, and easily operable, offering significant advantages for industrial-scale preparation.

In Vitro Development of Mouse Preantral Follicle with Using Amniotic Membrane Extract-Loaded Hydrogels.

An artificial ovary based on the alginate (ALG) hydrogel has been widely implemented to preserve prepubertal female fertility. However, this platform is not fully capable of successful an ovary microenvironment simulation for follicle development, holding great potential for its improvement. Therefore, this experimental study aimed to evaluate the effect of an amniotic membrane extract (AME) -loaded hydrogel on the mouse preantral follicles in vitro development. In order to have better follicle development, first, the impact of different concentrations of follicle-stimulating hormone (FSH) was evaluated on the mouse preantral follicles encapsulated in ALG. Later, the appropriate dose was adjusted for the follicles encapsulated in the ALG-AME hydrogel. Results demonstrated that 100 mIU/ml FSH showed a significant follicle survival rate compared with 10 mIU/ml FSH (P=0.005). According to MTT assay finding, the rate of weight loss, and rheology evaluations, ALG containing 1 mg/ml AME was identified as an optimal sample of follicle culture instead of other AME concentrations. Follicle diameter significantly increased in the ALG-AME 1 hydrogel compared with the ALG control group without AME (P=0.027). The storage modulus of ALG-AME 1 was 773 Pa and retained the follicle morphology for 13 days. No statistically substantial difference was seen in survival, antrum cavity formation, and competent oocyte in terms of the normal chromosomal arrangement and meiotic spindle rate in comparison with the control group. It can be concluded that ALG-AME 1 could not significantly impact the mouse preantral follicle.

Insights into the preparation and photoluminescence properties of MgTi2O5: Eu3+ red phosphor with high color purity for w-LED applications

As one of the critical components for achieving high-quality white light emitting diodes (w-LEDs) are efficient phosphors, a series of intense red emitting Eu3+doped magnesium dititanate (Mg(1-x)Ti2O5: xEu3+; x = 0, 0.005, 0.008, 0.01, 0.02 and 0.03 mol) phosphors were prepared via solid-state reaction method. The X-ray diffraction (XRD) patterns confirmed the phase purity and orthorhombic structure of the prepared phosphors. The Ti-O functional groups were detected from the Fourier transform infrared (FTIR) spectra and Field emission scanning electron microscopy (FESEM) images revealed agglomerated spherical-like shaped phosphor particles. Under 465 nm excitation, intense red emission corresponding to 5D0→7FJ (J = 0, 1, 2, 3, and 4) transitions of Eu3+ ions were recorded in the photoluminescence (PL) spectra with an intense peak at 615 nm. The optimum concentration of Eu3+ ions was determined to be 1 mol% and the concentration quenching mechanism was due to the interaction between adjacent Eu3+ ions. The lifetime values of the samples were in the millisecond range and the internal quantum efficiency was determined to be 31.65 %. The Commission Internationale de l'Eclairage (CIE) coordinates of the optimum sample Mg0.99Ti2O5:0.01Eu3+ are (0.6421, 0.3575), lies in the red region with 99.9 % color purity and have warm correlated color temperature (CCT) values. Therefore obtained results suggest that the prepared Mg(1-x)Ti2O5:xEu3+phosphors can be used as an efficient red phosphor for w-LED applications.

A Knowledge-Guided Multi-Objective Shuffled Frog Leaping Algorithm for Dynamic Multi-Depot Multi-Trip Vehicle Routing Problem

Optimization algorithms have a wide range of applications in symmetry problems, such as graphs, networks, and pattern recognition. In this paper, a dynamic periodic multi-depot multi-trip vehicle routing model for scheduling test samples is constructed, which considers the differences in testing unit price and testing capacity of various agencies and introduces a cross-depot collaborative transport method. Both the cost and the testing time are minimized by determining the optimal sampling routes and testing agencies, subjecting to the constraints of vehicle capacity, number of vehicles, and delivery time. To solve the model, a knowledge-guided multi-objective shuffled frog leaping algorithm (KMOSFLA) is proposed. KMOSFLA adopts a convertible encoding mechanism to realize the diversified search in different search spaces. Three novel strategies are designed: the population initialization with historical information reuse, the leaping rule based on the greedy crossover and genetic recombination, and the objective-driven enhanced search. Systematic experimental studies are implemented. First, feasibility analyses of the model are carried out, where effectiveness of the cross-depot collaborative transport is validated and sensitivity analyses on two parameters (vehicle capacity and proportion of the third-party testing agencies) are performed. Then, the proposed algorithm KMOSFLA is compared with five state-of-the-art algorithms. Experimental results indicate that KMOSFLA can provide a set of non-dominated schedules with lower cost and shorter testing time in each scheduling period, which provides a reference for the dispatcher to make a final decision.

Efficient augmented radial basis function for reliability analysis with dynamic importance sampling

Conducting reliability analysis on complex systems, particularly those characterized by minimal failure probabilities and computationally intensive models, presents significant challenges. This article introduces a reliability analysis method combining the adaptive augmented radial basis function and the dynamic importance sampling (ARBF-DIS) algorithm. This method improves the generalization capacity of the augmented radial basis function at both local and global scales through the application of an anisotropic technique and orthogonal conditions. By using efficient cross-validation, the methodology facilitates the training of the metamodel, enabling it to provide predictive variance essential for constructing an adaptive sampling learning function. A key aspect of this approach is its capability to address the issue of low failure probabilities through dynamic importance sampling, which involves calculating an optimal importance sampling function derived from the current metamodel. The effectiveness and precision of the ARBF-DIS method are demonstrated through two analytical examples and a solid rocket motor reliability problem.

Assessing eDNA capture method from aquatic environment to optimise recovery of human mt-eDNA

Previous studies have shown that environmental DNA (eDNA) from human sources can be recovered from natural bodies of water, and the generation of DNA profiles from such environmental samples may assist in forensic investigations. However, fundamental knowledge gaps exist around the factors influencing the probability of detecting human eDNA and the design of optimal sampling protocols. One of these is understanding the particle sizes eDNA signals are most strongly associated with and the most appropriate filter size needed for efficiently capturing eDNA particles. This study assessed the amount of mitochondrial eDNA associated with different particle sizes from human blood and skin cells recovered from freshwater samples. Samples (300 mL) were taken from experimental 10 L tanks of freshwater spiked with 50 µL of human blood or skin cells deposited by vigorously rubbing hands together for two minutes in freshwater. Subsamples were collected by passing 250 mL of experimental water sample through six different filter pore sizes (from 0.1 to 8 µm). This process was repeated at four time intervals after spiking over 72 hours to assess if the particle size of the amount of eDNA recovered changes as the eDNA degrades. Using a human-specific quantitative polymerase chain reaction (qPCR) assay targeting the HV1 mitochondrial gene region, the total amount of mitochondrial eDNA associated with different particle size fractions was determined. In the case of human blood, at 0 h, the 0.45 µm filter pore size captured the greatest amount of mitochondrial eDNA, capturing 42 % of the eDNA detected. The pattern then changed after 48 h, with the 5 µm filter pore size capturing the greatest amount of eDNA (67 %), and 81 % of eDNA at 72 h. Notably, a ten-fold dilution proved to be a valuable strategy for enhancing eDNA recovery from the 8 µm filter at all time points, primarily due to the PCR inhibition observed in hemoglobin. For human skin cells, the greatest amounts of eDNA were recovered from the 8 µm filter pore size and were consistent through time (capturing 37 %, 56 %, and 88 % of eDNA at 0 hours, 48 hours, and 72 hours respectively). There is a clear variation in the amount of eDNA recovered between different cell types, and in some forensic scenarios, there is likely to be a mix of cell types present. These results suggest it would be best to use a 5 µm filter pore size to capture human blood and an 8 µm filter pore size to capture human skin cells to maximize DNA recovery from freshwater samples. Depending on the cell type contributing to the eDNA, a combination of different filter pore sizes may be employed to optimize the recovery of human DNA from water samples. This study provides the groundwork for optimizing a strategy for the efficient recovery of human eDNA from aquatic environments, paving the way for its broader application in forensic and environmental sciences.

A novel highly dispersed calcium silicate hydrate nanosheets for efficient high-concentration Cu2+ adsorption

Currently, the low cost and effective purification toward heavy metal ions in wastewater has garnered global attention. Herein, we used hydrothermal method to prepare highly dispersed calcium silicate hydrate in fluorite tailings. And the stacking thickness of calcium silicate hydrate layered morphology was less than 5 nm. For high concentration Cu2+ purification investigation in wastewater, we found that the equilibrium adsorption capacity reached 797.92 mg/g via the CSH with 3:2 Ca/Si molar ratio, be 1.43–21.8 times than that of reported data. Therein, the metal-metal exchange and deposition are the primary pathways for Cu2+ adsorption, and electrostatic attraction is the secondary pathway. And the relative ∼100 % removal rate of high-concentration Ni2+ and Cr3+ ions were confirmed via CSH prepared from different tailings. This method offers a cost-effective way to utilize tailings for preparing highly efficient adsorbents toward HMIs removal in wastewater.

Fisher information for smart sampling in time-domain spectroscopy.

Time-domain spectroscopy encompasses a wide range of techniques, such as Fourier-transform infrared, pump-probe, Fourier-transform Raman, and two-dimensional electronic spectroscopies. These methods enable various applications, such as molecule characterization, excited state dynamics studies, or spectral classification. Typically, these techniques rarely use sampling schemes that exploit the prior knowledge scientists typically have before the actual experiment. Indeed, not all sampling coordinates carry the same amount of information, and a careful selection of the sampling points may notably affect the resulting performance. In this work, we rationalize, with examples, the various advantages of using an optimal sampling scheme tailored to the specific experimental characteristics and/or expected results. We show that using a sampling scheme optimizing the Fisher information minimizes the variance of the desired parameters. This can greatly improve, for example, spectral classifications and multidimensional spectroscopy. We demonstrate how smart sampling may reduce the acquisition time of an experiment by one to two orders of magnitude, while still providing a similar level of information.

Cetyltrimethylammonium bromide induced generation of highly (200) planes-exposed LiMnPO4 nanosheets coated by nitrogen-doped carbon for high-performance batteries

A simple solvothermal method is designed to synthesize LiMnPO4/N-doped C composites with core-shell structure using cetyltrimethylammonium bromide (CTAB) as surfactant and nitrogen source. The effectively growth of (200) planes and nitrogen-doped carbon layer coated LiMnPO4 nanosheets are obtained. The doped nitrogen induces the generation of active defect sites, which changes the cell electronic layer structure, and improve the electronic conductivity and diffusion rate of lithium ions. Thus, the electrochemical performance is beneficial from the synergistic effect of specific planar exposure and particular interfacial modifications. The optimal sample shows the supreme electrochemical performance at 0.1C, 1C and 5C with capacities of 159.1, 122.6 and 83.1 mAh g−1, respectively. The capacity retention can achieve 98 % even after 200 cycles at 1C. This work provides a simple but effective strategy to design polyanion cathode materials with enhanced electrochemical performance.

Insulation coating via hydrothermal oxidation for high-frequency FeSiAl soft magnetic composites

Soft magnetic composites (SMCs) play a crucial role in electronic devices owing to their high saturation magnetization. However, conventional insulation methods employed in SMCs exhibit inadequate insulation reliability, thereby restricting their further application in high frequencies. In this study, hydrothermal oxidation is introduced for insulation coating in the preparation of FeSiAl SMCs. When treated at 150 ℃ for 2 h, an insulation layer comprising of Al2O3, SiO2, Fe3O4 and Fe2O3 is formed on the surface of FeSiAl. The most optimal sample is achieved with saturation magnetization of 134.3 emu/g, presenting no significant reduction in saturation magnetization compared to the untreated sample. Cut-off frequency exceeding 300 MHz, and power loss of 503.7 kW/m3 (@7 MHz, 5 mT) are also obtained. This study suggests that hydrothermal oxidation could be a dependable method for fabricating high-frequency SMCs.

A Schottky heterojunction with spatially separated active sites for piezo-photocatalytic dual-channel hydrogen peroxide generation

Mechanical and solar energy-driven piezo-photocatalysis has drawn growing attention for H2O2 synthesis and related applications. However, most reported works focused on single pathway and ignored simultaneous oxygen reduction and water oxidation for H2O2 generation. Herein, Au-ZnO Schottky heterojunction is constructed for piezo-photocatalytic H2O2 synthesis via dual-channel pathway (simultaneous oxygen reduction and water oxidation). Experimental results and density functional theory calculations illustrated that the reduction and oxidation reactions predominantly take place at Au and ZnO sites, respectively. The spatially separated active sites avoid site competition, and enable a synergistic effect of oxidation and reduction. Furthermore, Au nanoparticles not only provide hot electrons via surface plasmon resonance effect, but also form Schottky junctions with ZnO to decrease the charge shielding effect. As a result, a H2O2 generation of 186 μM in pure water (30 min) is achieved by the optimal sample. This study provides a strategy for the construction of piezo-photocatalysts from the perspective of spatially separated redox sites for efficient H2O2 synthesis.

Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.

Complex spatio-temporal systems like lakes, forests and climate systems exhibit alternative stable states. In such systems, as the threshold value of the driver is crossed, the system may experience a sudden (discontinuous) transition or smooth (continuous) transition to an undesired steady state. Theories predict that changes in the structure of the underlying spatial patterns precede such transitions. While there has been a large body of research on identifying early warning signals of critical transitions, the problem of forecasting the type of transitions (sudden versus smooth) remains an open challenge. We address this gap by developing an advanced machine learning (ML) toolkit that serves as an early warning indicator of spatio-temporal critical transitions, Spatial Early Warning Signal Network (S-EWSNet). ML models typically resemble a black box and do not allow envisioning what the model learns in discerning the labels. Here, instead of naively relying upon the deep learning model, we let the deep neural network learn the latent features characteristic of transitions via an optimal sampling strategy (OSS) of spatial patterns. The S-EWSNet is trained on data from a stochastic cellular automata model deploying the OSS, providing an early warning indicator of transitions while detecting its type in simulated and empirical samples.

Optimization of Centrifugal Pump Impeller Based on Neural Network and NSGA-II Algorithm

Abstract This article focuses on the multistage centrifugal pump secondary impeller as the research object, and the two performance parameters of head and efficiency are taken as the optimization objectives. Firstly, the range analysis of significant influence degree of impeller geometric parameters was carried out by partial factor test method. The blade inlet angle at the front and rear cover boards of the impeller, blade outlet angle and blade wrap angle as the optimization variables, two optimization schemes with different blade numbers were proposed. On this basis, the optimal Latin hypercube sampling method was used to extract samples from the two schemes, CFD technology was used to calculate the corresponding target value of each sample, BP neural networks with different structures were used to train the samples, and the agent model with the highest prediction accuracy was optimized as the population evaluation function of NSGA-II algorithm to search for optimization. The results show that the pump performance parameters have been improved after optimization of Scheme II, and the flow of the internal flow field conforms to the law. Finally, through the experimental verification, the optimized centrifugal pump head and efficiency increased by 3.21m and 3.05%.

A proposal of the optimal sampling interval for shock pulse measurement

A proposal of the optimal sampling interval for shock pulse measurement

Optimizing neurotransmitter pathway detection by IR-MALDESI-MSI in mouse brain.

Mass spectrometry imaging (MSI) platforms such as infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) are advantageous for a variety of applications, including elucidating the localization of neurotransmitters (NTs) and related molecules with respect to ion abundance across a sample without the need for derivatization or organic matrix application. While IR-MALDESI-MSI conventionally uses a thin exogenous ice matrix to improve signal abundance, it has been previously determined that sucrose embedding without the ice matrix improves detection of lipid species in striatal, coronal mouse brain sections. This work considers components of this workflow to determine the optimal sample preparation and matrix to enhance the detection of NTs and their related metabolites in coronal sections from the striatal region of the mouse brain. The discoveries herein will enable more comprehensive follow-on studies for the investigation of NTs to enrich biological pathways and interpretation related to neurodegenerative diseases and ischemic stroke.

Determination of Optimal Sample Length for Passive Intermodulation Measurements in Microstrip Lines with Non-Contact Connections

Determination of Optimal Sample Length for Passive Intermodulation Measurements in Microstrip Lines with Non-Contact Connections

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Meta model-based importance sampling combined with adaptive Kriging method for estimating failure probability function

The failure probability function (FPF) can provide the entire effect of the distribution parameter in the concerned region on failure probability (FP), which is important for decoupling the optimal parameter search and constraint analysis in the FP-constrained design optimization. To improve the efficiency of solving the FPF, this paper combines meta model-based importance sampling with adaptive Kriging (Meta-IS-AK). First, based on the theoretical optimal importance sampling density (ISD) for estimating the FP (ISD-FP) at the given distribution parameter, the theoretical optimal ISD for solving FPF (ISD-FPF) is constructed using an integral operator. Then, a Kriging model of the performance function is adaptively trained to establish the quasi-optimal ISD-FPF for approximating the optimal performance function, and a simple rejection sampling (SRS) method is designed to extract the samples of the quasi-optimal ISD-FPF. For recognizing the state of the ISD-FPF sample and completing the IS estimation of FPF, the Kriging model is secondly updated to establish ISD-FPF in the sample pool comprising these ISD-FPF samples. Using the proposed method, the whole FPF corresponding to arbitrary distribution parameters can be obtained using one IS reliability analysis based on the constructed ISD-FPF. Four examples and an application illustrate the advantages of the proposed method.