Input Spectrum Research Articles

Spectral optimization using fast kV switching and filtration for photon counting CT with realistic detector responses: a simulation study.

Photon counting CT (PCCT) provides spectral measurements for material decomposition. However, the image noise (at a fixed dose) depends on the source spectrum. Our study investigates the potential benefits from spectral optimization using fast kV switching and filtration to reduce noise in material decomposition. The effect of the input spectra on noise performance in both two-basis material decomposition and three-basis material decomposition was compared using Cramer-Rao lower bound analysis in the projection domain and in a digital phantom study in the image domain. The fluences of different spectra were normalized using the CT dose index to maintain constant dose levels. Four detector response models based on Si or CdTe were included in the analysis. For single kV scans, kV selection can be optimized based on the imaging task and object size. Furthermore, our results suggest that noise in material decomposition can be substantially reduced with fast kV switching. For two-material decomposition, fast kV switching reduces the standard deviation (SD) by . For three-material decomposition, greater noise reduction in material images was found with fast kV switching (26.2% for calcium and 25.8% for iodine, in terms of SD), which suggests that challenging tasks benefit more from the richer spectral information provided by fast kV switching. The performance of PCCT in material decomposition can be improved by optimizing source spectrum settings. Task-specific tube voltages can be selected for single kV scans. Also, our results demonstrate that utilizing fast kV switching can substantially reduce the noise in material decomposition for both two- and three-material decompositions, and a fixed Gd filter can further enhance such improvements for two-material decomposition.

Enhanced heat transfer in two-phase closed thermosyphons with external liquid-vapor separation

Two-phase closed thermosyphons (TPCTs) are highly efficient heat transfer devices that leverage natural convection to transport heat over substantial distances with minimal temperature gradients. This study investigates the thermal performance of a novel TPCT equipped with an external liquid-vapor separator, compared to a conventional TPCT. The innovative design incorporates an external pathway connecting the condenser and evaporator sections. A comprehensive evaluation was conducted using three working fluids (water, methyl acetate, and ethanol) across a spectrum of heat inputs (50 ≤ Q ≤ 300 W) and filling ratios (25 %, 40 %, 55 %, 70 %, and 85 %). Optimal performance was consistently observed at a 70 % filling ratio for all fluids tested. While the conventional TPCT exhibited superior performance with methyl acetate, the novel TPCT demonstrated enhanced thermal efficiency when charged with water or ethanol. Notably, the novel TPCT using water reduced thermal resistance by approximately 63 % and 60.5 % compared to ethanol and methyl acetate, respectively, at a 50 W heat input and the optimal filling ratio. These findings underscore the critical influence of working fluid selection and offer valuable insights into the thermal characteristics of the novel TPCT. The results contribute significantly to the optimization and design of TPCTs for diverse applications.

Experimental and numerical study on focused wave generation using different energy modes

This paper reports an experimental and numerical study on the generation of freak waves related to the spatiotemporal focusing mechanism, where multiple water waves converge at a specific point in space and time to produce a large transient wave. A new energy distribution mode of constant-difference wave number (CDK) within the wave group is proposed and applied to both a physical and a numerical flume to generate focused waves, with primary attention given to the water surface elevation, underlying kinematics, and internal spectrum structure during the focused wave generation process. The classical experiment by Baldock et al. [“A laboratory study of nonlinear surface waves on water,” Philos. Trans. R. Soc., Ser. A 354, 649–676 (1996)], which used a constant-difference wave period within the wave group, is chosen for comparison. Spectral analysis shows that the CDK energy distribution mode can easily stimulate multiwave resonance in the focused wave group, where significant energy transfer from the main frequencies to higher harmonics is observed. Compared with the data from Baldock et al. [“A laboratory study of nonlinear surface waves on water,” Philos. Trans. R. Soc., Ser. A 354, 649–676 (1996)], the CDK wave group produces larger focused wave crests with higher propagation speeds and larger velocity gradients near the water surface due to strong multiwave resonance. The effects of the input spectrum, incident amplitude and initial water depth under different energy modes on focused-wave generation are analyzed. All the results indicate that the CDK energy distribution mode can produce larger focused waves compared to other energy distribution modes, which offers a novel approach for generating freak waves in the laboratory.

Innovating Civil Engineering: Strategies for Fostering Stakeholder Engagement in the Circular Economy

Sustainability and circular economy principles are increasingly important in civil engineering, as highlighted by the United Nations Environmental Programme in 2023 the construction activities represented 36% of global energy use in 2020. However, the sector's focus on reducing operational carbon overshadows the need for addressing embodied carbon from materials like cement, steel, and aluminium. To address this, a shift towards incentivizing all supply chain stakeholders and cultivating collaborative models is necessary. This research provides an initial scoping for the development of the Lithuanian Construction Materials Reuse Optimization (LSEPO) platform, a pioneering regional initiative fostering a marketplace for reused building materials. Such platforms are recognized for their potential to overcome sustainability barriers by utilizing digital technologies for efficient data management and stakeholder collaboration. Our research identifies a significant gap in the existing literature concerning effective stakeholder engagement strategies critical for the success of such platforms. To bridge this gap, we employed a three-tiered methodological approach: firstly, a semi-structured literature analysis delved into existing stakeholder engagement strategies within the circular economy. This review highlighted a fragmented understanding of incentives and collaboration dynamics across different sectors. Secondly, a content analysis of global material exchange and circular construction platforms provided insights into best practices, showcasing successful communication methods, innovative incentive models, and robust collaboration frameworks. Lastly, integrating these findings, our study proposes a conceptual stakeholder engagement framework tailored for social innovation in civil engineering, more specifically Lithuanian Construction Materials Reuse Optimization (LSEPO). Practical implications of our findings underscore the necessity for platforms like LSEPO to incorporate adaptive engagement strategies that are context-sensitive and inclusive, facilitating a broad spectrum of stakeholder inputs. The study demonstrates that effective stakeholder engagement goes beyond traditional communication methods; it requires a systemic integration of technological innovation and incentive alignment to promote sustained participation and collaboration. By providing a comprehensive foundation for the development of the LSEPO platform, this research not only aims to advance it as a beacon of social innovation and environmental stewardship within the civil engineering sector but also sets a benchmark for future initiatives aiming to embed circular economy principles effectively.

Decoding Bitcoin: leveraging macro- and micro-factors in time series analysis for price prediction.

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability, making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power. However, current research on the durability of proton exchange membrane electrolyzers is insufficient. Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks. In this study, the operating condition spectrum of an electrolysis stack degradation test cycle was proposed. Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference, a fluctuation output power sample set was formed. The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected. Dimensionality reduction of the operating data was performed using principal component analysis. Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm. Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example, the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed. After preliminary simulation analysis, the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack. This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

Analysis of Rutherford backscattering spectra with CNN-GRU mixture density network

Ion Beam Analysis (IBA) utilizing MeV ion beams provides valuable insights into surface elemental composition across the entire periodic table. While ion beam measurements have advanced towards high throughput for mapping applications, data analysis has lagged behind due to the challenges posed by large volumes of data and multiple detectors providing diverse analytical information. Traditional physics-based fitting algorithms for these spectra can be time-consuming and prone to local minima traps, often taking days or weeks to complete. This study presents an approach employing a Mixture Density Network (MDN) to model the posterior distribution of Elemental Depth Profiles (EDP) from input spectra. Our MDN architecture includes an encoder module (EM), leveraging a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), and a Mixture Density Head (MDH) employing a Multi-Layer Perceptron (MLP). Validation across three datasets with varying complexities demonstrates that for simple and intermediate cases, the MDN performs comparably to the conventional automatic fitting method (Autofit). However, for more complex datasets, Autofit still outperforms the MDN. Additionally, our integrated approach, combining MDN with the automatic fit method, significantly enhances accuracy while still reducing computational time, offering a promising avenue for improved analysis in IBA.

Experimental study on the variation characteristics of focused wave packets with wave spectra scaled from different stages of wave growth

This study investigated the relationship between wave breaking and spectral evolution, focusing on the variations in wave spectra caused solely by breaking under different initial spectral shapes. Laboratory experiments utilizing a controlled wave focusing technique were conducted to generate breaking wave trains with input spectra derived from SWAN model simulations representing 12 distinct stages of wave growth under varying wind durations. Experiment data were combined with HOS simulations to isolate the breaking spectral changes from nonlinearity. Results reveal that the Benjamin-Feir instability dominates the nonlinear interactions for young waves, exhibiting an asymmetric shift towards higher wavenumbers. The characteristics of breaking-induced spectral changes depend on the initial spectral shape and consistent with the “two-phase behavior” of wind wave breaking. The study reveals new insights into the increase in the low-frequency energy, finding that this gain was primarily associated with the breaking of dominant waves near the spectral peak, whereas high-frequency breaking played a minimal role in transferring energy towards lower frequencies. A strong correlation between Qp and the proportion of spectral dissipation in different frequency ranges was revealed, suggesting that Qp can serve as a valuable parameter for characterizing the spectral evolution of breaking waves at various stages of development.

Exploration of deep-learning-based dose rate estimation model for a large-volume plastic scintillation detector

This study explores a deep-learning-based dose rate estimation model for a large-volume plastic scintillation detector. Large-volume plastic scintillators are typically utilized as detector materials for radiation portal monitors (RPMs); however, they can also be utilized for environmental radiation monitoring. We introduced a deep-learning approach to estimate the ambient dose equivalent (H*(10)) from the spectral input using a transformer architecture. The performance of the model was compared with that of other models with conventional architectures and verified using simulated and measured spectra. In addition, we present a model explanation and an uncertainty quantification approach to ensure the reliability of the dose rate estimation model. Our verification demonstrates that the behavior of the model aligns with domain knowledge and that the uncertainty of the model's estimations increases according to the increment in the statistical uncertainties of the input spectra. The experimental results indicate that the model shows a promising performance as a practical application for RPMs.

Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy.

Neural networks (NNs) are emerging as a rapid and scalable method for quantifying metabolites directly from nuclear magnetic resonance (NMR) spectra, but the nonlinear nature of NNs precludes understanding of how a model makes predictions. This study implements an explainable artificial intelligence algorithm called integrated gradients (IG) to elucidate which regions of input spectra are the most important for the quantification of specific analytes. The approach is first validated in simulated mixture spectra of eight aqueous metabolites and then investigated in experimentally acquired lipid spectra of a reference standard mixture and a murine hepatic extract. The IG method revealed that, like a human spectroscopist, NNs recognize and quantify analytes based on an analyte's respective resonance line-shapes, amplitudes, and frequencies. NNs can compensate for peak overlap and prioritize specific resonances most important for concentration determination. Further, we show how modifying a NN training dataset can affect how a model makes decisions, and we provide examples of how this approach can be used to de-bug issues with model performance. Overall, results show that the IG technique facilitates a visual and quantitative understanding of how model inputs relate to model outputs, potentially making NNs a more attractive option for targeted and automated NMR-based metabolomics.

On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators

In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios.

Performance of deconvolution codes (MAXED, GRAVEL, MLEM) for neutron spectrometry of radioactive source using plastic scintillator simulated data

Neutron spectrometry plays a crucial role in decommissioning of nuclear sites and in ensuring the safety of workers from radiation exposure. In this study, we associate a proton recoil spectrometer made by a homemade plastic scintillator with triple discrimination capabilities (fast neutrons, thermal neutrons, and gamma rays) with deconvolution methods to obtain accurate measurements of neutron spectra. The purpose of this work is to test and evaluate the performance of three distinct deconvolution codes, namely MAXED (Maximum Entropy Deconvolution), GRAVEL, and MLEM (Maximum Likelihood Expectation Maximization), using two types of input spectra (flat and Watt spectrum). These deconvolution codes are applied to simulated data using the reference MCNP6.2 Monte Carlo code. Comparing the calculated mean squared error (MSE) performed by the three unfolding methods, we find that MLEM seems to perform better than MAXED and GRAVEL. Furthermore, given the calculated MSE values, the unfolded spectrum of Cf-252 is in better agreement with the standard reference spectrum by using Watt spectrum as input spectrum (MSE<1.2 × 10−6) than using a flat spectrum (MSE<1.3 × 10−6).

Using ChatGPT in Teaching/Learning Language Skills

ChatGPT, a conversational AI model developed by OpenAI, is a large language model like Gemini, Bing, BERT, and others. It utilizes deep learning techniques to generate human-like responses in natural language conversations (Alikaniotis &amp; Prokopidis, 2021). This paper looks into various aspects of ChatGPT, such as its functionality, its applications in ELT, its advantages, and its limitations. While it excels at generating responses similar to humans across a wide spectrum of inputs, it finds it difficult to deal with the complexity of human language. Of course, when used with some caution, ChatGPT can significantly benefit learners and teachers in teaching and learning language skills.

Broadband amplification and thermal lensing in a combination of Yb:YLF and Yb:YAG crystals

Broadband amplification of laser radiation in a combination of Yb:YLF and Yb:YAG crystals was demonstrated. It was shown that, with the input spectrum width of 20 nm, the width at the output spectrum can reach 17 nm. A thermal lens induced in the Yb:YAG crystal is partially compensated by the lens in the Yb:YLF crystal due to the different signs of the ∂n/∂T coefficients of the crystals. The thermal lens in the Yb:YLF crystal in the thin and thick (“long-the-side pumped”) rod geometries was studied theoretically and experimentally. An analytical model of thermally induced phase distortions in uniaxial crystals caused by the piezo-optical effect was developed for the thin-rod geometry.

On-demand design of dual-band electromagnetically induced transparency metamaterials based on improved convolutional neural network

Metamaterials (MMs) with dual electromagnetically induced transparency (EIT)-like windows possess more powerful capacity in Terahertz filtering than their single-EIT counterparts, but the design difficulty normally increases as the MM structure becomes more complicated. This work proposes a deep-learning method using a long short-term memory (LSTM) assisted convolutional neural network (LSTM-CNN) to simplify the design of dual-EIT MMs. Compared to the traditional CNN, the LSTM-CNN exhibits higher prediction accuracy for the geometric parameters of the MM according to the input spectrum, which can be ascribed to enhanced capability of extracting the spectral characteristics after adding the LSTM network. Further adding a feature-transforming network before the LSTM-CNN model to build a link between the MM performance and structure, a customizable MM design is realized. Two different dual-EIT metamaterials are quickly designed with this strategy, and the design accuracy has been fully demonstrated by simulations and experiments. The proposed deep learning method can achieve the rapid and efficient design of dual-EIT MMs, which offers a new pathway for the on-demand design of complex MM-based devices.

Quantitative modelling of type Ia supernovae spectral time series: Constraining the explosion physics

Abstract Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between observations and explosion model predictions. Such tools have provided useful insights, but the subjective nature with which empirical modelling is performed makes it difficult to obtain robust constraints on the explosion physics or expand studies to large populations of objects. Machine learning accelerated tools have therefore begun to gain traction. In this paper, we present riddler, a framework for automated fitting of SNe Ia spectral sequences up to shortly after maximum light. We train a series of neural networks on realistic ejecta profiles predicted by the W7 and N100 explosion models to emulate full radiative transfer simulations and apply nested sampling to determine the best-fitting model parameters for multiple spectra of a given SN simultaneously. We show that riddler is able to accurately recover the parameters of input spectra and use it to fit observations of two well-studied SNe Ia. We also investigate the impact of different weighting schemes when performing quantitative spectral fitting and show that best-fitting models and parameters are highly dependent on the assumed weighting schemes and priors. As spectroscopic samples of SNe Ia continue to grow, automated spectral fitting tools such as riddler will become increasingly important to maximise the physical constraints that can be gained in a quantitative and consistent manner.

Spectral recovery of broadband waveforms via cross-phase modulation based tunable Talbot amplifier

Physical processes in the Fourier domain play a crucial role in various applications such as spectroscopy, quantum technology, ranging, radio-astronomy, and telecommunications. However, the presence of stochastic noise poses a significant challenge in the detection of broadband spectral waveforms, especially those with limited power. In this study, we propose and experimentally demonstrate a cross-phase modulation (XPM) based spectral Talbot amplifier to recover the broadband spectral waveforms in high fidelity. Through the combination of spectral phase filtering and XPM nonlinear effect in an all-fiber configuration, we demonstrate spectral purification of THz-bandwidth spectral waveforms submerged in strong noise. The proposed spectral Talbot amplifier provides tunable amplification factors from 3 to 10, achieved by flexible control on the temporal waveform of the pump and the net dispersion. We demonstrate up to 10-dB remarkable improvement on optical signal-to-noise ratio (OSNR) while preserving the spectral envelope. Furthermore, our system allows frequency-selective reconstruction of noisy input spectra, introducing a new level of flexibility for spectral recovery and information extraction. We also evaluate numerically the impact of pump intensity deviation on the reconstructed spectral waveforms. Our all-optical approach presents a powerful means for effective recovery of broadband spectral waveforms, enabling information extraction from a noise-buried background.

Super-resolution spectroscopy via spectrum slicing with a Fabry–Perot cavity

Spectroscopy is one of the most powerful technical tools that are widely used in the study of natural sciences and the development of high technologies. To improve the performance of a traditional dispersive spectrometer, there is a competition between its spectral resolution and the geometric volume; a higher resolution is usually accompanied by a higher cost as well. However, a growing body of evidence suggests an urgent demand for miniaturized spectrometers with high spectral resolution and low costs. In this paper, we propose and study super-resolution spectroscopy via spectrum slicing by use of a Fabry–Perot (FP) cavity combined with a traditional spectrometer. The cavity functions as a spectral filter to slice into discrete pieces the continuous spectrum of the input light. For each given cavity length, the cavity outputs a set of sliced spectral pieces that are then analyzed and recorded by the subsequent spectrometer. By scanning the cavity length, one will have multiple sets of sliced spectral pieces that are then finally fused to recover a resolution-enhanced copy of the input spectrum. A theoretical analysis for the super-resolution spectroscopy is provided to prove the feasibility of the proposal, as further confirmed by MATLAB numerical simulation. The studied spectral analysis technique will benefit the research fields in need of high resolution spectrometers with moderate device volumes and low costs.

Efficient prediction of attosecond two-colour pulses from an X-ray free-electron laser with machine learning

X-ray free-electron lasers are sources of coherent, high-intensity X-rays with numerous applications in ultra-fast measurements and dynamic structural imaging. Due to the stochastic nature of the self-amplified spontaneous emission process and the difficulty in controlling injection of electrons, output pulses exhibit significant noise and limited temporal coherence. Standard measurement techniques used for characterizing two-coloured X-ray pulses are challenging, as they are either invasive or diagnostically expensive. In this work, we employ machine learning methods such as neural networks and decision trees to predict the central photon energies of pairs of attosecond fundamental and second harmonic pulses using parameters that are easily recorded at the high-repetition rate of a single shot. Using real experimental data, we apply a detailed feature analysis on the input parameters while optimizing the training time of the machine learning methods. Our predictive models are able to make predictions of central photon energy for one of the pulses without measuring the other pulse, thereby leveraging the use of the spectrometer without having to extend its detection window. We anticipate applications in X-ray spectroscopy using XFELs, such as in time-resolved X-ray absorption and photoemission spectroscopy, where improved measurement of input spectra will lead to better experimental outcomes.

SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation

Data augmentation is a crucial component in training neural networks to overcome the limitation imposed by data size, and several techniques have been studied for time series. Although these techniques are effective in certain tasks, they have yet to be generalized to time series benchmarks. We find that current data augmentation techniques ruin the core information contained within the frequency domain. To address this issue, we propose a simple strategy to preserve spectral information (SimPSI) in time series data augmentation. SimPSI preserves the spectral information by mixing the original and augmented input spectrum weighted by a preservation map, which indicates the importance score of each frequency. Specifically, our experimental contributions are to build three distinct preservation maps: magnitude spectrum, saliency map, and spectrum-preservative map. We apply SimPSI to various time series data augmentations and evaluate its effectiveness across a wide range of time series benchmarks. Our experimental results support that SimPSI considerably enhances the performance of time series data augmentations by preserving core spectral information. The source code used in the paper is available at https://github.com/Hyun-Ryu/simpsi.