Gaussian Spectrum Research Articles

Precision comparison of intensity ratios and area ratios in spectral analysis

The long-debated question in analytical chemistry of which of the area ratio or the intensity ratio is the more precise has yielded no definitive analytical conclusion. To address this issue theoretically, we derived analytical solutions for the lower limits of estimation precision for spectral parameters, including the intensity ratio and area ratio, based on the Cramér–Rao lower bound (CRLB) framework for a Gaussian spectrum. The precisions of spectral parameter estimations from the analytical solutions were consistent with results obtained from Monte Carlo simulations. Our theoretical and simulation results revealed that the precision of estimating the area ratio surpassed that of the intensity ratio by a factor of 2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{2}$$\\end{document}. Additionally, our experimental results aligned well with both theoretical predictions and simulation outcomes, further validating our approach. This increased precision of the area ratio is due to negative covariance between intensity and bandwidth, rather than the area containing more intensity information, as often misinterpreted. Consequently, and quite counter intuitively, prior bandwidth and intensity related information does not improve the area ratio precision: it worsens it. The analytical solution we derived represents the fundamental limits of spectral parameter measurement precision. Thus, it can be used as an alternative method for estimating the minimum error when experimental measurement uncertainty cannot be determined.

Predicting potential microbe-disease associations based on dual branch graph convolutional network.

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Discrete trapping levels of localized states in amorphous silicon nitride

The spectrum of localized hole states (traps) in amorphous silicon nitride, a-Si3N4, is experimentally studied using the method of thermally stimulated depolarization. The experiment is compared with theoretical calculations using three models of the energy spectrum of traps: discrete spectrum (monoenergetic trap), continuous spectrum, and Gaussian trap distribution. The experiment is quantitatively described by a model of a discrete spectrum of traps with an energy of 1.15 eV and a width of no more than 0.01 eV. In the case of a continuous and Gaussian spectrum of traps, the contribution to depolarization is made by the deepest traps. The blurring of the trap energy level in a-Si3N4 due to the absence of long-range order (fluctuations in the Si–N bond length and fluctuations in the N–Si–N and Si–N–Si angles) does not exceed 0.01 eV.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Spectrally resolved nonlinearities within a laser pulse in a single-scan and spectrometer-based nonlinear optical probing.

The intricate spectrally resolved optical nonlinearities resulting from a spectrally broad femtosecond Gaussian laser pulse have been unraveled using a single-scan and spectrometer-based nonlinear optical probing technique. The interaction of the broad femtosecond laser pulse with a strongly absorbing organic dye has unveiled a remarkably distinct nonlinear absorption behavior across the broad spectral window. The nonlinear absorption behavior unveils an unusual transition from the reverse saturation absorption (RSA) to the saturation absorption (SA) as we sweep the wavelength on both sides of the central wavelength of the excitation laser pulse. A competition between the band-filling and excited-state absorption results in such a dramatic switch-over from the RSA to the SA due to the variation of the intensity distribution across the Gaussian pulse spectrum. On the other hand, the nonlinear refraction studies dictate more over the constant Kerr-type positive nonlinear refractive indices across the entire laser pulse, with a pronounced contribution from the nonlinear absorption phase dominating at the center of the pulse. The presented technique establishes a robust and simple spectrometer-based technique that offers new, to the best of our knowledge, avenues for estimating optical nonlinearities for rapid nonlinear optical measurements.

First Monte Carlo beam model for ultra-high dose rate radiotherapy with a compact electron LINAC.

FLASH radiotherapy based on ultra-high dose rate (UHDR) is actively being studied by the radiotherapy community. Dedicated UHDR electron devices are currently a mainstay for FLASH studies. To present the first Monte Carlo (MC) electron beam model for the UHDR capable Mobetron (FLASH-IQ) as a dose calculation and treatment planning platform for preclinical research and FLASH-radiotherapy (RT) clinical trials. The initial beamline geometry of the Mobetron was provided by the manufacturer, with the first-principal implementation realized in the Geant4-based GAMOS MC toolkit. The geometry and electron source characteristics, such as energy spectrum and beamline parameters, were tuned to match the central-axis percentage depth dose (PDD) and lateral profiles for the pristine beam measured during machine commissioning. The thickness of the small foil in secondary scatter affected the beam model dominantly and was fine tuned to achieve the best agreement with commissioning data. Validation of the MC beam modeling was performed by comparing the calculated PDDs and profiles with EBT-XD radiochromic film measurements for various combinations of applicators and inserts. The nominal 9 MeV electron FLASH beams were best represented by a Gaussian energy spectrum with mean energy of 9.9 MeV and variance (σ) of 0.2 MeV. Good agreement between the MC beam model and commissioning data were demonstrated with maximal discrepancy<3% for PDDs and profiles. Hundred percent gamma pass rate was achieved for all PDDs and profiles with the criteria of 2mm/3%. With the criteria of 2mm/2%, maximum, minimum and mean gamma pass rates were (100.0%, 93.8%, 98.7%) for PDDs and (100.0%, 96.7%, 99.4%) for profiles, respectively. A validated MC beam model for the UHDR capable Mobetron is presented for the first time. The MC model can be utilized for direct dose calculation or to generate beam modeling input required for treatment planning systems for FLASH-RT planning. The beam model presented in this work should facilitate translational and clinical FLASH-RT for trials conducted on the Mobetron FLASH-IQ platform.

Fatigue behavior of load-carrying cruciform fillet weld joints under variable amplitude load

The objective of the current work is to study the mean stress effect of variable amplitude (VA) loading on the fatigue strength of load-carrying fillet-welded joints (LCFWJs). Experimental fatigue tests were carried out on LCFWJs made of S700 high-strength steel, considering constant amplitude (CA) and VA loads. In the case of VA loads, a Gaussian load spectrum shape with both constant minimum and maximum stress levels was considered to study varying mean stress conditions. Numerical analyses were conducted to determine the effective stresses at the weld root. The fatigue behavior of LCFWJs was evaluated using the 4R method, which takes into account the local stress ratio (Rlocal, i), material ultimate strength (Rm), residual stress (σres), and weld toe radius (r). Lower fatigue strength was found for the VA tests with the constant maximum stress tests compared to the CA tests when using damage parameter of D = 1.00, while the VA tests with the constant minimum stress tests showed higher fatigue performance. Nonetheless, employing damage parameters of D = 0.2 and D = 0.5 for variable amplitude loads with and without fluctuating mean stress conditions, respectively, provided conservative assessments compared to the design standards. By employing the 4R method, the precision of the assessments could be significantly improved, and the CA and VA fatigue test results fit into a single scatter band with scatter index of Tσ = 1.06 with damage sum of D = 1.00.

Nd, Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal spectrum gain characteristic study on the broadband laser amplification

Spectral gain narrowing is one of the key factors affecting broadband amplification of ultrashort pulses. In this paper, the spectral gain characteristics in broadband amplification are studied theoretically and experimentally by using the characteristic of Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal, i.e. the emission spectrum has a certain width at the higher stimulated emission cross section. Through the numerical simulation, the evolution law of output spectrum of the laser gain medium under different spectral gain lineshapes and different gain values is studied in detail. Theoretical calculation shows that the spectral gain narrows obviously with the increase of gain values of the traditional Gaussian emission spectrum and increasing the spectral bandwidth at the maximum stimulated emission cross section can obviously suppress the spectral gain narrowing. Furthermore, the spectral gain narrowing characteristics of the Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal were studied experimentally. The Ф13mm×150mm Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal was used as the gain medium which was pumped by flash lamps in the experimental study. The experimental results show that the output spectra of Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystals are not obviously narrowed when the input laser spectral width is 5nm (FWHM) and the gain is 140 times. The experimental results are consistent with the theoretical calculation and analysis. The crystal can work normally at the repetition rate of 0.2Hz and 1Hz in the experiment, but due to the influence of thermal effect, the gain will decrease to a certain extent with the increase of pump energy and repetition rate. The research results lay the foundation for the application of fluoride crystal in broadband chirped pulse amplification.

Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.

Neural network-aided simulation of non-Gaussian stochastic processes

Simulation of non-Gaussian stochastic processes is of paramount importance to dynamic reliability assessment of structures driven by non-Gaussian loads. In this paper, a novel translation model based on neural network is proposed to convert the target non-Gaussian power spectrum to the underlying Gaussian power spectrum for simulating non-Gaussian stochastic processes. First, a valid neural network model, whose structure is similar to the transformation from a non-Gaussian power spectrum to its underlying Gaussian power spectrum, is established. Then, starting with an initial underlying Gaussian power spectrum, the non-Gaussian power spectrum corresponding to the target non-Gaussian distribution is obtained through the Wiener-Khintchine transform and translation process. After that, the discrete data of non-Gaussian power spectrum is employed as the input layer in the neural network above, while the discrete data of Gaussian power spectrum serves as the output layer. With the trained neural network, the required underlying Gaussian power spectrum can be directly obtained by inputting the target non-Gaussian power spectrum. Finally, Gaussian samples can be generated by using the spectral representation method, which are then converted into non-Gaussian samples via the translation process. The effectiveness, accuracy, and applicability of the proposed method are demonstrated through two numerical examples.

Evolution and Statistical Analysis of Internal Random Wave Fields within the Benjamin–Ono Equation

This study investigates the numerical evolution of an initially internal random wave field characterized by a Gaussian spectrum shape using the Benjamin–Ono (BO) equation. The research focuses on analyzing various properties associated with the random wave field, including the transition to a steady state of the spectra, statistical moments, and the distribution functions of wave amplitudes. Numerical simulations are conducted across different Ursell parameters, revealing intriguing findings. Notably, it is observed that the spectra of the wave field converge to a stationary state in a statistical sense, while exhibiting statistical characteristics that deviate from a Gaussian distribution. Moreover, as the Ursell parameter increases, the positive skewness of the wave field intensifies, and the kurtosis increases. The investigation also involves the computation of the probability of rogue wave formation, revealing deviations from the Rayleigh distribution. Notably, the study uncovers distinct types of internal rogue waves, specifically referred to as the “two sisters” and “three sisters” phenomena.

Particle dynamics in a turbulent electric field

Charged particle velocity-space diffusion in a prescribed one-dimensional turbulent electric field is investigated through numerical trajectories in phase-space and compared against quasi-linear theory (QL), including resonance broadening (RB). A Gaussian spectrum electric field of variable amplitude E is studied in conjunction with two plasma dispersion relations, namely, the Langmuir and ion-acoustic dispersion. A first parameter scan shows that RB effects become significant for a Kubo number K of a few percent. A Kubo number scan shows that diffusion increases as a power law of D∝K3∝E3/2 for large Kubo numbers. Moreover, at large Kubo numbers, transport processes include significant diffusion measured at velocities much higher than the resonant region, where QL and RB predict negligible diffusion. For times much larger than the trapped particle flight time τb and the autocorrelation time τ0, the velocity distribution departs from a Gaussian. Nevertheless, measurements show that the variance increases linearly in time, with a Hurst parameter of H∼0.5, where the diffusion scales as K5/2∝E5/4 and K3/2∝E3/4 for small and large Kubo number, respectively.

Local Gaussian Cross-Spectrum Analysis

The ordinary spectrum is restricted in its applications, since it is based on the second-order moments (auto- and cross-covariances). Alternative approaches to spectrum analysis have been investigated based on other measures of dependence. One such approach was developed for univariate time series by the authors of this paper using the local Gaussian auto-spectrum based on the local Gaussian auto-correlations. This makes it possible to detect local structures in univariate time series that look similar to white noise when investigated by the ordinary auto-spectrum. In this paper, the local Gaussian approach is extended to a local Gaussian cross-spectrum for multivariate time series. The local Gaussian cross-spectrum has the desirable property that it coincides with the ordinary cross-spectrum for Gaussian time series, which implies that it can be used to detect non-Gaussian traits in the time series under investigation. In particular, if the ordinary spectrum is flat, then peaks and troughs of the local Gaussian spectrum can indicate nonlinear traits, which potentially might reveal local periodic phenomena that are undetected in an ordinary spectral analysis.

BaCa13Mg2(SiO4)8: Ce3+ — A blue phosphor with high brightness, high internal quantum efficiency and excellent thermal stability for w-LEDs

In this paper, a series of Ce3+ ions doped BaCa13Mg2(SiO4)8 blue phosphors were synthesized via a conventional high-temperature solid-state method. Calculations by DFT confirmed that the BaCa13Mg2(SiO4)8 host had a suitable band gap for doping rare earth ions. In terms of luminescence performance, BaCa13Mg2(SiO4)8: Ce3+ blue phosphor with an optimal excitation wavelength of 365 nm can effectively absorb light from 310 to 400 nm and produce a wide emission band centered at 440 nm with an internal quantum efficiency of 71.39%. Based on the analysis of the structure and Gaussian fitting spectra, the Ce3+ ion can occupy all of the Ba2+ and Ca2+ ion lattice positions. After charge compensation by K+ ions, the emission intensity can be increased by 36.8%, and the emission intensity at 140 °C can still maintain 88.7% compared with that at room temperature. Under 365 nm excitation, the integral intensity of the emission spectrum BaCa13Mg2(SiO4)8: 3% Ce3+, 3% K+ sample can reach 90.4% of that of the commercial blue phosphor BaMgAl10O17: Eu2+. When BaCa13Mg2(SiO4)8: Ce3+ phosphors are combined with green (Ba, Sr)2SiO4: Eu2+ phosphors and red CaAlSiN3: Eu2+ phosphors on a 365 nm UV-LED chip, bright LED devices with a correlated color temperature of 4172 K can be obtained. The results show that the blue phosphor BaCa13Mg2(SiO4)8: Ce3+ has great potential for w-LEDs applications.

Identification of ring current proton precipitation driven by scattering of electromagnetic ion cyclotron waves

Among the most intense emissions in the Earth's magnetosphere, electromagnetic ion cyclotron (EMIC) waves are regarded as a critical candidate contributing to the precipitation losses of ring current protons, which however lacks direct multi-point observations to establish the underlying physical connection. Based upon a robust conjunction between the satellite pair of Van Allen Probe B and NOAA-19, we perform a detailed analysis to capture simultaneous enhancements of EMIC waves and ring current proton precipitation. By assuming that the ring current proton precipitation is mainly caused by EMIC wave scattering, we establish a physical model between the wave-driven proton diffusion and the ratio of precipitated-to-trapped proton count rates, which is subsequently applied to infer the intensity of EMIC waves required to cause the observed proton precipitation. Our simulations indicate that the model results of EMIC wave intensity, obtained using either the observed or empirical Gaussian wave frequency spectrum, are consistent with the wave observations, within a factor of 1.5. Our study therefore strongly supports the dominant contribution of EMIC waves to the ring current proton precipitation, and offers a valuable means to construct the global profile of EMIC wave intensity using low-altitude NOAA POES proton measurements, which generally have a broad L-shell coverage and high time resolution in favor of near-real-time conversion of the global EMIC wave distribution.

Spectral broadening scheme for suppressing SBS effects based on time-domain optimized chirp-like signals.

We propose a novel (to our knowledge) driving scheme to suppress the stimulated Brillouin scattering (SBS) effect in master oscillator power amplification (MOPA) systems based on an external high-order phase modulation. Since seed sources with the linear chirp can uniformly broaden the SBS gain spectrum with a high SBS threshold, a chirp-like signal was designed by applying further editing and processing to the piecewise parabolic signal. Compared with the traditional piecewise parabolic signal, the chirp-like signal has similar linear chirp characteristics and can reduce the driving power and sampling rate requirements, enabling more efficient spectral spreading. The SBS threshold model is constructed theoretically based on the three-wave coupling equation. The spectrum modulated by the chirp-like signal is compared with the flat-top and Gaussian spectra in terms of the SBS threshold and the bandwidth-distribution normalized threshold, and a considerable improvement is demonstrated. Meanwhile, the experimental validation is carried out in a watt-class amplifier based on the MOPA structure. At a 3 dB bandwidth of ∼10 GHz, the SBS threshold of the seed source modulated by the chirp-like signal is improved by 35% compared to the flat-top spectrum and 18% compared to the Gaussian spectrum, respectively, and the normalized threshold is also the highest among them. Our study shows that the SBS suppression effect is not only related to the power distribution of the spectrum but also can be improved by the time domain design, which provides a new idea for analyzing and improving the SBS threshold of narrow-linewidth fiber lasers.

Nd, Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal spectrum gain characteristic in broadband laser amplification

Spectral gain narrowing is one of the key factors affecting broadband amplification of ultrashort pulses. In this paper, the spectral gain characteristics in broadband amplification are studied theoretically and experimentally by using the characteristic of Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal, i.e. the emission spectrum that has a certain width at the higher stimulated emission cross section. Through the numerical simulation, the evolution law of output spectrum of the laser gain medium under different spectral gain lineshapes and different gain values is studied in detail. Theoretical calculation shows that the spectral gain is narrowed obviously with the increase of gain value of the traditional Gaussian emission spectrum, and that increasing the spectral bandwidth at the maximum stimulated emission cross section can obviously suppress the spectral gain narrowing. Furthermore, the spectral gain narrowing characteristics of the Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystal are studied experimentally. The &lt;i&gt;Ф&lt;/i&gt; 13 mm× 150 mm Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystals are used as the gain medium which are pumped by flash lamps in the experimental study. The experimental results show that the output spectra of Nd,Gd:SrF&lt;sub&gt;2&lt;/sub&gt; crystals are not obviously narrowed when the full width at half maximum (FWHM) of spectral width of the input laser is 5 nm and the gain is 140 times. The experimental results are consistent with the theoretical calculation and analysis. The crystal can work normally at a repetition rate of 0.2 Hz and 1.0 Hz in the experiment, but due to the influence of thermal effect, the gain will decrease to a certain extent with the increase of pump energy and repetition rate. The research results lay the foundation for the application of fluoride crystal in broadband chirped pulse amplification.

Numerical investigation on nonlinear evolution behavior and water particle velocity of wave crests for narrow-band wave field with Gaussian spectrum

The nonlinear evolution of initial narrow-band Gaussian spectrum wave field, as well as the water particle velocity of wave crests for Freak waves are investigated by phase-resolved numerical model of high-order spectral (HOS) method. The long-time evolution behavior (up to 2000 wave periods) are considered under the effects of initialized parameters, including initial spectral width, wave steepness and BFI. The effects of initial BFI on occurrence probability of Freak waves indicate various distinctive stages. Moreover, the evolution characteristics of spectral width, local wave steepness, BFI and kurtosis are analyzed. And near-periodic oscillating pattern of spectral width after its long-time evolution is obtained and effected by initial spectral width and wave steepness with different features. Besides, the positive (or negative) linear correlations between wave height extreme value and local wave steepness & kurtosis (or BFI evolution value) are effected by initial spectral width primarily. At last, the behavior of water particle velocity (including the extreme value) of Freak wave crest is discussed. And the nonlinear correlation between wave crest height and water particle velocity of wave crest is obtained quantitatively.

Deep Learning-Based Miniaturized All-Dielectric Ultracompact Film Spectrometer

Conventional benchtop spectrometers with bulky dispersive optics and long optical path lengths display limitations where the significance of miniaturization, real-time detection, and low cost transcend the ultrafine resolution and wide spectral range. Here, we demonstrate a miniaturized all-dielectric ultracompact film spectrometer based on deep learning working in the single-shot mode. The scheme employs 16 spectral encoders with simple five-layer film stacks where merely the thickness of the intermediate high-index modulation layer is varied to realize unique encoded transmission spectra. Structural parameters as well as transmission spectra of the filters are predesigned to guarantee weak correlation and highly efficient encoding. Leveraging a trained reconstruction network, the absolute spectra of various nonluminous samples are successfully reconstructed excluding the emitting spectrum of the light source and the spectral response of the detector. The remarkable reconstructed spectral imaging result for the color board is presented and the reconstructed spectra match well with the measured ones for different patches using the identical network. We utilized the least number of spectral encoders ever since to guarantee efficient encoding, along with the single thickness-variant modulation layer, which shows potential for mass, rapid, large-area production by combining deposition with nanoimprint. Instead of the synthetic Gaussian line shape spectra, a training dataset composed of diverse spectrum types is adopted to achieve fine generalization of the trained reconstruction network. In addition, by retraining the neural network, the reconstruction network is modified to fit for the actual filter functions of the spectral encoders, thus better reconstruction performance. The proposed miniaturized spectrometer has great prospects in the fields of consumer electronics, environmental monitoring, and disaster prevention.

Particle production in a hybrid approach for a beam energy scan of Au+Au/Pb+Pb collisions between sqrt{s_textrm{NN}} = 4.3 GeV and sqrt{s_textrm{NN}} = 200.0 GeV

Heavy-ion collisions at varying collision energies provide access to different regions of the QCD phase diagram. In particular collisions at intermediate energies are promising candidates to experimentally identify the postulated first order phase transition and critical end point. While heavy-ion collisions at low and high collision energies are theoretically well described by transport approaches and hydrodynamics+transport hybrid approaches, respectively, intermediate energy collisions remain a challenge. In this work, a modular hybrid approach, the SMASH-vHLLE-hybrid coupling 3+1D viscous hydrodynamics (vHLLE) to hadronic transport (SMASH), is introduced. It is validated and subsequently applied in Au+Au/Pb+Pb collisions between sqrt{s_textrm{NN}} = 4.3 GeV and sqrt{s_textrm{NN}} = 200.0 GeV to study the rapidity and transverse mass distributions of identified particles as well as excitation functions for textrm{dN}/textrm{d}y|_{y = 0} and langle p_textrm{T} rangle . A good agreement with experimental measurements is obtained, including the baryon stopping dynamics. The transition from a Gaussian rapidity spectrum of protons at lower energies to the double-hump structure at high energies is reproduced. The centrality and energy dependence of charged particle v_2 is also described reasonably well. This work serves as a basis for further studies, e.g. systematic investigations of different equations of state or transport coefficients.