Python Script Research Articles

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

Influence of magma flux on magma storage depths along the Reykjanes Ridge

Quantitative thermomechanical models of mid-ocean ridge magmatic systems focus on spreading rate variations as the principal control on crustal thermal structure and the distribution of melt storage. The Reykjanes Ridge (RR), a slow-spreading portion of the Mid-Atlantic Ridge, has a near-constant spreading rate, but crustal thickness varies by a factor of two. Therefore, magma flux to the ridge varies independently of spreading rate. Here, we investigate the role of magma flux on storage depths along mid-ocean ridges (MORs) and constrain the roles of mantle melt generation rate and spreading rate on oceanic crustal magmatic systems.Using pyOPAM, an open-source Python script written to carry out olivine–plagioclase– augite–melt (OPAM) thermobarometry, with an uncertainty of 1.2 kbar and 9.7°C, samples between 64°N and 52.5°N are compared. The distribution of median OPAM pressure and temperature estimates are compared with calculated magma flux rates across the area of interest.The section of the RR immediately south of the subaerial Reykjanes Peninsula has a median OPAM pressure of 3.7 kbar, and estimated magma flux of 8.82 × 10−6 km3 km−2 y−1. The southern end of the RR has a median storage pressure of 4.8 kbar, and magma flux of 4.58 × 10−6 km3 km−2 y−1. In comparison, The Charlie-Gibbs Fracture Zone to the south has a median OPAM estimate of 6.3 kbar and calculated magma flux of 2.71 × 10−6 km3 km−2 y−1. Despite important local variation, median final magma storage depths and temperatures before eruption increase southwards along the RR. This finding indicates that magma storage depths at mid-ocean ridges are controlled predominantly by magma flux rather than spreading rate alone. Increasing magma flux drives the shallowing of cooler, final magma storage depths, and decreasing magma flux increases magma storage depths and temperature.

Location of constriction in velar sounds in French

This study examines the constriction location (CL) of the velar stop [k] in French. While previous studies have investigated how vocalic contexts influence the CL of velar stops [Liker & Gibbon (2008) Clin. Ling. & Phon. 22(2); Tabain (2000) JPhon 28(2)], few investigated high-level changes in CL. Building on our prior work [Islam & Gick (2023) JASA 154], which showed an unexpected palato-velar articulation of [k] followed by [a] compared with [w] followed by [a], we tested whether this [k]-palatalizing is universal or specific to [a]. Using a French MRI speech corpus [Isaieva et al., Scientific Data 8], we measured constriction in [k] before [i], [ɛ], [o], [u], and [a]. MRI video frames were manually traced to mark the upper surface of the tongue and the lower surface of the hard palate, resulting in two contours. Using Euclidean distance, the location of the constriction was identified as the narrowest point and distance, respectively, between the two contours. A Python script that measured Euclidean distance from traced MRI frames calculated these distances from the MRI frames. Results indicate a frontal shift in [k] across all contexts, indicating a general fronting articulation of [k] in French speech.

Analysis of Pushing Forces During the Water Polo Eggbeater: Reliability and Validity of a Novel Approach.

Water polo players benefit from greater odds of success when maintaining their tactical position against their opponents. This study evaluated the reliability and validity of a water-based resistance test to replicate this skill.Thirty-three water polo players participated in this study (19 males and 14 females, 14 from senior and 19 from junior national teams). Data were collected during two regular training sessions, separated by one week, using a load cell to instrument a weight stack resistance setup on the pool deck. Performance parameters such as mean force, maximum force, mean peak force and total impulse were defined with custom Python scripts. Test-retest reliability was assessed using intra-class correlations (ICC3,1). Group comparisons were explored between male and female players. Level of significance was set at p < 0.05. The reliability findings were high to very high for the mean force, maximum force, mean peak force, inter-stroke range, and total impulse (ICC 0.85-0.93, p < 0.01). Group comparisons showed significantly greater values in male players for these variables (p < 0.01, ES = 1.05-9.36) with large to very large effect sizes. However, there was no significant difference in endurance measured between sexes (p = 0.88, ES = 0.04). This study presents a methodology with satisfactory metrological qualities for field applications using simple and affordable equipment. The testing apparatus presented in this study can readily be replicated in a variety of training environments by practitioners working with water polo teams. Coaches can use this approach to evaluate individual player progress or to compare performance across a group of water polo players.

Implement Speech Recognition and Synthesis System Using Python

Python script implements a speech recognition and synthesis system using the Speech Recognition and pyttsx3 libraries. The system allows users to speak into their microphone, recognizes the speech using Google's Web Speech API, and then speaks out the recognized text. The main program loop continuously listens for speech input, recognizes it, and then synthesizes the recognized text into speech. The loop terminates when the recognized speech is "exit". This implementation demonstrates the integration of speech recognition and synthesis capabilities in Python, showcasing their potential applications in various domains such as human-computer interaction, accessibility, and automation.

Simulating multi-particle deposition based on CEL method: studing the effects of particle and substrate temperature on deposition

The subject matter of this study is to use numerical simulation methods to study the influence of the temperature of particles and substrates on the post-deposition coating during the multi-particle deposition process of cold spray. The goal is to study the temperature of Al6061 particles and the temperature of the substrate, which are factors that have a greater impact on the deposited coating, and to observe the shape of the coating and the temperature distribution of the cross-section of the substrate after deposition. The tasks to be solved are as follows: use Python scripts to model multi-particles, generate and randomly assign positions according to particle size distribution in the Euler domain, and establish a cold spray multi-particle collision model to simulate the process of cold spray deposition. The following methods were used: The influence of temperature and substrate temperature on the deposited coating was studied through a single variable method; the Coupled Eulerian Lagrangian (CEL) method was used to simulate the collision process of cold-sprayed Al6061 multi-particles. The following results were obtained: changing the temperature of Al6061 particles has a more obvious control effect on the porosity of the deposited coating; after particles of different temperatures impact the constant-temperature substrate, the high-temperature area on the surface of the substrate is mainly located at the junction of pits; after the particle temperature reaches 650K, the coating changes after deposition are no longer significant, indicating an optimal temperature range for Al6061 particle deposition; increasing the temperature of the substrate can increase the depth of particle deposition on the substrate; at the same time, it serves as a reference basis for further using the CEL method to predict the porosity of the Al6061 coating. Conclusions. The scientific novelty of the results obtained is as follows: 1) powder preheating can effectively reduce the porosity of Al6061 coating; 2) the CEL method has good robustness and is used to simulate cold spray multi-particle deposition to monitor the porosity of the coating, which cannot be achieved by the SPH and ALE methods.

Open-source milligram-scale, four channel, automated protein purification system.

Liquid chromatography purification of multiple recombinant proteins, in parallel, could catalyze research and discovery if the processes are fast and approach the robustness of traditional, "one-protein-at-a-time" purification. Here, we report an automated, four channel chromatography platform that we have designed and validated for parallelized protein purification at milligram scales. The device can purify up to four proteins (each with its own single column), has inputs for up to eight buffers or solvents that can be directed to any of the four columns via a network of software-driven valves, and includes an automated fraction collector with ten positions for 1.5 or 5.0 mL collection tubes and four positions for 50 mL collection tubes for each column output. The control software can be accessed either via Python scripting, giving users full access to all steps of the purification process, or via a simple-to-navigate touch screen graphical user interface that does not require knowledge of the command line or any programming language. Using our instrument, we report milligram-scale, parallelized, single-column purification of a panel of mammalian cell expressed coronavirus (SARS-CoV-2, HCoV-229E, HCoV-OC43, HCoV-229E) trimeric Spike and monomeric Receptor Binding Domain (RBD) antigens, and monoclonal antibodies targeting SARS-CoV-2 Spike (S) and Influenza Hemagglutinin (HA). We include a detailed hardware build guide, and have made the controlling software open source, to allow others to build and customize their own protein purifier systems.

Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion's Polarizability and Molecular Mass with Limited Data.

The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3.

Enhancing Health Care Accessibility and Equity Through a Geoprocessing Toolbox for Spatial Accessibility Analysis: Development and Case Study.

Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

Technical note: SpekPy Web-online x-ray spectrum calculations using an interface to the SpekPy toolkit.

Knowledge of the photon spectrum emitted from an x-ray tube is frequently needed in imaging and dosimetry contexts. As the spectrum characteristics are influenced by several parameters and routine measurement of a spectrum is often impractical, a variety of software programs have been developed over the decades for convenient calculations. SpekPy is a state-of-the-art software package containing several spectrum models, and was created to estimate photon spectra originating from x-ray tubes using a small set of input parameters (e.g., anode material, anode angle, tube potential, filtration, etc.). SpekPy is distributed as a Python toolkit and is available free of charge. The toolkit does, however, lack a graphical user interface and a user is required to write a Python script to make use of it. In this work this limitation is addressed by introducing a web application called SpekPy Web: a graphical user interface together with an application programmable interface (API). These developments both make the SpekPy spectrum models accessible to a broader set of users and increases the ease of use for existing users. SpekPy Web is hosted at: https://spekpy.smile.ki.se. The functionality of the software is demonstrated, using its API, by estimating first half-value layers (HVLs) for 15 standard beam qualities from the International Bureau of Weights and Measures (BIPM). The estimated HVLs were found to all be within 3.5% agreement when compared to experimental values, with an average calculation time of 2.5 s per spectrum. half-value-layer, software, x-ray spectrum.

Isoform-specific RNA structure determination using Nano-DMS-MaP.

RNA structure determination is essential to understand how RNA carries out its diverse biological functions. In cells, RNA isoforms are readily expressed with partial variations within their sequences due, for example, to alternative splicing, heterogeneity in the transcription start site, RNA processing or differential termination/polyadenylation. Nanopore dimethyl sulfate mutational profiling (Nano-DMS-MaP) is a method for in situ isoform-specific RNA structure determination. Unlike similar methods that rely on short sequencing reads, Nano-DMS-MaP employs nanopore sequencing to resolve the structures of long and highly similar RNA molecules to reveal their previously hidden structural differences. This Protocol describes the development and applications of Nano-DMS-MaP and outlines the main considerations for designing and implementing a successful experiment: from bench to data analysis. In cell probing experiments can be carried out by an experienced molecular biologist in 3-4 d. Data analysis requires good knowledge of command line tools and Python scripts and requires a further 3-5 d.

Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models.

The emergence of artificial intelligence, capable of human-level performance on some tasks, presents an opportunity to revolutionise development of systematic reviews and network meta-analyses (NMAs). In this pilot study, we aim to assess use of a large-language model (LLM, Generative Pre-trained Transformer 4 [GPT-4]) to automatically extract data from publications, write an R script to conduct an NMA and interpret the results. We considered four case studies involving binary and time-to-event outcomes in two disease areas, for which an NMA had previously been conducted manually. For each case study, a Python script was developed that communicated with the LLM via application programming interface (API)calls. The LLM was prompted to extract relevant data from publications, to create an R script to be used to run the NMA and then to produce a small report describing the analysis. The LLM had a > 99% success rate of accurately extracting data across 20 runs for each case study and could generate R scripts that could be run end-to-end without human input. It also produced good quality reports describing the disease area, analysis conducted, results obtained and a correct interpretation of the results. This study provides a promising indication of the feasibility of using current generation LLMs to automate data extraction, code generation and NMA result interpretation, which could result in significant time savings and reduce human error. This is provided that routine technical checks are performed, as recommend for human-conducted analyses. Whilst not currently 100% consistent, LLMs are likely to improve with time.

UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow.

We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).

SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images

BackgroundInvestigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici—a frequently occurring and damaging disease on wheat crops.ResultsWe developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions.ConclusionsSeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.

Charge cluster occurrence in land plants’ mitochondrial proteomes with functional and structural insights

The Charge Clusters (CCs) are involved in key functions and are distributed according to the organism, the protein’s type, and the charge of amino acids. In the present study, we have explored the occurrence, position, and annotation as a first large-scale study of the CCs in land plants mitochondrial proteomes. A new python script was used for data curation. The Finding Clusters Charge in Protein Sequences Program was performed after adjusting the reading window size. A 44316 protein sequences belonging to 52 species of land plants were analysed. The occurrence of Negative Charge Clusters (NCCs) (1.2%) is two times more frequent than the Positive Charge Clusters (PCCs) (0.64%). Moreover, 39 and 30 NCCs were conserved in 88 and 41 proteins in intra and in inter proteomes respectively, while 14 and 21 PCCs were conserved in 53 and 85 protein sequences in intra and inter proteomes consecutively. Sequences carrying mixed CCs are rare (0.12%). Despite this low abundance, CCs play a crucial role in protein function. The CCs tend to be located mainly in the terminal regions of proteins which guarantees specific protein targeting and import into the mitochondria. In addition, the functional annotation of CCs according to Gene Ontology shows that CCs are involved in binding functions of either proteins or macromolecules which are deployed in different metabolic and cellular processes such as RNA editing and transcription. This study may provide valuable information while considering the CCs in understanding the environmental adaptation of plants. Communicated by Ramaswamy H. Sarma

A Scada system used for monitoring virtual chemical plants via Modbus communication

AbstractMany chemical processes commonly use many instruments to meticulously gauge and regulate critical system variables. Automation within the industrial sector hinges upon deploying a Supervisory Control and Data Acquisition (SCADA) system. However, the software typically employed for this purpose is endowed with substantial added value, rendering its application in smaller‐scale projects economically impractical. In the context of this study, our primary objective was to develop a SCADA system tailored for a virtual plant. To realize this aim, we simulated a heat exchanger network process unit using UniSim®. We integrated it with the ScadaBR software through a Python script, capitalizing on the Modbus communication protocol. It encompasses essential functionalities such as real‐time variable supervision, dynamic graph and report generation. For the proposed example, 28 variables were monitored. The results revealed that statistical analysis of the variables could be performed graphically and computationally, evidencing that the ScadaBR tool was successfully connected to the Unisim® simulator.

A multiscale modeling method with updatable ABD shells for laminated flexible solar arrays

The flexible solar array is an innovative deployable system to provide electrical power for space stations, satellites, and other spacecrafts. Due to the advantages of lightweight, large area, and high power, it has received considerable attention and applications. However, the intricate multilayer configuration, nonlinear material behavior, and low stiffness characteristic of the array have made it always challenging to accurately predict the morphology and frequencies. This paper presents a numerical nonlinear multiscale method called PWL FE2 for large-scale flexible solar arrays. The proposed method is implemented in Abaqus with Python scripts and Micromechanics plugin. Its fundamental procedure is as follows: at microscale, the nonlinear constitutive model is piecewise linearly interpolated, followed by ABD stiffness and mass homogenizations of each segment; at macroscale, material properties of general ABD shells are updated based on section forces. Benchmark tests on substrate tension show that the error between PWL FE2 and direct numerical simulation (DNS) is less than 5 %. The refined shell model with PWL FE2 algorithm proves to be computationally efficient and convergence-friendly for morphology and frequency analysis of the array. For the single array undergoing strong nonlinearity, the computational efficiency of PWL FE2 is nine times that of DNS. The out-of-plane displacement reaches up to 210 mm, and the differences in reaction forces and deformations between nonlinear and linear simulations exceed 20 %. For the global array subjected to 84 N tensile loads, the array remains planar, and the first four modes are clustered around 0.2 Hz.

Adaptation to extreme weather events using pre-conditioning: a model-based testing of novel resilience algorithms on a residential case study

Climate change increases the frequency and intensity of extreme weather events that can be a prominent cause of power outages in North America. These events may cause buildings to experience outages for hours to days, endangering occupant well-being. Although typical adaptive strategies can offer assistance, they often demand substantial initial investments. Thus, due to the need for low-cost solutions, this paper evaluates the efficacy of the proposed pre-heating/cooling algorithm using smart thermostats. The ongoing research employs automated energy modelling through Python scripting to streamline the energy model upgrade process and the EnergyPlus Energy Management System (EMS) algorithm to incorporate pre-conditioning features during grid outages. The results indicated an average 18% improvement in peak intensity and 9% in overall performance during extreme events. Also, it offers the potential for future studies to employ this methodology in assessing the effects of other low-cost strategies for adapting to grid disruptions.

PARSID: a Python script for automatic analysis of local BLAST results for a rapid molecular taxonomic identification

ObjectiveA reliable taxonomic identification of species from molecular samples is the first step for many studies. For researchers unfamiliar with programming, running a BLAST analysis, filtering, and organizing results for hundreds of sequences through the BLAST web interface can be difficult. Additionally, sequences deposited in GenBank can have outdated taxonomic identification. The use of reliable Reference Sequences Library (RSL) containing accurate taxonomically-identified sequences facilitates this task. Pending the availability of a RSL with the user, we developed a tool that automates the molecular taxonomic identification of sequences.ResultsWe developed PARSID, a Python script running through the command-line that automates the routine workflow of blasting an input sequence file against the user’s RSL, and retrieves the matches with the highest percentage of identity in five steps. PARSID accepts cut-off parameters and supplementary information in a.csv file for filtering the results. The final output is visualized in a spreadsheet. We tested its functioning using 10 input sequences simulating different situations of the molecular taxonomic identification of sequences against an example RSL containing 25 sequences. Step-by-step instructions and test files are publicly available at https://github.com/kokinide/PARSID.git.

Sc/SiC/Al Multilayer Optimization for Li K Spectroscopy

This paper presents an X-ray reflectivity study of a Sc/SiC/Al periodic multilayer deposited via magnetron sputtering and its possible adaptation to be used as a dispersive element in the crystal spectrometers equipping scanning electron microscopes and electron probe microanalyzers. This multilayer is designed for the spectral range of 45–60 eV. The results reveal a reflectance of 40.8% at 54.1 eV for a near-normal incidence angle of 7° with a narrow bandwidth of 2.6 eV. The measured and simulated reflectivity curves are very close, suggesting that this system has smooth interfaces and low interdiffusion. Owing to the growing importance of lithium and lack of spectroscopic data, we simulate a new Sc/SiC/Al stack based on the reflectivity data and optimize it to perform spectroscopy in the range near the Li K absorption edge around 55 eV, which is in the spectral range of the Li Kα emission band. This optimization is achieved by tuning the thicknesses of the different layers and the number of periods of the multilayer using an in-house Python script. The optimization results are compared with the performances of other multilayers employed in the same energy range and at a working angle close to 30° grazing, including Be/Si/Al. This analysis indicates that the Sc/SiC/Al multilayer could be a good candidate for performing spectroscopy in the Li K range.