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VIsToLAI: A modular open-source platform for estimating the leaf area index from remote sensing-derived vegetation indices VIsToLAI：一个模块化的开源平台，用于从遥感衍生的植被指数估算叶面积指数

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102504

Jonghan Ko , Chi Tim Ng

VIsToLAI, a unique open-source software framework developed in Python, stands out for its ability to estimate the leaf area index (LAI) using time series data derived from various remote sensing vegetation indices (VIs). The framework integrates both empirical regression models and machine learning (ML) approaches, offering a flexible and scalable workflow for LAI estimation. Through case studies on four major staple crops—rice, barley, wheat, and maize—this study demonstrates the framework's ability to accurately estimate LAI across diverse crop types and environmental conditions. Results show that machine learning models, particularly extra trees and gradient boosting, outperform traditional empirical models in terms of accuracy and robustness, especially under heterogeneous data conditions. VIsToLAI’s modular architecture enables the easy incorporation of new indices and algorithms, as well as seamless integration into existing remote sensing workflows. The software provides a valuable tool for bridging remote sensing data with agricultural modeling, supporting precision agriculture and large-scale monitoring initiatives.

VIsToLAI是一个用Python开发的独特的开源软件框架，它能够使用来自各种遥感植被指数（VIs）的时间序列数据来估计叶面积指数（LAI）。该框架集成了经验回归模型和机器学习（ML）方法，为LAI估计提供了灵活且可扩展的工作流程。通过对水稻、大麦、小麦和玉米这四种主要作物的案例研究，本研究证明了该框架在不同作物类型和环境条件下准确估计LAI的能力。结果表明，机器学习模型，特别是额外的树和梯度增强，在准确性和鲁棒性方面优于传统的经验模型，特别是在异构数据条件下。VIsToLAI的模块化架构可以轻松整合新的指数和算法，并无缝集成到现有的遥感工作流程中。该软件为连接遥感数据与农业建模、支持精准农业和大规模监测举措提供了宝贵的工具。

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引用次数: 0

VelCrys: Interactive web-based application to compute acoustic wave velocity in crystals and its magnetic corrections VelCrys：基于web的交互式应用程序，用于计算晶体中的声波速度及其磁校正

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2025-12-12 DOI: 10.1016/j.softx.2025.102472

P. Nieves , I. Korniienko , A. Fraile , J.M. Fernández-Díaz , R. Iglesias , D. Legut

This paper presents a web-based interactive tool called VelCrys that allows to compute and plot the group velocity of the acoustic waves in crystals, as well as to account for the effects of an external magnetic field on sound. The group velocity is obtained by calculating Christoffel matrix elements and their partial derivatives with respect to the phase velocity direction, and inserting them into an analytical expression for the group velocity. The effect of that external magnetic field is computed through the induced effective corrections to the elastic tensor which depend on the magnetic susceptibility tensor and the magnetoelastic constants. We apply it to dry sandstone, cubic CoPt and hcp Co to show some of the program’s features. In the analysis of the magnetic field effects, we find complex landscapes of fractional change in group velocity as a function of ray direction, as well as a field dependence consistent with the Simon effect.

本文介绍了一种基于网络的交互式工具VelCrys，它可以计算和绘制晶体中声波的群速度，并解释外部磁场对声音的影响。群速度是通过计算Christoffel矩阵元素及其相对于相速度方向的偏导数，并将其代入群速度的解析表达式得到的。通过对弹性张量的感应有效修正来计算外磁场的影响，该修正依赖于磁化率张量和磁弹性常数。我们将其应用于干砂岩，立方CoPt和hcp Co，以显示程序的一些特征。在磁场效应的分析中，我们发现了群速度随射线方向的分数变化的复杂景观，以及与西蒙效应一致的场依赖性。

引用次数: 0

SNAPRed: Reduction of multidimensional neutron time-of-flight diffraction data SNAPRed：减少多维中子飞行时间衍射数据

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2025-12-08 DOI: 10.1016/j.softx.2025.102464

M. Guthrie , M.M. Walsh , K.A. Travis , S.R. Boston , D.L. Caballero , D.D. Dinger , G. Elsarboukh , J.M. Hetrick , A.T. Savici , P.F. Peterson

SNAP is a neutron time-of-flight diffractometer at the Spallation Neutron Source operated by Oak Ridge National Laboratory. It generates large arrays of neutron detection events that encode the crystalline atomic structure of materials under study. SNAPRed is an application that makes these datasets accessible to end users by orchestrating the process of data reduction while automatically managing the variable neutron instrumentation configuration. It supports arbitrary grouping and masking of individual detector pixels and includes custom-developed data compression approaches to accommodate the large volumes of data generated by the SNAP instrument.

SNAP是由橡树岭国家实验室操作的散裂中子源上的中子飞行时间衍射仪。它产生大量中子探测事件，编码所研究材料的晶体原子结构。SNAPRed是一个应用程序，通过编排数据缩减过程，同时自动管理可变中子仪器配置，使最终用户可以访问这些数据集。它支持任意分组和屏蔽单个探测器像素，并包括定制开发的数据压缩方法，以适应由SNAP仪器生成的大量数据。

引用次数: 0

TD-TSPS: A hybrid strategy method for TD detection based on two-step progressive segmentation TD- tsps：一种基于两步渐进分割的TD检测混合策略方法

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102499

Yuxi Zhang, Jinxin Dong, Hua Jiang, Ruchao Du, Ranran Sun

Tandem duplication (TD) represents a crucial type of structural variations within the human genome. When the sequencing depth is low, TD signal at each single nucleotide position becomes more indistinct. So the detection of TDs under low coverage remains a challenging task. This paper proposes a method called TD-TSPS (Two-Step Progressive Segmentation for TD detection) on whole genome sequencing data. A two-step progressive segmentation strategy is employed to divide the genome into continuous and similar bins. Additionally, it integrates split read and paired-end mapping strategies to refine TD regions. Performance tests on simulated and real datasets show that TD-TSPS achieves a good F1-score. Therefore, it can be used as an effective tool for TDs detection.

串联重复（TD）是人类基因组中一种重要的结构变异类型。当测序深度较低时，每个单核苷酸位置的TD信号变得更加模糊。因此，检测低覆盖率的td仍然是一项具有挑战性的任务。本文提出了一种基于全基因组测序数据的TD- tsps （Two-Step Progressive Segmentation for TD detection）方法。采用两步渐进分割策略将基因组划分为连续和相似的bin。此外，它还集成了分裂读取和对端映射策略来细化TD区域。在模拟和真实数据集上的性能测试表明，TD-TSPS获得了良好的f1分数。因此，它可以作为TDs检测的有效工具。

引用次数: 0

Spectral Imaging Deep Learning Mapper - SpecDeepMap: An open-source EnMAP-Box semantic segmentation application for hyper- and multispectral mapping 光谱成像深度学习Mapper - SpecDeepMap：一个开源的EnMAP-Box语义分割应用程序，用于超光谱和多光谱映射

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102481

Leon-Friedrich Thomas , Benjamin Jakimow , Andreas Janz , Patrick Hostert , Antti Lajunen

Deep learning is increasingly applied in spectral imaging and remote-sensing research, yet accessible interface-based software remains limited. We therefore developed the Spectral Imaging Deep Learning Mapper (SpecDeepMap), a free and open-source application embedded into the EnMAP-Box QGIS plugin that enables deep-learning–based spectral analysis and mapping. SpecDeepMap implements a comprehensive semantic segmentation workflow through a user-friendly graphical interface, requiring no programming expertise. The software is designed for multispectral and hyperspectral data and addresses geographical data challenges, such as spatial class distribution, and continuous largescale mapping tasks. SpecDeepMap offers various deep-learning architectures, such as U-Net, U-Net++, DeeplabV3+, and SegFormer, paired with diverse backbones such as ResNet-18, ConvNeXt, Swin-Transformers and Segment Anything Model 2. This software is the first QGIS plugin that enables fine-tuning multispectral foundation models for Sentinel-2 Top of Atmosphere Reflectance imagery. These weights stem from pretraining by Wang et al. (2022) on the Self-Supervised Learning for Earth Observation Sentinel-1/2 dataset.

深度学习在光谱成像和遥感研究中的应用越来越广泛，但基于接口的可访问软件仍然有限。因此，我们开发了光谱成像深度学习映射器（SpecDeepMap），这是一个嵌入到EnMAP-Box QGIS插件中的免费开源应用程序，可以实现基于深度学习的光谱分析和映射。SpecDeepMap通过用户友好的图形界面实现了全面的语义分割工作流，不需要编程专业知识。该软件专为多光谱和高光谱数据而设计，并解决地理数据挑战，如空间类分布和连续大规模制图任务。SpecDeepMap提供各种深度学习架构，如U-Net， U-Net++， DeeplabV3+和SegFormer，与各种骨干（如ResNet-18， ConvNeXt， swing - transformers和Segment Anything Model 2）配对。该软件是第一个QGIS插件，可以对Sentinel-2大气反射图像的多光谱基础模型进行微调。这些权重来源于Wang等人（2022）对自监督学习for Earth Observation Sentinel-1/2数据集的预训练。

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引用次数: 0

D4Xgui: A tool for baseline correction and standardization of carbonate clumped isotope raw data D4Xgui：碳酸盐岩块状同位素原始数据基线校正与标准化工具

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.softx.2025.102492

Miguel Bernecker , Mathieu Daëron , Philip Tauxe Staudigel , Sven Hofmann , Jens Fiebig

Accurate and precise mass spectrometric determination of ppm-ppb quantities of mass 47–49 clumped isotopologues of carbonate-derived CO₂, expressed as

Δ_{47}

–

Δ_{49}

values, requires advanced processing schemes. Here, we introduce D4Xgui, a user-friendly processing tool that allows correction of mass-spectrometric raw intensities for a pressure baseline artifact, before standardization is carried out using D47crunch. D4Xgui enables rapid processing of multi-session data under consideration of full error-propagation, interactive visualization of results including tools for data quality assurance, calculation of carbonate crystallization temperature from finally processed data, and rapid re-evaluation of datasets with revised processing parameters. Though the primary focus of D4Xgui is on carbonates it can also be applied to the correction of mass spectrometric raw data obtained on CO₂ from other sources.

准确和精确的质谱测定质量47-49的块状同位素碳酸盐衍生的二氧化碳的ppm-ppb的数量，表示为Δ47 -Δ49值，需要先进的处理方案。在这里，我们介绍了D4Xgui，这是一个用户友好的处理工具，允许在使用D47crunch进行标准化之前对压力基线工件的质谱原始强度进行校正。D4Xgui能够在充分考虑误差传播的情况下快速处理多会话数据，实现结果的交互式可视化，包括数据质量保证工具，从最终处理的数据中计算碳酸盐结晶温度，以及使用修订的处理参数对数据集进行快速重新评估。虽然D4Xgui的主要焦点是碳酸盐，但它也可以应用于从其他来源获得的二氧化碳质谱原始数据的校正。

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引用次数: 0

AssociationExplorer: A user-friendly shiny application for exploring associations and visual patterns AssociationExplorer：一个用户友好的应用程序，用于探索关联和可视化模式

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.softx.2025.102483

Antoine Soetewey , Cédric Heuchenne , Arnaud Claes , Antonin Descampe

AssociationExplorer is an open-source interactive R Shiny application designed to help non-technical users explore statistical associations within multivariate datasets. Aimed particularly at journalists, educators, and engaged citizens, the tool facilitates the discovery and interpretation of meaningful patterns between variables without requiring programming or statistical expertise. Users can upload structured data (e.g., from surveys or open government datasets), select relevant variables, and dynamically visualize relationships via a correlation network and contextual bivariate plots. To illustrate its capabilities, we present a case study based on the European Social Survey (ESS), showcasing how users can investigate links between attitudes, behaviors, and socio-demographic indicators across countries. The app supports a range of association measures adapted to variable types (Pearson’s

r

, Eta, and Cramer’s V), ensuring both flexibility and statistical rigor. The visual interface enables users to adjust thresholds for association strength and examine results through interactive graphs and summary tables, making the app particularly well-suited for data storytelling, exploratory research, and public communication. AssociationExplorer demonstrates how open-source statistical tools can enhance transparency, accessibility, and insight in the interpretation of complex social data.

AssociationExplorer是一个开源的交互式R Shiny应用程序，旨在帮助非技术用户探索多元数据集中的统计关联。该工具特别针对记者、教育工作者和参与其中的公民，它有助于发现和解释变量之间有意义的模式，而不需要编程或统计专业知识。用户可以上传结构化数据（例如，来自调查或公开的政府数据集），选择相关变量，并通过关联网络和上下文二元图动态可视化关系。为了说明其功能，我们提出了一个基于欧洲社会调查（ESS）的案例研究，展示了用户如何调查各国态度、行为和社会人口指标之间的联系。该应用程序支持一系列适应变量类型的关联度量（Pearson 's r， Eta和Cramer 's V），确保了灵活性和统计严谨性。可视化界面使用户能够调整关联强度的阈值，并通过交互式图表和汇总表检查结果，使该应用程序特别适合数据叙述，探索性研究和公共交流。AssociationExplorer演示了开源统计工具如何在解释复杂的社会数据时提高透明度、可访问性和洞察力。

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引用次数: 0

PermXCT: A novel framework for imaging-based virtual permeability prediction PermXCT：一种基于成像的虚拟渗透率预测框架

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-29 DOI: 10.1016/j.softx.2026.102529

Debabrata Adhikari, Jesper John Lisegaard, Jesper Henri Hattel, Sankhya Mohanty

PermXCT is an open-source computational framework designed to predict virtual permeability in fiber-reinforced polymer composites based on data extracted from X-ray computed tomography (XCT). It provides an automated and reproducible workflow that connects imaging based geometry extraction, mesh generation, and numerical flow simulation for permeability estimation. The framework integrates both mesoscale and microscale morphological characteristics, such as intra and inter-yarn porosity and fiber orientation, to capture realistic flow pathways within complex composite geometries. PermXCT utilises a combination of established open-source tools, including DREAM3D for mesh creation, OpenFOAM for fluid flow simulation, and Python and MATLAB for data processing and automation. Computational efficiency is achieved through optimized meshing strategies and domain scaling, enabling large XCT datasets to be analyzed with reduced computational cost. Validation against experimental permeability measurements demonstrates strong agreement, confirming the reliability and physical accuracy of the imaging based predictions. By minimizing uncertainties and repeatability issues associated with experimental permeability testing, PermXCT provides a robust foundation for XCT-informed virtual permeability characterization.

PermXCT是一个开源计算框架，旨在根据x射线计算机断层扫描（XCT）提取的数据预测纤维增强聚合物复合材料的虚拟渗透率。它提供了一个自动化的、可重复的工作流程，将基于成像的几何形状提取、网格生成和渗透率估计的数值流动模拟连接起来。该框架整合了中尺度和微观尺度的形态特征，如纱线内部和纱线之间的孔隙率和纤维方向，以捕捉复杂复合几何结构中真实的流动路径。PermXCT结合了现有的开源工具，包括用于网格创建的DREAM3D，用于流体流动模拟的OpenFOAM，以及用于数据处理和自动化的Python和MATLAB。通过优化网格策略和域缩放来提高计算效率，使大型XCT数据集能够以更低的计算成本进行分析。与实验渗透率测量值的验证显示了很强的一致性，证实了基于成像预测的可靠性和物理准确性。PermXCT最大限度地减少了与实验渗透率测试相关的不确定性和重复性问题，为基于xct的虚拟渗透率表征提供了坚实的基础。

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引用次数: 0

TorchCor: High-performance cardiac electrophysiology simulations with the finite element method on GPUs TorchCor：基于gpu的有限元方法的高性能心脏电生理模拟

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 10.1016/j.softx.2026.102521

Bei Zhou , Maximilian Balmus , Cesare Corrado , Ludovica Cicci , Shuang Qian , Steven A. Niederer

Cardiac electrophysiology (CEP) simulations are increasingly used for understanding cardiac arrhythmias and guiding clinical decisions. However, these simulations typically require high-performance computing resources with numerous CPU cores, which are often inaccessible to many research groups and clinicians. To address this, we present TorchCor, a high-performance Python library for CEP simulations using the finite element method on general-purpose GPUs. Built on PyTorch, TorchCor significantly accelerates CEP simulations, particularly for large 3D meshes. The accuracy of the solver is verified against manufactured analytical solutions and the

N

-version benchmark problem. TorchCor is freely available for both academic and commercial use without restrictions.

心脏电生理（CEP）模拟越来越多地用于理解心律失常和指导临床决策。然而，这些模拟通常需要具有众多CPU内核的高性能计算资源，这通常是许多研究小组和临床医生无法访问的。为了解决这个问题，我们提出了TorchCor，一个高性能的Python库，用于在通用gpu上使用有限元方法进行CEP模拟。基于PyTorch， TorchCor显著加速了CEP模拟，特别是对于大型3D网格。通过制造的解析解和n版本基准问题验证了求解器的准确性。TorchCor免费提供学术和商业用途，没有任何限制。

引用次数: 0

MPAS-viewer: A Python package for an efficient visualization of the MPAS-atmosphere unstructured mesh mpas查看器：一个Python包，用于有效地可视化mpas大气非结构化网格

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX

Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.softx.2025.102497

Jorge Humberto Bravo Mendez, Marouane Temimi

Visualizing the output of models that use unstructured meshes, such as the Model for Prediction Across Scales Atmosphere (MPAS-A), poses unique challenges. MPAS-A employs a variable-resolution hexagon-based mesh to accurately capture complex geometries and localized phenomena, offering more details where needed and less details elsewhere to reduce computational cost. While MPAS-A input and output data are stored in NetCDF format, their organization by mesh cells rather than regular latitude-longitude grids makes them difficult to visualize using conventional tools. While some tools support MPAS-A data, they often require preprocessing steps to convert the mesh into a more compatible format due to inherent limitations. To address this gap, we present MPAS-Viewer, a lightweight Python-based post-processing tool designed to be efficient, portable across systems, and easy to install with minimal dependencies. It supports both regional and global MPAS-A domains, making it suitable for a wide range of applications. MPAS-Viewer provides an accurate and user-friendly way to visualize MPAS-A data directly on its native mesh, faster compared to similar tools, enabling faster insights and easier exploration.

可视化使用非结构化网格的模型的输出，例如跨尺度大气预测模型（MPAS-A），提出了独特的挑战。MPAS-A采用可变分辨率的六边形网格来精确捕获复杂的几何形状和局部现象，在需要的地方提供更多的细节，在其他地方提供更少的细节，以降低计算成本。虽然MPAS-A的输入和输出数据以NetCDF格式存储，但它们的组织方式是网格单元，而不是常规的经纬度网格，这使得使用传统工具很难将它们可视化。虽然有些工具支持MPAS-A数据，但由于固有的限制，它们通常需要预处理步骤才能将网格转换为更兼容的格式。为了解决这一问题，我们提出了MPAS-Viewer，这是一种轻量级的基于python的后处理工具，旨在高效、跨系统可移植，并且易于安装，依赖关系最小。它支持区域和全球MPAS-A域，适用于广泛的应用。MPAS-Viewer提供了一种准确且用户友好的方式，可以将MPAS-A数据直接在其原生网格上可视化，与类似工具相比速度更快，从而实现更快的见解和更轻松的探索。

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