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CoGenASD: A tool for the co-design and generation of cross-platform applications for people with Autism spectrum disorder CoGenASD：为自闭症谱系障碍患者共同设计和生成跨平台应用程序的工具

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

SoftwareX

Pub Date : 2026-01-11 DOI: 10.1016/j.softx.2026.102512

Yoel Arroyo, Ana I. Molina, Carmen Lacave, Miguel Á. Redondo

Current ASD-focused app development faces key limitations, such as high technical barriers for non-experts, limited personalization, and scarce involvement of therapists, families and educators in the design process. This paper presents CoGenASD, a framework that integrates co-design principles with a Model-Driven Development (MDD) approach to support the semi-automatic generation of cross-platform applications for individuals with ASD. The tool enables multidisciplinary teams (therapists, families and educators) to collaboratively define and model participant profiles, activities, interaction modes and content, supporting the semi-automatic generation of cross-platform, accessible and tailored applications. CoGenASD lowers technical barriers, promotes inclusive design practices, and accelerates the development of support tools. Its potential impact includes increasing application effectiveness, fostering stakeholder engagement, and enabling new research on customizable interventions for neurodiverse populations.

目前以自闭症为中心的应用程序开发面临着一些关键的限制，比如对非专家的高技术壁垒，有限的个性化，以及在设计过程中治疗师，家庭和教育工作者的参与很少。本文介绍了CoGenASD，一个将协同设计原则与模型驱动开发（MDD）方法集成在一起的框架，以支持ASD患者跨平台应用程序的半自动生成。该工具使多学科团队（治疗师、家庭和教育工作者）能够协同定义和建模参与者的个人资料、活动、交互模式和内容，支持半自动生成跨平台、可访问和定制的应用程序。CoGenASD降低了技术壁垒，促进了包容性设计实践，并加速了支持工具的开发。它的潜在影响包括提高应用效率，促进利益相关者的参与，以及为神经多样性人群提供可定制干预措施的新研究。

引用次数: 0

CIDS-Sim: Simulator for collaborative intrusion detection system based on federated learning CIDS-Sim：基于联邦学习的协同入侵检测系统模拟器

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

SoftwareX

Pub Date : 2026-01-10 DOI: 10.1016/j.softx.2026.102511

Aulia Arif Wardana , Grzegorz Kołaczek , Parman Sukarno

This research introduces CIDS-Sim, a simulator for Collaborative Intrusion Detection Systems (CIDS) based on federated learning, addressing the complexity of coordinated attacks on networks. Traditional Intrusion Detection Systems (IDS) are limited by isolated operations and privacy concerns. CIDS-Sim leverages federated learning to maintain data privacy while enabling collaborative anomaly detection. It assesses collaboration strategies, federated learning’s privacy-performance trade-offs, and different attack vectors and defenses. CIDS-Sim is a critical tool for researchers and practitioners to develop secure IDS solutions, offering a robust platform for simulating and evaluating the dynamics of collaborative defense strategies. CIDS-Sim is also suitable for educators or lecturers who want to teach the concept of CIDS.

本研究引入基于联邦学习的协同入侵检测系统（CIDS）模拟器CIDS- sim，解决网络协同攻击的复杂性问题。传统的入侵检测系统（IDS）受到隔离操作和隐私问题的限制。CIDS-Sim利用联邦学习来维护数据隐私，同时支持协作异常检测。它评估了协作策略、联邦学习的隐私-性能权衡，以及不同的攻击向量和防御。IDS- sim是研究人员和从业人员开发安全IDS解决方案的关键工具，为模拟和评估协作防御策略的动态提供了强大的平台。CIDS- sim也适合想要教授CIDS概念的教育工作者或讲师。

引用次数: 0

QOPTec: a modular platform for benchmarking quantum algorithms through combinatorial optimization problems QOPTec：通过组合优化问题对量子算法进行基准测试的模块化平台

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

SoftwareX

Pub Date : 2026-01-09 DOI: 10.1016/j.softx.2026.102507

Pablo Miranda-Rodríguez, Eneko Osaba

Combinatorial optimization is a critical field in many applications that remains challenging due to its general computational complexity. Quantum computing is believed to be a promising alternative to classical methods to solve these types of problems. We introduce QOPTec, a Python library for benchmarking optimization problems using quantum or hybrid solvers. QOPTec offers a simple, extensible framework for reproducible evaluation of solver performance. By enabling integration of new problems and algorithms, the tool aims to lower the entry barrier to quantum optimization and supports systematic studies of different solver approaches, helping assess their practical potential as quantum technologies evolve.

组合优化是许多应用中的一个关键领域，由于其一般的计算复杂性，仍然具有挑战性。量子计算被认为是解决这类问题的经典方法的一个有前途的替代方案。我们介绍QOPTec，一个Python库，用于使用量子或混合求解器对优化问题进行基准测试。QOPTec为求解器性能的可重复评估提供了一个简单的、可扩展的框架。通过整合新问题和算法，该工具旨在降低量子优化的进入门槛，并支持对不同求解器方法的系统研究，随着量子技术的发展，帮助评估它们的实际潜力。

引用次数: 0

PyPortTickerSelector: A top tickers selection library using multiple indicators, performance metrics, strategies with benchmark PyPortTickerSelector：一个顶级的股票选择库，使用多个指标、性能指标和基准策略

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

SoftwareX

Pub Date : 2026-01-08 DOI: 10.1016/j.softx.2025.102506

Rushikesh Nakhate , Harikrishnan Ramachandran , Neeraj Kumar Shukla

This paper presents PyPortTickerSelector, an automated ticker selection library designed to identify top-performing tickers based on predefined and user-defined strategies. The library supports various methods to calculate multiple indicators and performance-metrics. Users have the flexibility to customize the ticker selection process at every step, using built-in options or their own methods. The library achieves improved computational efficiency over manual analysis while maintaining approx 90 % test coverage for business logic. Validation includes comparison against benchmark performance, latency profiling, memory usage optimization, and statistical significance testing, addressing critical gaps in quantitative finance tooling. The library allows seamless integration with the PyPortOptimization Pipeline for portfolio construction.

本文介绍了PyPortTickerSelector，这是一个自动报价器选择库，旨在根据预定义和用户定义的策略识别性能最好的报价器。该库支持多种方法来计算多个指标和性能指标。用户可以使用内置选项或自己的方法，灵活地定制每一步的行情选择过程。该库在为业务逻辑保持大约90%的测试覆盖率的同时，实现了比手工分析更高的计算效率。验证包括对基准性能的比较、延迟分析、内存使用优化和统计显著性测试，以解决定量金融工具中的关键差距。该库允许与PyPortOptimization Pipeline无缝集成以进行投资组合构建。

引用次数: 0

Version [2.0]-[NeoCoMM: Neocortical neuro-inspired computational model for realistic microscale simulations] 版本[2.0]-[NeoCoMM：用于现实微观模拟的新皮层神经启发计算模型]

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

SoftwareX

Pub Date : 2026-01-07 DOI: 10.1016/j.softx.2025.102505

M. Yochum , F. Karimi , F. Wendling, M. Al Harrach

In this new version of the NeoCoMM (Neocortical Computational Microscale Model) software, we present an updated neuroinspired computational model of the cortical column that includes neuroplasticity and a transcranial Electric Stimulation (tES) modeling platform. The neuroplasticity update consists of three types of long- term plasticity models based on the calcium dynamics that are incorporated into the principal cells (PCs) of the network. For tES, a new panel in the GUI was added to simulate the electric field parameters allowing the user to simulate the impact of both Direct (tDCS) and Alternating (tACS) Current Stimulation on the network dynamics.

在这个新版本的NeoCoMM（新皮质计算微尺度模型）软件中，我们提出了一个更新的神经启发的皮质柱计算模型，包括神经可塑性和经颅电刺激（tES）建模平台。神经可塑性更新包括三种基于钙动力学的长期可塑性模型，这些模型被纳入网络的主细胞（pc）。对于tES， GUI中增加了一个新的面板来模拟电场参数，允许用户模拟直接（tDCS）和交流（tACS）电流刺激对网络动态的影响。

引用次数: 0

PyCompact: An integrated workflow for discrete element method–multi-particle finite element method for powder compaction simulation PyCompact：一个集成的离散元方法工作流-粉末压实模拟的多粒子有限元方法

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

SoftwareX

Pub Date : 2026-01-06 DOI: 10.1016/j.softx.2025.102495

Majid Mohammadhosseinzadeh, Hossein Ghorbani-Menghari, Ji Hoon Kim

This study introduces an integrated workflow for simulating powder compaction through a hybrid discrete element method (DEM) and multi-particle finite element method (MPFEM) approach. PyCompact, by integrating FreeCAD, LIGGGHTS, ParaView, LS-PrePost, and OpenRadioss with two in-house Python scripts for automated data translation and mesh generation, the framework covers the full simulation cycle: geometric modelling, particle generation, finite element meshing, compaction analysis, and visualization. The workflow was validated using experimental data from two Fe-Si-Al-P powders with distinct particle size distributions. Results demonstrated a maximum relative density deviation of only 2.5 % compared to experiments, matching ABAQUS predictions. This work introduces the first validated DEM-MPFEM framework that eliminates licensing barriers for the core simulation steps, enhances reproducibility, and adapts to various powder compaction applications in academic and industrial settings.

本文介绍了一种基于离散元法（DEM）和多粒子有限元法（MPFEM）的粉末压实模拟集成工作流程。PyCompact通过将FreeCAD、lights、ParaView、LS-PrePost和OpenRadioss与两个内部Python脚本集成在一起，用于自动数据转换和网格生成，该框架涵盖了整个仿真周期：几何建模、粒子生成、有限元网格划分、压缩分析和可视化。用两种不同粒度分布的Fe-Si-Al-P粉末的实验数据验证了该工作流程。结果表明，与实验相比，最大相对密度偏差仅为2.5%，与ABAQUS预测相符。这项工作引入了第一个经过验证的DEM-MPFEM框架，消除了核心模拟步骤的许可障碍，提高了可重复性，并适应了学术和工业环境中的各种粉末压实应用。

引用次数: 0

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 : 2026-01-06 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

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 : 2026-01-06 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 : 2026-01-06 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 : 2026-01-06 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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