MethodsX最新文献_第3页

On the implementation of maximum entropy sampling with unequal probabilities and without replacement 不等概率无替换的最大熵抽样的实现

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-30 DOI: 10.1016/j.mex.2025.103780

Philippe Aubry

Sampling with maximum entropy offers robustness to statistical inference based on randomization theory. However, there were no comprehensive, practical guides explaining how to implement maximum entropy sampling for finite populations with unequal probabilities and without replacement. This article serves as both a toolkit and a reference guide for researchers and engineers, filling a gap in the literature. It links key formal results with ready-to-use algorithms that can be implemented in any procedural programming language. Maximum entropy sampling is straightforward when the sample size is allowed to vary. This is achieved via the Poisson sampling design, in which the sample size is a random variable distributed according to a Poisson binomial distribution. In contrast, the conditional Poisson sampling design, which is obtained by conditioning Poisson sampling on a fixed sample size, has long posed a significant challenge to statisticians.

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A compendium of formal results for Poisson sampling, the Poisson binomial distribution, and conditional Poisson sampling is presented.
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The computation of inclusion probabilities up to the second order is detailed for the conditional Poisson sampling, and the corresponding algorithms are provided.
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Ready-to-use algorithms are provided for implementing Poisson sampling and the Poisson binomial distribution. For conditional Poisson sampling, the rejective, draw-by-draw, sequential, and exchange sampling algorithms are detailed.

最大熵抽样对基于随机化理论的统计推断具有鲁棒性。然而，没有全面的、实用的指南来解释如何在不相等概率和不替换的有限种群中实现最大熵抽样。本文作为研究人员和工程师的工具箱和参考指南，填补了文献中的空白。它将关键的形式化结果与可用的算法联系起来，这些算法可以用任何过程编程语言实现。当允许样本量变化时，最大熵抽样是直接的。这是通过泊松抽样设计实现的，其中样本量是根据泊松二项分布分布的随机变量。相比之下，条件泊松抽样设计是通过固定样本量的泊松抽样来获得的，长期以来对统计学家提出了重大挑战。•介绍了泊松抽样、泊松二项分布和条件泊松抽样的形式结果汇编。•对于条件泊松采样，详细介绍了二阶包含概率的计算，并提供了相应的算法。•准备使用的算法提供了实现泊松采样和泊松二项分布。对于条件泊松采样，拒绝，逐抽取，顺序和交换采样算法进行了详细介绍。

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引用次数: 0

Combination of partial least square structural equation modeling scheme of principal component analysis with importance performance analysis 主成分分析的偏最小二乘结构方程建模方案与重要性能分析的结合

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-30 DOI: 10.1016/j.mex.2025.103783

Bambang Widjanarko Otok, Zulfani Alfasanah, Diaz Fitra Aksioma

Structural Equation Modeling (SEM) is widely used to assess causal relationships among latent variables, yet its strict assumptions often limit empirical applications. Partial Least Squares SEM (PLS-SEM) offers greater flexibility, but the choice of weighting scheme remains a methodological challenge. This study introduces a PCA-based weighting scheme to improve the stability and accuracy of PLS estimation. Importance-Performance Analysis (IPA) is further integrated to identify high-impact but underperforming indicators. Applied to child malnutrition in East Java, the approach reveals that socio-economic conditions most strongly influence food security, parenting, and health–environment services. IPA highlights exclusive breastfeeding as a priority for intervention. The proposed methodological approach strengthens PLS estimation and yields actionable insights for prioritizing policy measures.

结构方程模型（SEM）被广泛用于评估潜在变量之间的因果关系，但其严格的假设往往限制了实证应用。偏最小二乘扫描电镜（PLS-SEM）提供了更大的灵活性，但加权方案的选择仍然是一个方法上的挑战。为了提高PLS估计的稳定性和准确性，本文引入了一种基于pca的加权方案。重要性-绩效分析（IPA）进一步整合，以确定高影响但表现不佳的指标。该方法适用于东爪哇儿童营养不良问题，结果表明，社会经济条件对粮食安全、养育子女和卫生环境服务的影响最大。国际出版商协会强调纯母乳喂养是干预的重点。提出的方法方法加强了PLS估计，并为优先考虑政策措施提供了可操作的见解。

引用次数: 0

Graph neural network-based mutation-aware regression test ordering using code dependency graphs and execution traces 使用代码依赖图和执行轨迹绘制基于神经网络的突变感知回归测试排序图

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-25 DOI: 10.1016/j.mex.2025.103782

S Sowmyadevi, Anna Alphy

The mutation-aware test prioritisation system in this paper uses Graph Neural Networks (GNNs) to combine static program structure, dynamic execution traces, and mutation coverage into a hybrid graph representation to enhance regression testing. The framework embeds higher-order dependencies in test cases using GCN, GAT, and GraphSAGE variations and ranks them using a multi-objective optimisation function that balances fault detection, execution cost, and mutation coverage. On benchmark datasets like Defects4J and ManySStuBs4J, the proposed approach consistently outperforms traditional baselines (coverage-based APFD = 72.4 %, cost-based = 74.5 %) and ML baselines (LSTM = 80.1 %, RL = 82.7 %), achieving an average APFD of 88.9 % and mutation score of 84.6 % with a 16.1-second execution overhead. Statistical tests (Wilcoxon signed-rank, p < 0.05) indicate the robustness of these gains. Ablation experiments show that removing execution traces or mutation characteristics reduces APFD by 5–8 %, emphasising their relevance. Qualitative research shows that GNN embeddings cluster fault-related test cases for interpretable prioritisation. The suggested paradigm for contemporary regression testing is scalable, accurate, and mutation-driven.

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Multi-Tiered Graph-Based Architecture: The method transforms raw program artifacts (codebase, mutants, test traces) into Program Dependence Graphs and Call Graphs, where nodes represent program elements and edges capture dependencies enriched with runtime characteristics.
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GNN-Powered Multi-Objective Optimization: Core innovation uses Graph Neural Networks (GCN, GAT, GraphSAGE) to create enriched embeddings through iterative neighborhood aggregation, feeding into a scoring function that balances fault detection potential, execution cost, and mutation coverage.
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Superior Validated Performance: Achieves 88.9 % APFD compared to 82.7 % for best baseline methods on real-world datasets, with statistical significance confirmed through Wilcoxon signed-rank tests across multiple evaluation metrics.

本文的突变感知测试优先级排序系统使用图神经网络（gnn）将静态程序结构、动态执行轨迹和突变覆盖组合成混合图表示，以增强回归测试。该框架使用GCN、GAT和GraphSAGE变体在测试用例中嵌入高阶依赖，并使用平衡故障检测、执行成本和突变覆盖的多目标优化功能对它们进行排序。在像Defects4J和ManySStuBs4J这样的基准数据集上，所提出的方法始终优于传统基线（基于覆盖率的APFD = 72.4%，基于成本的= 74.5%）和ML基线（LSTM = 80.1%, RL = 82.7%），平均APFD为89.9%，突变分数为84.6%，执行开销为16.1秒。统计检验（Wilcoxon signed-rank, p < 0.05）表明这些增益的稳健性。消融实验表明，去除执行痕迹或突变特征可使APFD降低5 - 8%，强调其相关性。定性研究表明，GNN嵌入聚类与故障相关的测试用例，以实现可解释的优先级。当代回归测试的建议范例是可伸缩的、准确的和突变驱动的。•多层基于图的体系结构：该方法将原始程序工件（代码库，突变体，测试跟踪）转换为程序依赖图和调用图，其中节点表示程序元素，边缘捕获具有运行时特征的依赖关系。•基于gnn的多目标优化：核心创新使用图神经网络（GCN， GAT, GraphSAGE）通过迭代邻域聚合来创建丰富的嵌入，并将其输入到平衡故障检测潜力，执行成本和突变覆盖的评分函数中。•卓越的验证性能：实现88.9%的APFD，而在真实数据集上，最佳基线方法的APFD为82.7%，通过多个评估指标的Wilcoxon签名秩检验证实了统计显著性。

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引用次数: 0

Rebuilding life after heart valve surgery: The VALCAR(E)_QoL study on rehabilitation and quality of life 心脏瓣膜手术后重建生活：康复和生活质量的VALCAR(E)_QoL研究

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-25 DOI: 10.1016/j.mex.2025.103781

Piergiuseppe Liuzzi , Camilla Elena Magi , Paolo Iovino , Ercole Vellone , Andrea Mannini , Claudio Macchi , Biagio Nicolosi , Mariachiara Figura , Francesco Limonti , Duccio Frangi , Hamilton Dollaku

The VALCAR(E)_QoL study is a prospective, mixed-methods observational investigation designed to characterize the rehabilitation process and determinants of health-related quality of life in patients undergoing cardiac valve surgery. Conducted at the IRCCS Fondazione Don Carlo Gnocchi in Florence (Italy), the study integrates quantitative and qualitative approaches to capture the multidimensional nature of recovery within the International Classification of Functioning, Disability and Health (ICF) framework.

Consecutive patients admitted for inpatient cardiac rehabilitation after valve surgery will be assessed at four time points: pre-surgery (T0), rehabilitation admission (T1), discharge (T2), and six-month follow-up (T3). Data collection includes clinical, functional, and psychosocial indicators, as well as patient-reported outcomes such as the Kansas City Cardiomyopathy Questionnaire (primary outcome), SF-12 Health Survey, and standardized measures of anxiety, depression, stress, self-care, and sleep quality. Qualitative interviews at discharge complement quantitative findings by exploring patients’ emotional experiences, perceived barriers, and facilitators of recovery.

All data are collected in pseudonymized form through a secure REDCap database and analyzed using multivariate and thematic techniques. This protocol adheres to the STROBE guidelines to ensure methodological transparency and reproducibility.

By integrating objective measures and subjective experiences, the VALCAR(E)_QoL study aims to identify clinical and psychosocial predictors of successful rehabilitation and to provide evidence for patient-centered, multidisciplinary models of post-surgical care for individuals recovering from heart valve surgery.

VALCAR(E)_QoL研究是一项前瞻性、混合方法的观察性研究，旨在描述心脏瓣膜手术患者的康复过程和健康相关生活质量的决定因素。这项研究是在佛罗伦萨（意大利）的国际康复与康复中心Don Carlo Gnocchi基金会进行的，它综合了定量和定性方法，以便在国际功能、残疾和健康分类框架内捕捉康复的多层面性质。在术前（T0）、康复入院（T1）、出院（T2）和6个月随访（T3）四个时间点对瓣膜术后连续住院心脏康复患者进行评估。数据收集包括临床、功能和社会心理指标，以及患者报告的结果，如堪萨斯城心肌病问卷（主要结果）、SF-12健康调查，以及焦虑、抑郁、压力、自我保健和睡眠质量的标准化测量。出院时的定性访谈通过探索患者的情绪体验、感知障碍和康复促进因素来补充定量调查结果。所有数据通过安全的REDCap数据库以假名形式收集，并使用多元和主题技术进行分析。本方案遵循STROBE指南，以确保方法的透明度和可重复性。通过综合客观测量和主观经验，VALCAR(E)_QoL研究旨在确定成功康复的临床和社会心理预测因素，并为心脏瓣膜手术患者康复的多学科术后护理模式提供证据。

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引用次数: 0

Real-time vehicle control via edge cloud sensor fusion and CNN based perceptron 基于边缘云传感器融合和CNN感知器的实时车辆控制

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-24 DOI: 10.1016/j.mex.2025.103779

Sumukh Chaurasia , Parambrata Sanyal , Gagandeep Kaur , Satvik Barhanpure , Kshitij Bhele , Amol D. Wable , Suhashini Awadhesh Chaurasia , Rutuja Rajendra Patil , Devika Verma

Reliable real-time vehicle control is essential for intelligent transport systems where accurate perception and decision-making depend on fast sensor data processing. This study developed a hybrid edge–cloud method integrating deep learning with Internet of Things (IoT) sensor fusion for adaptive vehicle control. Ultrasonic range data were combined with convolutional neural networks (CNNs) to enable object detection, stopping-time prediction, and braking control under varying environmental conditions. The CNN-based model was trained and evaluated under normal and simulated adverse driving scenarios. Results indicated strong performance with R² = 0.99 under normal and 0.98 under adverse conditions, and a mean squared error (MSE) of 0.0085. Average inference latency is 110–116 ms on Jetson Nano and 210–230 ms on Raspberry Pi, confirming suitability for real-time deployment on edge hardware.

The hybrid edge–cloud method enables adaptive, real-time vehicle control through IoT sensor fusion.

CNN-based perception enhances prediction accuracy and operational safety under variable driving conditions.

Demonstrates feasibility of deep learning deployment on low-cost edge devices for intelligent transport applications.

Thus, integrating deep learning with IoT-enabled sensors on an edge–cloud platform provides a reliable and scalable pathway toward safe, adaptive, and efficient vehicle control in intelligent transportation systems.

可靠的实时车辆控制对于智能交通系统至关重要，因为智能交通系统的准确感知和决策依赖于快速的传感器数据处理。本研究开发了一种融合深度学习和物联网传感器融合的混合边缘云方法，用于自适应车辆控制。超声波距离数据与卷积神经网络（cnn）相结合，实现了不同环境条件下的目标检测、停车时间预测和制动控制。基于cnn的模型在正常和模拟的不良驾驶场景下进行训练和评估。结果表明，在正常条件下R²= 0.99，在不良条件下R²= 0.98，均方误差（MSE）为0.0085。Jetson Nano上的平均推理延迟为110-116毫秒，Raspberry Pi上的平均推理延迟为210-230毫秒，证实了在边缘硬件上实时部署的适用性。混合边缘云方法通过物联网传感器融合实现自适应实时车辆控制。基于cnn的感知增强了在可变驾驶条件下的预测精度和操作安全性。展示了深度学习在智能交通应用的低成本边缘设备上部署的可行性。因此，将深度学习与支持物联网的传感器集成在边缘云平台上，为智能交通系统中的安全、自适应和高效车辆控制提供了可靠且可扩展的途径。

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引用次数: 0

Construction and applications of iterative methods for finding approximate solutions of nonlinear equations having unknown zeros of multiplicity with fractal geometry and dynamical behavior 具有分形几何和动力学特性的未知零点非线性方程近似解的迭代方法的构造和应用

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-19 DOI: 10.1016/j.mex.2025.103778

Farooq Ahmed Shah , Iftikhar Haider , Muhammad Waseem , Alexey Mikhaylov , Nora Baranyai

In this study, several new iterative schemes are developed to compute the multiple zeros of nonlinear equations. The construction of these methods is based on the variational iteration approach which provides a systematic framework for formulating efficient and flexible algorithms. The proposed schemes generalize and encompass well-known classical methods such as Newton’s and Halley’s methods, along with their modified versions as special cases. This generality enhances their adaptability to a broader class of nonlinear problems involving both known and unknown multiplicities. To assess the effectiveness of the proposed iterative schemes, extensive numerical experiments are conducted, comparing their convergence speed and accuracy with existing methods. The results demonstrate that the newly developed methods exhibit superior performance in terms of stability, precision, and computational efficiency. Furthermore, to visualize and analyse the global convergence behaviour fractal basin plots are presented. These fractals illustrate the basins of attraction in the complex plane, providing deeper insight into the dynamical behaviour, convergence regions and boundary structures associated with each iterative process.

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Developing efficient and flexible iterative methods using the variational iteration method.
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Generalizing classical methods to tackle nonlinear problems with known and unknown multiplicity.
•
Validating performance of various methods through detailed numerical experiments and fractal basin plots.

在本研究中，提出了几种新的迭代格式来计算非线性方程的多重零。这些方法的构造基于变分迭代法，为制定高效灵活的算法提供了系统框架。所提出的方案概括并包含了著名的经典方法，如牛顿和哈雷的方法，以及作为特殊情况的修改版本。这种普遍性增强了它们对更广泛的非线性问题的适应性，包括已知和未知的多重性。为了评估所提出的迭代方案的有效性，进行了大量的数值实验，比较了它们与现有方法的收敛速度和精度。结果表明，新方法在稳定性、精度和计算效率方面表现出优异的性能。此外，为了可视化和分析全局收敛行为，提出了分形盆地图。这些分形说明了复杂平面上的吸引力盆地，为每个迭代过程相关的动力学行为、收敛区域和边界结构提供了更深入的了解。•使用变分迭代法开发高效灵活的迭代方法。•推广经典方法来解决具有已知和未知多重性的非线性问题。•通过详细的数值实验和分形盆地图验证各种方法的性能。

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引用次数: 0

Analyzing the mahakam river water quality using the geographically weighted panel regression model 利用地理加权面板回归模型分析玛哈干河水质

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-19 DOI: 10.1016/j.mex.2025.103773

Zabrina Nathania Fauziyah, Suyitno Suyitno, Darnah, Memi Nor Hayati, Meirinda Fauziyah

This study discusses the geographically weighted panel regression (GWPR) model. GWPR is an extension of geographically weighted regression model, designed for spatially heterogeneous panel data. In this study, GWPR model is applied to panel data on biochemical oxygen demand (BOD) in Mahakam River water 2022–2024. The model is estimated at each spatial location using a fixed effects model (FEM) as the global model, with temporal effects addressed through a demeaning transformation. All statistical analyses and spatial processing are conducted using R software, GNU Octave, QGIS, and Google Earth. This study aims to map factors influencing Mahakam River water BOD using GWPR model. The results indicate that GWPR outperforms FEM, with AIC = -60.6419,

R^{2} = 80.321 %

, and root mean square error of 0.7122. The factors influencing BOD include temperature, water pH, color degree, nitrate, ammonia, total suspended solids, and sulfate.

•
We present a GWPR model using FEM as global model, applied to the spatially heterogeneous panel data, namely demeaned Mahakam River water BOD data 2022–2024.
•
The mapping of factors influencing BOD is analyzed locally using GWPR model.
•
The optimal adaptive bandwidth is determined using Akaike Information Criterion, and model goodness-of-fit is evaluated using the coefficient of determination and root mean square error.

本研究探讨地理加权面板回归（GWPR）模型。GWPR是对地理加权回归模型的扩展，是针对空间异质性面板数据而设计的。本研究将GWPR模型应用于2022-2024年Mahakam河水体生化需氧量（BOD）面板数据。该模型在每个空间位置使用固定效应模型（FEM）作为全局模型进行估计，并通过降级转换处理时间效应。所有的统计分析和空间处理使用R软件、GNU Octave、QGIS和谷歌Earth进行。本研究旨在利用GWPR模型绘制马哈坎河水BOD的影响因子。结果表明，GWPR优于FEM， AIC = -60.6419, R2=80.321%，均方根误差为0.7122。影响BOD的因素包括温度、水pH、色度、硝酸盐、氨、总悬浮物和硫酸盐。•我们提出了一个GWPR模型，将FEM作为全球模型，应用于空间异质性面板数据，即2022-2024年Mahakam河水BOD数据。•利用GWPR模型局部分析了BOD影响因素的映射。•使用赤池信息准则确定最优自适应带宽，使用决定系数和均方根误差评估模型的拟合优度。

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引用次数: 0

The integration of virtual reality and EEG: A step-by-step guideline 虚拟现实和脑电图的集成：一步一步的指导方针

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-18 DOI: 10.1016/j.mex.2025.103770

Caspar Krampe , Juriaan Wolfers , Philip Dean

Virtual Reality (VR) is gaining traction in cognitive and decision-making research because of its ability to generate immersive, controlled environments that closely replicate real-world situations. Its integration with neurophysiological tools such as electroencephalography (EEG) and eye-tracking offers a unique opportunity to gain deep insights into consumer behaviour by combining behavioural and neural measures in real-time. However, the simultaneous use of VR and neurophysiological measures remains challenging due to crucial issues concerning data stream alignment, event timestamping, hardware compatibility, and potential signal interference induced by head-mounted equipment. To date, the absence of standardised protocols has limited the scalability and reproducibility of multimodal VR research, thereby hindering its widespread adoption. This paper presents a detailed, step-by-step guideline for harmonising EEG, eye-tracking, and VR data streams using the Lab Streaming Layer (LSL) in a Unity-based VR environment. A Varjo headset with in-built eye-tracking and a Neuroelectrics Enobio EEG system are used as a working case to illustrate a practical implementation of the guidelines displayed. By outlining clear guidelines for hardware configuration, event timestamping and software implementation, this paper demonstrates how open-source tools can enable high-precision data synchronisation in immersive research setting. The protocol is flexible and transferable to similar setups and therefore supports cross-study comparability and encourages wider uptake of multimodal VR methodologies, while acknowledging methodological constraints.

虚拟现实（VR）在认知和决策研究中越来越受欢迎，因为它能够产生沉浸式的、受控的环境，密切复制现实世界的情况。它与脑电图（EEG）和眼动追踪等神经生理学工具的结合，通过实时结合行为和神经测量，为深入了解消费者行为提供了独特的机会。然而，由于数据流对齐、事件时间戳、硬件兼容性以及头戴式设备引起的潜在信号干扰等关键问题，同时使用VR和神经生理测量仍然具有挑战性。迄今为止，标准化协议的缺乏限制了多模态VR研究的可扩展性和可重复性，从而阻碍了其广泛采用。本文介绍了在基于unity的VR环境中使用实验室流层（LSL）协调EEG，眼动追踪和VR数据流的详细，逐步指南。以内置眼动追踪的Varjo头戴式耳机和Neuroelectrics Enobio EEG系统为例，说明了所展示指南的实际实现。通过概述硬件配置，事件时间戳和软件实现的明确指导方针，本文演示了开源工具如何在沉浸式研究环境中实现高精度数据同步。该协议是灵活的，可转移到类似的设置，因此支持交叉研究的可比性，并鼓励更广泛地采用多模式虚拟现实方法，同时承认方法上的限制。

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引用次数: 0

Systematic review of gabion-faced geogrid and pile systems for slope and embankment stability 格宾网面土工格栅和桩系统对边坡和路堤稳定性的系统评价

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103767

Devi Oktaviana Latif, Virananda Samudera Rahmadhian, Amalia Ula Hazhiyah

Slope instability is a major geotechnical hazard intensified by rainfall infiltration, seismic loading, groundwater fluctuations, and human disturbances. Composite reinforcement systems—such as gabion-faced geogrid walls combined with piles or geosynthetic-encased columns (GECs)—are increasingly implemented to address multi-hazard conditions. This study presents a PRISMA-guided systematic review of empirical, numerical, centrifuge, and field investigations on hybrid slope-stabilization systems. The review advances prior work by explicitly incorporating multi-field coupling and soil–structure interaction (SSI) terms into the search strategy, applying transparent screening and data-extraction procedures supported by a reusable metadata codebook, and conducting cross-study triangulation across field evidence, centrifuge modelling, and 2D/3D numerical analyses. The synthesized evidence shows that hybrid systems can significantly enhance slope performance, with reported improvements of up to ∼45 % in factor of safety and >30 % reduction in settlement, depending on reinforcement configuration, soil conditions, and coupled rainfall–seismic effects. The study further highlights current limitations in optimisation practice, long-term monitoring, and design standardisation, and outlines directions for uncertainty-aware and performance-based slope design.

边坡失稳是受降雨入渗、地震荷载、地下水波动和人为干扰加剧的主要岩土工程灾害。复合加固系统，如石笼面土工格栅墙与桩或土工合成材料包裹柱（gec）相结合，越来越多地用于解决多重灾害条件。本研究提出了prisma指导的经验，数值，离心机和混合边坡稳定系统的实地调查系统综述。该综述通过明确地将多场耦合和土壤-结构相互作用（SSI）术语纳入搜索策略，应用透明筛选和数据提取程序（由可重复使用的元数据代码本支持），以及跨场证据进行交叉研究三角测量，离心机建模和2D/3D数值分析，推进了先前的工作。综合证据表明，混合系统可以显著提高边坡性能，据报道，根据加固配置、土壤条件和降雨-地震耦合效应，混合系统的安全系数提高了45%，沉降减少了30%。该研究进一步强调了当前优化实践、长期监测和设计标准化方面的局限性，并概述了不确定性意识和基于性能的边坡设计的方向。

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引用次数: 0

BioMedStatX – Statistical workflows for reliable biomedical data analysis 用于可靠生物医学数据分析的统计工作流程

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103776

Philipp Krumm , Nicole Böttcher , Richard Ottermanns , Thomas Pufe , Athanassios Fragoulis

Robust statistical analysis is essential for scientific validity and to ensure good scientific practice. Yet many researchers, especially in biomedical fields, struggle with checking assumptions, selecting the correct tests, and interpreting results. These obstacles can lead to misleading conclusions and undermine scientific progress.

BioMedStatX explicitly addresses these issues by ensuring that the implemented workflows exclude the use of inadequate statistical tests. This Python-based desktop application features an intuitive graphical interface that automatically selects appropriate statistical tests based on the data and its characteristics, ensuring that users, even with minor statistical training, follow a statistically valid workflow.

Users can import Excel or CSV files, select groups and let BioMedStatX manage the rest: from outlier detection, assumption checks and guided data transformations to test execution (parametric or non-parametric) and guided post-hoc analyses. Results are exported in a structured Excel workbook including a decision tree that visualizes each analytical step, and customizable plots are exported as SVG-/PNG-files.

By embedding statistical expertise directly into the software, BioMedStatX prevents invalid analysis paths, increases transparency, and enables reproducibility.

稳健的统计分析对于科学有效性和确保良好的科学实践至关重要。然而，许多研究人员，特别是生物医学领域的研究人员，在检查假设、选择正确的测试和解释结果方面遇到了困难。这些障碍可能导致误导性结论，破坏科学进步。通过确保实施的工作流程排除使用不充分的统计测试，BioMedStatX明确解决了这些问题。这个基于python的桌面应用程序具有直观的图形界面，可以根据数据及其特征自动选择适当的统计测试，从而确保用户即使没有受过多少统计培训，也能遵循统计上有效的工作流程。用户可以导入Excel或CSV文件，选择组，并让BioMedStatX管理其余部分：从异常值检测，假设检查和指导数据转换到测试执行（参数或非参数）和指导事后分析。结果导出到一个结构化的Excel工作簿中，其中包括一个可视化每个分析步骤的决策树，可自定义的绘图导出为SVG / png文件。通过将统计专业知识直接嵌入到软件中，BioMedStatX防止了无效的分析路径，增加了透明度，并实现了可重复性。

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引用次数: 0