MethodsX最新文献_第4页

BRAIN-META: A reproducible CNN–vision transformer meta-ensemble pipeline for explainable brain tumor classification brain - meta：一个可重复的cnn -视觉转换器元集成管道，用于解释脑肿瘤分类

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103769

Komal Kumar Napa , Sangeetha Murugan , J.Senthil Murugan , A. Jayanthi

This study presents BRAIN-META, a reproducible deep learning methodology designed for multi-class brain tumor classification using structural MRI. The proposed approach combines ten hybrid CNN–Vision Transformer (ViT) models with a meta-learning ensemble framework. The dataset includes 2D MRI images representing four tumor categories: glioma, meningioma, pituitary, and notumor. A standardized preprocessing pipeline involving image resizing, normalization, and CLAHE (Contrast Limited Adaptive Histogram Equalization) is applied to improve image quality and feature visibility. Ten pre-trained CNN architectures—DenseNet121, DenseNet169, DenseNet201, MobileNet, MobileNetV2, EfficientNetB0, EfficientNetB1, EfficientNetB4, InceptionV3, and Xception—are fused with Vision Transformer blocks to extract both local and global features. Each CNN-ViT model is trained independently, and the softmax outputs from validation data are used to generate stacked feature vectors. These vectors are input to two meta-learners, Logistic Regression and XGBoost, which are trained to produce final predictions. Evaluation metrics include accuracy, precision, recall, F1-score, and confusion matrix. XGBoost meta-learner achieved the highest accuracy of 97.10%, followed by Logistic Regression meta-learner at 97.03%, outperforming all individual base models. To enhance interpretability, Grad-CAM was employed, visually highlighting regions influencing classification. The proposed method is accurate, explainable, and modular, making it a strong candidate for clinical decision support in neuro-oncology.

本研究提出了brain - meta，一种可重复的深度学习方法，用于使用结构MRI对多类脑肿瘤进行分类。该方法将十种混合CNN-Vision Transformer （ViT）模型与元学习集成框架相结合。该数据集包括代表四种肿瘤类别的二维MRI图像：胶质瘤、脑膜瘤、垂体和非肿瘤。一个标准化的预处理管道，包括图像调整大小，归一化和CLAHE（对比度有限自适应直方图均衡化），用于提高图像质量和特征可见性。十个预训练的CNN架构- densenet121， DenseNet169, DenseNet201, MobileNet, MobileNetV2, EfficientNetB0, EfficientNetB1, EfficientNetB4， InceptionV3和例外-与Vision Transformer块融合以提取局部和全局特征。每个CNN-ViT模型都是独立训练的，使用验证数据的softmax输出来生成堆叠的特征向量。这些向量被输入到两个元学习器，逻辑回归和XGBoost，它们被训练来产生最终的预测。评估指标包括准确性、精密度、召回率、f1分数和混淆矩阵。XGBoost元学习器的准确率最高，为97.10%，其次是Logistic回归元学习器，准确率为97.03%，优于所有个体基础模型。为了提高可解释性，采用了Grad-CAM，直观地突出了影响分类的区域。所提出的方法是准确的，可解释的，模块化的，使其成为神经肿瘤学临床决策支持的有力候选人。

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引用次数: 0

Resume data extract and job recruitment Chatbot features for AI-based resume screening & analytics 基于人工智能的简历筛选和分析的聊天机器人功能

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103775

Kar Weng Chong, Kok Why Ng, Yong Hong Fu

AI technologies are changing the field of manpower recruitment since they make it much more efficient, accurate, and scalable than conventional approaches. The project builds an AI recruitment system that is combined to have two main elements of an integrated AI recruitment system including a Resume Screening AI and a Job Recruitment Chatbot, which have the goal of improving the process in hiring as well as making the experience of the candidates better in the process.

The Resume Screening AI uses a mixed approach incorporating both classical document processing, and capabilities of advanced language models. The unstructured data of raw resumes are mined and normalized into standard forms to allow evaluation and ranking of the candidates in an organized and systematic manner according to the position’s requirements. The Job Recruitment Chatbot entails a programmed chat system of interactive communication during the job recruitment procedure comprising the component of FAQ, conversation-based direction, and voice-to-text dynamic to make the system more accessible to a diverse group of users.

•
Document Processing Pipeline: Parsed all-format resumes by the means of PyPDF2 and python-docx libraries and programmed the data in a structured manner, via Google Gemini 1.5 Flash API and engineered special prompts to validate the set of JSON-Schema.
•
Intelligent Screening System: Created automated candidate screening based on (large language model) inference process to compare resume text with job requirements, producing relevance scores and classified evaluations.
•
Interactive Chatbot Development: Developed natural language processing AI interface with chat capabilities and with speech-to-text and FAQ automation that could be used to answer candidate questions and optimize the recruitment process.

人工智能技术正在改变人力招聘领域，因为它比传统方法更高效、更准确、更可扩展。该项目构建了一个人工智能招聘系统，该系统结合了集成人工智能招聘系统的两个主要元素，包括简历筛选人工智能和工作招聘聊天机器人，其目标是改善招聘过程，并使候选人在此过程中获得更好的体验。简历筛选人工智能使用了一种混合的方法，结合了传统的文档处理和高级语言模型的功能。对原始简历中的非结构化数据进行挖掘，并将其规范化为标准表格，以便根据职位要求，有组织、系统地对候选人进行评估和排名。工作招聘聊天机器人需要一个编程聊天系统，在工作招聘过程中进行互动交流，包括常见问题解答、基于对话的指导和语音到文本的动态，使系统更容易被不同群体的用户访问。•文档处理管道：通过PyPDF2和python-docx库解析所有格式的简历，并通过谷歌Gemini 1.5 Flash API以结构化方式编程数据，并设计特殊提示来验证JSON-Schema集。•智能筛选系统：创建基于（大型语言模型）推理过程的自动候选人筛选，将简历文本与工作要求进行比较，产生相关性分数和分类评估。•交互式聊天机器人开发：开发具有聊天功能的自然语言处理AI界面，具有语音转文本和FAQ自动化功能，可用于回答候选人问题并优化招聘流程。

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引用次数: 0

A multivariate correlated poisson generalized inverse gaussian regression model for dependent count data: Estimation and testing procedures 依赖计数数据的多元相关泊松广义逆高斯回归模型：估计和检验程序

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103772

Yusrianti Hanike , Purhadi , Achmad Choiruddin

Regression modeling for multivariate count data often struggles with assumption of overdispersion and correlation among response variables. To address these issues, this study proposes a new model called Multivariate Correlated Poisson Generalized Inverse Gaussian Regression (MCPGIGR), which integrates random effects through common shock variables and allows for flexible mean structures via a log-link function. This research develops a Maximum Likelihood Estimation (MLE) and Maximum Likelihood Ratio Tests (MLRT) to evaluate both simultaneous and partial significance of predictors. We conduct simulation studies to assess the consistency and performance of the proposed estimators. Furthermore, in an application to maternal and neonatal mortality across 38 districts/cities in East Java (Indonesia), MCPGIGR substantially improves model fit relative to a Multivariate Poisson Regression (MPR) baseline (AICc decreases from 2378.63 to 1924.60 for

γ = - 1 / 2

). The proposed framework provides a practical and flexible tool for analyzing correlated, overdispersed multivariate counts in public health and related domains. The highlights of this research are:

• The MCPGIGR model introduces a correlated multivariate count regression framework with exposure adjustment.

• It provides robust parameter estimation and hypothesis testing via MLE and MLRT.

• MCPGIGR demonstrates improved model fit and practical interpretability in public health applications.

多变量计数数据的回归建模经常与响应变量之间的过度分散和相关性假设作斗争。为了解决这些问题，本研究提出了一种称为多元相关泊松广义逆高斯回归（MCPGIGR）的新模型，该模型通过常见的冲击变量集成随机效应，并通过对数链接函数允许灵活的平均值结构。本研究发展了最大似然估计（MLE）和最大似然比检验（MLRT）来评估预测因子的同时显著性和部分显著性。我们进行模拟研究，以评估所提出的估计器的一致性和性能。此外，在东爪哇（印度尼西亚）38个地区/城市的孕产妇和新生儿死亡率的应用中，MCPGIGR相对于多变量泊松回归（MPR）基线显著改善了模型拟合（当γ= - 1/2时，AICc从2378.63降至1924.60）。提出的框架为分析公共卫生和相关领域中相关的、过度分散的多变量计数提供了一个实用和灵活的工具。本研究的重点是：•MCPGIGR模型引入了一个与曝光调整相关的多变量计数回归框架。•通过MLE和MLRT提供鲁棒参数估计和假设检验。•MCPGIGR在公共卫生应用中展示了改进的模型拟合性和实际可解释性。

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引用次数: 0

Actor-critic guided CDBN with GAN augmentation for robust facial emotion recognition 演员评论家引导的GAN增强CDBN鲁棒性面部情绪识别

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103774

Akshay S , Jnana Sai S R , Sinchana B R , Kannan M , Adwitiya Mukhopadhyay

Facial emotion recognition (FER) remains challenging under limited data, noise, and occlusion. This study introduces an Actor–Critic Convolutional Deep Belief Network (ACCDBN) that unifies Generative Adversarial Network (GAN)–based augmentation, deep probabilistic feature learning, and reinforcement-driven optimization. Conditional GANs expand minority emotion classes, enhancing data diversity, while the CDBN extracts hierarchical texture features through convolutional and restricted Boltzmann layers. An Actor–Critic module dynamically refines representations by rewarding accurate emotion classification and penalizing uncertain predictions. Trained and validated on the CK+ dataset with five-fold cross-validation, the proposed model achieves higher accuracy and stability than CNN, LSTM, and ResNet-50 baselines, maintaining strong performance under noise and occlusion. The approach demonstrates how reinforcement-guided generative learning can improve both accuracy and robustness in FER tasks.

1. To implement this, the research utilised the publicly available Cohn-Kanade+ dataset, consisting of eight classes with samples of 920 grey-scale images.

2. An improved ACCDBN model outperformed with 90.4% accuracy and 0.69 MCC (Mathew’s Correlation Coefficient) in 5-fold cross-validation using the cGAN-generated dataset and 87% on the CK+ dataset

3. The main objective is to present an advanced facial emotion recognition (FER) system that combines a Convolution Deep Belief Network (CDBN) with a model-free reinforcement learning technique, namely the actor-critic approach.

面部情绪识别（FER）在有限的数据、噪声和遮挡下仍然具有挑战性。本研究引入了一种Actor-Critic卷积深度信念网络（ACCDBN），该网络将基于生成对抗网络（GAN）的增强、深度概率特征学习和强化驱动优化相结合。条件gan扩展了少数情感类，增强了数据多样性，而CDBN通过卷积和受限玻尔兹曼层提取分层纹理特征。演员-评论家模块通过奖励准确的情绪分类和惩罚不确定的预测来动态地改进表征。在CK+数据集上进行5倍交叉验证，该模型比CNN、LSTM和ResNet-50基线具有更高的准确率和稳定性，在噪声和遮挡下保持了较强的性能。该方法展示了强化引导的生成学习如何提高FER任务的准确性和鲁棒性。为了实现这一目标，该研究利用了公开可用的Cohn-Kanade+数据集，该数据集由8个类别和920个灰度图像样本组成。改进的ACCDBN模型在使用cgan生成的数据集的5倍交叉验证中表现出90.4%的准确率和0.69的MCC（马修相关系数），在CK+数据集上表现为87%。主要目标是提出一种先进的面部情绪识别（FER）系统，该系统将卷积深度信念网络（CDBN）与无模型强化学习技术（即演员批评方法）相结合。

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引用次数: 0

An in depth investigation on device-to-device communication in heterogeneous networks: opportunities and challenges 异构网络中设备对设备通信的深入研究：机遇与挑战

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-17 DOI: 10.1016/j.mex.2025.103777

Amjad Ali , Aqsa Zehra , Muhammad Nafees , Muhammad Awais Amin

Accurate data from appropriate areas and in rural areas collect reliably and quickly with the help of UAVs. UAVs are used to gather any type of information either from land, sea, or air. The communication between UAV to UAV and UAV to the ground requires some linkage or a path for transmission. Device-to-device transmission is used to offload traffic in a congestion network. However, a D2D transmission is based on multiple networks. All these networks are collectively discussed in this paper included; cellular, 5G, military, ad-hoc, IoT, Social, and public safety scenarios. A variety of advantages in terms of reliability, latency, spectrum efficiency, energy efficiency, interference mitigation, high altitude, power optimization, autonomous relaying, increased throughput, high data rates, privacy, security, mode selection, FANET, MANET, VANET, coverage extension, and covert communication is given in this paper. Eventually, in this paper, we provide all the opportunities and challenges in D2D transmission over contrasting network domains.

在无人机的帮助下，从适当地区和农村地区可靠、快速地收集准确的数据。无人机用于从陆地、海上或空中收集任何类型的信息。无人机与无人机之间以及无人机与地面之间的通信需要一定的联动或传输路径。设备到设备传输用于在拥塞网络中分流流量。然而，D2D传输是基于多个网络的。本文对这些网络进行了综合讨论，包括：蜂窝、5G、军事、ad-hoc、物联网、社会和公共安全场景。本文给出了在可靠性、延迟、频谱效率、能源效率、干扰缓解、高海拔、功率优化、自主中继、增加吞吐量、高数据速率、隐私、安全、模式选择、FANET、MANET、VANET、覆盖扩展和隐蔽通信方面的各种优势。最后，在本文中，我们提供了在不同网络域上D2D传输的所有机遇和挑战。

引用次数: 0

Knowledge, support, and networking for Phelan-McDermid syndrome: a study protocol 费伦-麦克德米德综合征的知识、支持和网络：一项研究方案

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-16 DOI: 10.1016/j.mex.2025.103771

Luca Colnaghi , Giulia Villa , Ilaria Marcomini , Andrea Poliani , Maya Fedeli , Claudio Losi , Debora Rosa , Duilio Fiorenzo Manara

Background

Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by deletions in chromosome 22q13.3 or pathogenic variants in the SHANK3 gene. Individuals present with intellectual disability, autism-spectrum traits, seizures, gastrointestinal and motor issues, and sleep disturbances, requiring lifelong multidisciplinary care. In Italy, PMS care is fragmented and unevenly distributed, with families often providing intensive home-based support at high personal, financial, and social costs.

Methods

This national participatory Citizen-Science study, conducted with the Italian Phelan-McDermid Syndrome Association (AISPHEM), will engage informal caregivers of individuals with genetically confirmed PMS across Italy. A qualitative phase using semi-structured online interviews will explore caregiving experiences, unmet needs, barriers to care, coping strategies, and social isolation. Insights will guide the creation of the first Italian PMS registry, capturing longitudinal clinical, socio-demographic, and caregiver-related data.

Expected Results & Conclusions

The project will generate novel evidence on caregiver needs, develop the first national PMS registry, and produce a service map to support equitable, coordinated PMS care and a stronger national caregiver network in Italy.

phan - mcdermid综合征（PMS）是一种罕见的神经发育障碍，由染色体22q13.3缺失或SHANK3基因的致病性变异引起。患有智力残疾、自闭症谱系特征、癫痫、胃肠道和运动问题以及睡眠障碍的个体，需要终身多学科护理。在意大利，经前症候群护理支离破碎，分布不均，家庭往往以高昂的个人、经济和社会成本提供密集的家庭支持。方法：这项与意大利费伦-麦克德米德综合征协会（AISPHEM）合作进行的全国性参与性公民科学研究将招募意大利各地经遗传证实的经前症候群患者的非正式护理人员。使用半结构化在线访谈的定性阶段将探讨护理经验、未满足的需求、护理障碍、应对策略和社会孤立。洞察将指导创建第一个意大利经前症候群注册，捕获纵向临床，社会人口统计和护理相关数据。预期结果和结论该项目将提供有关护理人员需求的新证据，建立首个全国经前综合症登记处，并绘制服务地图，以支持意大利公平、协调的经前综合症护理和更强大的全国护理人员网络。

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引用次数: 0

Feasibility study of transcranial direct current stimulation (tDCS) efficacy in patients with neurogenic dysphagia: Study Protocol 经颅直流电刺激（tDCS）治疗神经性吞咽困难的可行性研究：研究方案

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-16 DOI: 10.1016/j.mex.2025.103768

Carolina Fiorin Anhoque , Bárbara Aguiar do Sacramento da Silva , Luana Ximenes Comarela , Amanda Mezabarba Raggazzi , Walter Gomes da Silva Filho , Fernanda Moura Vargas Dias , Fernando Zanela da Silva Arêas

Non-invasive brain stimulation (NIBS) techniques have shown increasing scientific relevance due to their potential positive effects across several health domains, including rehabilitation and clinical treatment. Among these techniques, transcranial direct current stimulation (tDCS) has emerged as a promising approach for the treatment of various neurological symptoms and conditions. Specifically, tDCS has shown potential benefits in speech therapy, hearing, voice modulation, and particularly in the treatment of neurogenic dysphagia. This feasibility study aims to assess the implementation of tDCS in patients with neurogenic dysphagia and to determine whether a future randomized controlled trial (RCT) is justified. The study will be conducted at the Neurorehabilitation and Neuromodulation Laboratory, located at the Interprofessional Health Clinic School (CEIS) of the Federal University of Espírito Santo, Brazil. Twelve patients will be recruited by convenience sampling and will undergo eight 30-minute tDCS sessions (twice a week over four weeks). Swallowing function will be assessed using the Dysphagia Risk Evaluation Protocol (PARD), the Eating Assessment Tool (EAT-10), and the Quality of Life in Swallowing Disorders Protocol (SWAL-QOL) before and after the intervention. This protocol offers preliminary insights into the clinical feasibility of tDCS for dysphagia and contributes to the development of future clinical trials in this field.

由于非侵入性脑刺激技术在包括康复和临床治疗在内的多个健康领域具有潜在的积极作用，其科学相关性日益增强。在这些技术中，经颅直流电刺激（tDCS）已成为治疗各种神经症状和疾病的一种有前途的方法。具体来说，tDCS在语言治疗、听力、语音调节，特别是神经源性吞咽困难的治疗中显示出潜在的益处。这项可行性研究旨在评估tDCS在神经源性吞咽困难患者中的实施情况，并确定未来的随机对照试验（RCT）是否合理。该研究将在位于巴西圣Espírito联邦大学跨专业健康诊所学院（CEIS）的神经康复和神经调节实验室进行。12名患者将接受8次30分钟的tDCS治疗（每周两次，为期四周）。在干预前后，将使用吞咽困难风险评估方案（PARD）、进食评估工具（EAT-10）和吞咽障碍生活质量方案（sval - qol）评估吞咽功能。该方案为tDCS治疗吞咽困难的临床可行性提供了初步的见解，并有助于该领域未来临床试验的发展。

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引用次数: 0

The progressive power program: An exercise intervention to improve health outcomes in adults with overweight and obesity 渐进式力量计划：一项改善超重和肥胖成人健康结果的运动干预

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-14 DOI: 10.1016/j.mex.2025.103766

V. Bilro , A. Duarte Martins , T. Sobral , J.A. Parraca , M.I. Varela-Silva , N. Batalha

The present study outlines the progressive power program (PPP), a structured exercise protocol developed for adults with overweight and obesity. The PPP integrates high-intensity interval training (HIIT), high-intensity functional training (HIFT), and moderate-intensity continuous training (MICT) within a progressive 12-week framework. Participants complete three 50-minute sessions per week, delivered either face-to-face or remotely, using bodyweight exercises and self-regulated pacing guided by heart rate monitoring and perceived exertion.

The protocol is described in sufficient detail to ensure reproducibility and adaptability across diverse clinical, community, and online settings. Emphasising accessibility, safety, and participant autonomy, the PPP aims to reduce barriers related to equipment, supervision, and location. Standardised procedures for anthropometric, functional, and behavioural assessments reinforce methodological rigour.

By combining evidence-based exercise modalities with scalable delivery strategies, the PPP offers a resource-efficient and adaptable approach to promoting physical activity and supporting weight management. Its methodological design aligns with public health priorities by addressing physical inactivity, reducing sedentary behaviour, and improving health outcomes in at-risk populations.

•
Designed a progressive 12-week exercise protocol incorporating HIIT, HIFT, and MICT modalities.
•
Assessed feasibility and reach by comparing face-to-face and remote delivery formats.
•
Evaluated physical, functional, and behavioural outcomes before and after the intervention.

目前的研究概述了渐进式力量计划（PPP），这是一种为超重和肥胖的成年人开发的结构化运动方案。PPP将高强度间歇训练（HIIT）、高强度功能性训练（HIFT）和中强度连续训练（MICT）整合在一个渐进式的12周框架内。参与者每周完成三次50分钟的训练，面对面或远程授课，通过体重练习和心率监测和感知运动引导的自我调节步调。该方案有足够详细的描述，以确保在不同的临床、社区和在线环境下的可重复性和适应性。PPP强调可达性、安全性和参与者自主性，旨在减少与设备、监督和位置相关的障碍。人体测量学、功能和行为评估的标准化程序加强了方法的严谨性。通过将基于证据的锻炼模式与可扩展的交付策略相结合，PPP提供了一种资源高效且适应性强的方法来促进身体活动和支持体重管理。其方法设计符合公共卫生重点，解决缺乏身体活动问题，减少久坐行为，改善高危人群的健康状况。•设计了一个渐进式的12周运动方案，包括HIIT、HIFT和MICT模式。•通过比较面对面和远程交付形式评估可行性和覆盖范围。•评估干预前后的身体、功能和行为结果。

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引用次数: 0

Innovative parallel grasshopper optimization algorithm for reliability optimization 创新的并行蚱蜢优化算法用于可靠性优化

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-14 DOI: 10.1016/j.mex.2025.103759

Dipti Singh, Neha Chand

This study introduces a novel Parallel Grasshopper Optimization Algorithm (p-GOA), specifically designed to address reliability optimization problems. Although several hybrid algorithms exist in this field, the proposed p-GOA distinctly differs through its parallel cooperative strategy. Unlike sequential methods that apply techniques one after another, p-GOA simultaneously divides the population into two groups operating in parallel: one group employs a migration strategy (SOMA) for broad global exploration of the search space, while the other utilizes a mutation operator (NUMO) for focused local refinement of solutions. This dual-strategy parallel operation creates achieving a stronger balance between global exploration and local refinement, while a smart penalty-free method naturally steers the search toward workable solutions. When tested on four well-known reliability problems, the results demonstrate that our method consistently finds more reliable systems and converges faster than existing approaches, demonstrating its effectiveness in handling real-world engineering constraints.

● This study introduces a Parallel Grasshopper Optimization Algorithm (p-GOA) that integrates GOA, SOMA, and a Non-Uniform Mutation Operator (NUMO). It employs mutation, migration, and a parallel approach to efficiently explore both feasible and near-feasible regions without relying on penalty functions.

● The p-GOA dividing the population into two parallel groups—one updated using SOMA-based migration and the other using NUMO-based mutation. This dual-strategy, simultaneous processing not only accelerates convergence but also strengthens the balance between global search and local optimization.

● Specifically targets reliability optimization problems, particularly redundancy allocation issues where components must meet specific reliability and resource consumption (cost, weight, volume) constraints.

本文介绍了一种新的并行蚱蜢优化算法（p-GOA），专门用于解决可靠性优化问题。虽然该领域存在多种混合算法，但所提出的p-GOA通过其并行合作策略具有明显的不同之处。与顺序方法不同，p-GOA同时将种群分为并行操作的两组：一组使用迁移策略（SOMA）对搜索空间进行广泛的全局探索，而另一组使用突变算子（NUMO）对解决方案进行集中的局部优化。这种双策略并行操作在全局勘探和局部优化之间实现了更强的平衡，而一种智能的无惩罚方法自然地引导了对可行解决方案的搜索。当对四个众所周知的可靠性问题进行测试时，结果表明，我们的方法始终能够找到更可靠的系统，并且比现有方法收敛得更快，证明了它在处理实际工程约束方面的有效性。●本研究引入了一种并行蚱蜢优化算法（p-GOA），该算法集成了GOA、SOMA和非均匀变异算子（NUMO）。它采用突变、迁移和并行方法来有效地探索可行和近可行区域，而不依赖于惩罚函数。●p-GOA将种群划分为两个平行组，一个使用基于somo的迁移更新，另一个使用基于numo的突变更新。这种双重策略的同时处理不仅加快了收敛速度，而且加强了全局搜索和局部优化之间的平衡。●专门针对可靠性优化问题，特别是冗余分配问题，其中组件必须满足特定的可靠性和资源消耗（成本，重量，体积）的限制。

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引用次数: 0

The Rapid Initial Community Builder (RICB) V1.1 for LANDIS-II 用于LANDIS-II的快速初始社区构建器（RICB） V1.1

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-12-13 DOI: 10.1016/j.mex.2025.103765

Steven A Flanagan , Zachary J. Robbins , Mac A. Callaham Jr , J. Kevin Hiers , Brian R. Miranda , Joseph J. O’Brien , Robert M. Scheller , E. Louise Loudermilk

Forest succession, or ecosystem process models, can aid in many land management decisions as they inherently incorporate ecological processes to predict how disturbances impact the resilience of the forest and its potential future structure. The landscape class of ecosystem process models are often ideal for management goals as they simulate individual tree species interactions through time at the landscape scale and offer a robust assortment of disturbance extensions. However, broad implementation and adoption are often limited as users must rebuild the landscape models for every new site of interest. Creation of the initial forest communities that all model simulations depend on, can be particularly time consuming. To address this issue, we created the Rapid Initial Community Builder (RICB) that:

•
Starts with high resolution CONtinental United States (CONUS) forest coverage and type data.
•
Makes modifications to the data for use in the landscape class ecosystem process model LANDIS-II.
•
Packages everything in an executable file that eliminates coding language or version barriers.

Thus, greatly reducing the initialization time often associated with the intensive data assimilation techniques commonly used to generate initial communities.

森林演替或生态系统过程模型可以帮助许多土地管理决策，因为它们本质上包含生态过程，以预测干扰如何影响森林的恢复能力及其潜在的未来结构。景观类生态系统过程模型通常是理想的管理目标，因为它们模拟了景观尺度上单个树种随时间的相互作用，并提供了各种各样的干扰扩展。然而，广泛的实施和采用往往受到限制，因为用户必须为每个感兴趣的新站点重新构建景观模型。所有模型模拟所依赖的原始森林群落的创建可能特别耗时。为了解决这个问题，我们创建了快速初始社区构建器（RICB），它：•从高分辨率美国大陆（CONUS）森林覆盖率和类型数据开始。•对用于景观级生态系统过程模型LANDIS-II的数据进行修改。•将所有内容打包到可执行文件中，从而消除编码语言或版本障碍。因此，大大减少初始化时间通常与用于生成初始社区的密集数据同化技术相关。

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引用次数: 0