Results in Control and Optimization最新文献_第7页

High-temperature performance evaluation of sustainable date palm fiber concrete with activated carbon: An MCDM and Weibull analysis approach 可持续椰枣纤维混凝土的高温性能评价：MCDM和Weibull分析方法

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-24 DOI: 10.1016/j.rico.2025.100602

Musa Adamu , Yasser E. Ibrahim , Ashwin Raut

This study explores the optimization and performance evaluation of date palm fiber (DPF) reinforced concrete containing powdered activated carbon (PAC) subjected to high temperatures. Multi-criteria decision-making (MCDM) and Weibull distribution analyses were used to analyse the concrete’s properties. Weight loss and residual compressive strength at 300 °C, 600 °C, and 900 °C were measured. The concrete mixes were designed with varying DPF (0 %, 1 %, 2 %, and 3 %) and PAC (0 %, 1 %, 2 %, and 3 % by cement weight). Performance evaluation included compressive strength, workability, water absorption, and mass loss were measured. The EDAS method identified Mix M1D1P (1 % DPF, 1 % PAC) as the best-performing mix composition. The optimal mix demonstrated high compressive strength (54.13 MPa), residual strength at 800 °C (25.17 MPa), and low mass loss (9.84 %), making it suitable for high-temperature applications. The MCDM results revealed that mixes with moderate PAC content (1 %) outperformed those with excessive DPF and PAC due to reduced porosity and enhanced strength retention. Statistical reliability was verified through Weibull distribution, with high degree of correlation (R² = 0.981) for the residual strength at 600 °C. The findings underscore the potential of DPF as a sustainable fiber that enhances thermal stability and mechanical performance while supporting eco-friendly construction practices. This study advances smart concrete technology by providing a systematic framework for material selection and optimization, paving the way for durable and sustainable high-performance concrete applications. The findings in this study also explores its practical applications in structural and thermal barrier systems, offering functional foresight for civil engineering and sustainable construction practices.

研究了高温条件下含有粉末活性炭（PAC）的枣椰树纤维（DPF）增强混凝土的优化及性能评价。采用多准则决策（MCDM）和威布尔分布分析对混凝土的性能进行了分析。测量了300°C、600°C和900°C时的失重和残余抗压强度。设计了不同DPF（0%、1%、2%和3%）和PAC（0%、1%、2%和3%水泥重量）的混凝土混合料。性能评价包括抗压强度、和易性、吸水率和质量损失。EDAS法鉴定Mix M1D1P （1% DPF, 1% PAC）为性能最佳的混合成分。优化后的混合料具有较高的抗压强度（54.13 MPa）、800℃时的残余强度（25.17 MPa）和较低的质量损失（9.84%），适合高温应用。MCDM结果显示，适度PAC含量（1%）的混合物优于那些含有过多DPF和PAC的混合物，因为它们降低了孔隙率，增强了强度保持。通过Weibull分布验证了统计信度，600°C时的残余强度具有高度相关（R²= 0.981）。研究结果强调了DPF作为一种可持续纤维的潜力，它可以提高热稳定性和机械性能，同时支持环保建筑实践。这项研究通过提供材料选择和优化的系统框架来推进智能混凝土技术，为耐用和可持续的高性能混凝土应用铺平了道路。本研究还探讨了其在结构和热障系统中的实际应用，为土木工程和可持续建筑实践提供了功能预见。

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引用次数: 0

An accurate MINLP formulation for selecting conductors in three-phase asymmetric networks: A computational implementation in the Julia software 用于选择三相非对称网络导体的精确MINLP公式：Julia软件中的计算实现

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-18 DOI: 10.1016/j.rico.2025.100596

Brandon Cortés-Caicedo , Oscar Danilo Montoya

The increasing global demand for electricity, driven by population growth and technological advancements, has necessitated the expansion of power generation, transmission, and distribution systems. Consequently, grid operators are focused on redesigning networks to enhance energy supply efficiency, with special emphasis on selecting conductors for unbalanced three-phase systems to minimize costs. This paper introduces a computational implementation of a mathematical model in the complex variable domain, addressing the conductor selection problem. The primary aim is to offer a comprehensive tutorial that guides readers through the formulation of three-phase power flow in distribution networks and the computational implementation of the conductor selection problem in both single-hour and multi-period scenarios using the Julia programming language. The mathematical models were implemented and solved using the JuMP optimization environment alongside the Bonmin solver. Validation was performed on academic test systems with different network configurations to assess the impact of conductor selection under balanced and unbalanced operating conditions. The results confirm that the proposed exact optimization methodology outperforms metaheuristic algorithms commonly found in the literature, achieving lower total costs and improved computational efficiency.

在人口增长和技术进步的推动下，全球对电力的需求不断增加，这使得发电、输电和配电系统的扩张成为必要。因此，电网运营商专注于重新设计网络以提高能源供应效率，特别强调为不平衡三相系统选择导体以最小化成本。本文介绍了一种复变域数学模型的计算实现，用于解决导体选择问题。主要目的是提供一个全面的教程，指导读者通过配电网三相潮流的制定和导体选择问题的计算实现在单小时和多周期使用Julia编程语言。利用JuMP优化环境和Bonmin求解器对数学模型进行了实现和求解。在不同网络配置的学术测试系统上进行了验证，以评估平衡和不平衡运行条件下导体选择的影响。结果证实，所提出的精确优化方法优于文献中常见的元启发式算法，实现了更低的总成本和更高的计算效率。

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引用次数: 0

Robust switching control design for matrix second order systems: Application to robotic cane platform 矩阵二阶系统的鲁棒切换控制设计：在机器人手杖平台上的应用

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-16 DOI: 10.1016/j.rico.2025.100597

Ivan Yupanqui, Macarena Vilca, Renzo Mendoza, Alain Chupa, Diego Arce, Jesús Alan Calderón, Bryan Bastidas, Miguel Badillo

This paper addresses the switching control design problem for a class of nonlinear matrix second-order systems that characterize the dynamics of robotic and multibody systems. These systems are inherently characterized by significant nonlinearities and are subject to uncertainties, parameter variations, and external disturbances, which pose substantial challenges for control design. Analytical solutions for such control problems are often intractable, necessitating the use of numerical optimization techniques. This study presents sufficient conditions, derived from Lyapunov stability theory, for synthesizing switching feedback controllers that ensure system stability with guaranteed

H_{\infty}

performance. The approach leverages the Linear Parameter Varying (LPV) representation of the nonlinear dynamics through Takagi–Sugeno (T-S) modeling methodology. The proposed stability conditions are formulated as Linear Matrix Inequalities (LMIs), enabling efficient computation using standard convex optimization software. Comprehensive simulation studies demonstrate that the proposed switching control strategy, applicable to a broad class of nonlinear matrix second-order systems, significantly outperforms conventional weighted gain-scheduling approaches in terms of feasibility regions and

H_{\infty}

performance indices. Experimental validation on a robotic cane platform confirms the practical effectiveness of the proposed methodology, achieving nice dynamic performance and robust disturbance rejection capabilities.

研究了一类具有机器人和多体系统动力学特征的非线性矩阵二阶系统的切换控制设计问题。这些系统本质上具有显著的非线性特征，并受到不确定性、参数变化和外部干扰的影响，这对控制设计构成了重大挑战。这类控制问题的解析解通常是棘手的，需要使用数值优化技术。本文从Lyapunov稳定性理论出发，给出了合成切换反馈控制器的充分条件，保证了系统稳定性和H∞性能。该方法通过Takagi-Sugeno （T-S）建模方法利用非线性动力学的线性参数变化（LPV）表示。提出的稳定性条件被表述为线性矩阵不等式（lmi），使得使用标准凸优化软件进行高效计算。综合仿真研究表明，所提出的切换控制策略适用于广泛的非线性矩阵二阶系统，在可行性区域和H∞性能指标方面明显优于传统的加权增益调度方法。在机器人手杖平台上的实验验证验证了该方法的实用性，取得了良好的动态性能和鲁棒抗干扰能力。

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引用次数: 0

Customer satisfaction in peer-to-peer lending platforms: A text mining and sentiment analysis approach p2p借贷平台的客户满意度：文本挖掘和情感分析方法

IF 3.2 Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-11 DOI: 10.1016/j.rico.2025.100598

Sarasanabelli Prasanna Kumari , Ali B.M. Ali , Madhusmita Mohanty , Bibhuti Bhusan Dash , Muhammad Rafiq , Sachi Nandan Mohanty , Iskandar Shernazarov , Nashwan Adnan Othman , Nadia Batool

This study examines customer experience and satisfaction with peer-to-peer (P2P) lending platforms in India by analyzing user-generated online reviews. Despite the rapid expansion of India’s P2P lending market, few studies have analyzed consumer feedback to evaluate platform performance. To address this gap, 11,000 customer reviews were scraped from nine leading Indian P2P platforms. Text mining and sentiment analysis techniques, specifically Frequency Analysis, Convergence of Iterated Correlations (CONCOR) cluster analysis, and Exploratory Factor Analysis (EFA) were employed to extract latent satisfaction drivers. The analysis identified key experience drivers such as customer support, loan processing speed, usability, and fraud-related concerns. EFA distilled these into three underlying satisfaction factors: Positive Experiences and Core Functionalities, Customer Support and Overall Experience, and Efficiency in Application Interaction. The study reveals India-specific insights into digital lending behavior and provides targeted recommendations for improving platform trust, responsiveness, and financial accessibility, essential to user retention and financial inclusion in India’s evolving FinTech ecosystem.

本研究通过分析用户生成的在线评论，考察了印度P2P借贷平台的客户体验和满意度。尽管印度P2P借贷市场迅速扩张，但很少有研究分析消费者反馈来评估平台的性能。为了弥补这一差距，我们从印度9家领先的P2P平台上收集了1.1万条客户评论。文本挖掘和情感分析技术，特别是频率分析，迭代相关收敛（CONCOR）聚类分析和探索性因子分析（EFA）被用于提取潜在的满意度驱动因素。分析确定了关键的体验驱动因素，如客户支持、贷款处理速度、可用性和与欺诈相关的问题。EFA将这些因素提炼成三个潜在的满意度因素：积极的体验和核心功能，客户支持和整体体验，以及应用程序交互的效率。该研究揭示了印度对数字借贷行为的具体见解，并为提高平台信任度、响应能力和金融可及性提供了有针对性的建议，这对印度不断发展的金融科技生态系统中的用户留存和金融包容性至关重要。

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引用次数: 0

TeaNet8: A real time Android application-based Tea Leaf Disease detection using fine-tuned transfer learning and Gradient-Weighted Class Activation Mapping visualization TeaNet8：基于Android应用程序的实时茶叶病害检测，使用微调迁移学习和梯度加权类激活映射可视化

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-09 DOI: 10.1016/j.rico.2025.100577

Ismotara Dipty , Md Assaduzzaman , Nafiz Fahad , Md. Jakir Hossen , Md. Farhatul Haider , Fiaj Rahman

Tea is widely regarded as one of the most popular beverages globally, and Bangladesh plays a significant role both as a producer and consumer of this renowned drink. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final product’s quantity and quality. To prevent and control tea leaf diseases, a reliable and precise diagnosis and identification system is needed. Tea leaf infections are discovered manually, which takes time and affects crop quality and production. Detecting tea leaf disease early can lead to decreased damage to overall tea production. Advanced deep learning methods are simplifying the identification and categorization of specific illnesses in tea plants. This current study introduces TeaNet8, a deep learning-based approach for identifying and classifying eight tea leaf disease classes using a fine-tuned ResNet50V2 model. Moreover, this study employs 2824 images of eight different types of leaf diseases. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), brightness adjustment, and unsharp masking were applied to enhance the dataset. Additionally, data augmentation techniques were used to increase its diversity. The proposed model identify the differnt type of tea leaf disease with 97% accuracy.Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization was employed to interpret and understand model predictions. The model demonstrated perfect accuracy for Algal Spot, Anthracnose, Gray Blight, and White Spot, with accuracy rates of 97.14% for Brown Blight, 94.59% for Healthy leaves, 94.12% for Red Spot, and 92.31% for Bird Eye Spot. Furthermore, the proposed model’s performance was compared against three pre-trained fine-tuning models. Various performance measurement indicators were used to evaluate the performance of the proposed model. The results showed that the proposed model is effective in categorizing diseases in tea leaves.Finally, An Android-based system was developed employing the most effective model to aid farmers for detecting tea leaf diseases.

茶被广泛认为是全球最受欢迎的饮料之一，孟加拉国作为这种著名饮料的生产国和消费国都发挥着重要作用。然而，影响作物质量和生产力的疾病会极大地阻碍茶叶的生产，影响最终产品的数量和质量。为了预防和控制茶叶病害，需要一个可靠、精确的诊断和鉴定系统。茶叶感染是人工发现的，这需要时间，也会影响作物的质量和产量。及早发现茶叶病害可以减少对茶叶总产量的损害。先进的深度学习方法正在简化对茶树特定疾病的识别和分类。本研究介绍了TeaNet8，这是一种基于深度学习的方法，用于使用微调的ResNet50V2模型识别和分类八种茶叶疾病类别。此外，本研究使用了8种不同类型叶片病害的2824张图像。采用对比度有限自适应直方图均衡化（CLAHE）、亮度调整和非锐利掩蔽等预处理技术增强数据集。此外，还使用了数据增强技术来增加其多样性。该模型识别不同类型的茶叶病害的准确率为97%。采用梯度加权类激活映射（Grad-CAM）可视化来解释和理解模型预测。该模型对褐枯病、炭疽病、灰枯病和白斑病的预测准确率为97.14%，对健康叶的预测准确率为94.59%，对红斑病的预测准确率为94.12%，对鸟眼斑病的预测准确率为92.31%。此外，将该模型的性能与三种预训练的微调模型进行了比较。使用各种性能测量指标来评估所提出模型的性能。结果表明，该模型对茶叶病害分类是有效的。最后，开发了一个基于android的系统，利用最有效的模型来帮助农民检测茶叶病害。

{"title":"TeaNet8: A real time Android application-based Tea Leaf Disease detection using fine-tuned transfer learning and Gradient-Weighted Class Activation Mapping visualization","authors":"Ismotara Dipty , Md Assaduzzaman , Nafiz Fahad , Md. Jakir Hossen , Md. Farhatul Haider , Fiaj Rahman","doi":"10.1016/j.rico.2025.100577","DOIUrl":"10.1016/j.rico.2025.100577","url":null,"abstract":"<div><div>Tea is widely regarded as one of the most popular beverages globally, and Bangladesh plays a significant role both as a producer and consumer of this renowned drink. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final product’s quantity and quality. To prevent and control tea leaf diseases, a reliable and precise diagnosis and identification system is needed. Tea leaf infections are discovered manually, which takes time and affects crop quality and production. Detecting tea leaf disease early can lead to decreased damage to overall tea production. Advanced deep learning methods are simplifying the identification and categorization of specific illnesses in tea plants. This current study introduces TeaNet8, a deep learning-based approach for identifying and classifying eight tea leaf disease classes using a fine-tuned ResNet50V2 model. Moreover, this study employs 2824 images of eight different types of leaf diseases. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), brightness adjustment, and unsharp masking were applied to enhance the dataset. Additionally, data augmentation techniques were used to increase its diversity. The proposed model identify the differnt type of tea leaf disease with 97% accuracy.Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization was employed to interpret and understand model predictions. The model demonstrated perfect accuracy for Algal Spot, Anthracnose, Gray Blight, and White Spot, with accuracy rates of 97.14% for Brown Blight, 94.59% for Healthy leaves, 94.12% for Red Spot, and 92.31% for Bird Eye Spot. Furthermore, the proposed model’s performance was compared against three pre-trained fine-tuning models. Various performance measurement indicators were used to evaluate the performance of the proposed model. The results showed that the proposed model is effective in categorizing diseases in tea leaves.Finally, An Android-based system was developed employing the most effective model to aid farmers for detecting tea leaf diseases.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"20 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning approach for automated ‘Kent’ mango maturity grading in compliance with Peruvian standards 深度学习方法自动“肯特”芒果成熟度分级符合秘鲁标准

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-07-03 DOI: 10.1016/j.rico.2025.100589

Orlando Salazar-Campos , Javier Moran Ruiz , José Luis Peralta , Mirian Rubio Cieza , Breysi Salazar Medina , Johonathan Salazar-Campos

Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced automated fruit classification based on image analysis. However, accurate classification of Mangifera indica L. remains challenging due to high variability in external appearance and the subjectivity of visual maturity assessment. Misclassification contributes to post-harvest losses, reduced market value, and inconsistencies in quality control. This study develops a CNN-based model for classifying 'Kent' mangoes according to the Peruvian Technical Standard (NTP) 011.025:2023. A dataset of 603 labelled images was used to optimise the CNN architecture, systematically evaluating convolutional and pooling layers, image resolution, and training cycles. The optimised model, trained on 32× 32 pixel images, achieved 96.04 % classification accuracy, 90.91 % recall, and an F1-score of 93.57 %. To validate model robustness, 5-fold cross-validation demonstrated minimal accuracy variation (±0.5 %), while external evaluation achieved 95.8 % accuracy, confirming its real-world applicability. The lightweight single-layer CNN ensures scalable, low-cost implementation for automated sorting systems, reducing computational demands while enhancing classification efficiency. These findings establish deep learning as a viable and cost-effective solution for post-harvest fruit classification, ensuring greater consistency in quality control and supporting sustainable agricultural practices.

深度学习，特别是卷积神经网络（cnn），极大地推进了基于图像分析的自动水果分类。然而，由于芒果外观的高度变异性和视觉成熟度评价的主观性，对芒果的准确分类仍然具有挑战性。错误分类导致收获后损失、市场价值降低和质量控制不一致。本研究开发了一个基于cnn的模型，根据秘鲁技术标准（NTP） 011.025:2023对“肯特”芒果进行分类。使用603个标记图像的数据集来优化CNN架构，系统地评估卷积和池化层、图像分辨率和训练周期。优化后的模型在32× 32像素图像上训练，分类准确率为96.04%，召回率为90.91%，f1得分为93.57%。为了验证模型的稳健性，5倍交叉验证显示最小的准确度变化（±0.5%），而外部评估达到95.8%的准确度，证实了其在现实世界中的适用性。轻量级的单层CNN确保了自动分拣系统的可扩展、低成本实现，减少了计算需求，同时提高了分类效率。这些研究结果表明，深度学习是收获后水果分类的可行且具有成本效益的解决方案，可确保质量控制的一致性，并支持可持续农业实践。

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引用次数: 0

Effectiveness of bed nets and media awareness in dengue control: A fuzzy analysis 蚊帐和媒体意识在登革热控制中的有效性：一个模糊分析

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-26 DOI: 10.1016/j.rico.2025.100593

Shobha Islam, Md. Shahidul Islam, Md. Kamrujjaman

The incorporation of fuzzy analysis in the mathematical modeling of disease outbreaks has brought a paradigm shift in epidemiological research, offering a sophisticated approach to understanding and addressing the complexities inherent in disease dynamics. Unlike traditional mathematical models, which often rely on deterministic assumptions and precise parameter values, fuzzy analysis provides a flexible framework capable of accommodating uncertainty and imprecision within epidemiological systems. This paper presents a novel SVEIR-SEI compartmental model for dengue disease where five key parameters such as transmission rate, mortality rate, recovery rate, biting rate, and vaccination rate are treated as fuzzy numbers. Among these, transmission rate, mortality rate, and recovery rate are defined as a function of virus load whereas biting rate and vaccinations rate are defined as a function of number of bed net users and media awareness regarding vaccination, respectively. Crisp reproduction number is determined using next generation matrix method and hence fuzzy reproduction number is derived as a triangular fuzzy number (TFN). Numerical results show that biting rate and vaccination rate are the two most sensitive parameters to crisp reproduction number. We also examine the impact of using bed nets and media awareness regarding vaccination on the model system under fuzzy environment. It is found that using bed nets is a more effective strategy for dengue control than media coverage regarding vaccination.

将模糊分析纳入疾病暴发的数学模型，带来了流行病学研究的范式转变，为理解和处理疾病动力学固有的复杂性提供了一种复杂的方法。传统数学模型往往依赖于确定性假设和精确的参数值，而模糊分析不同，它提供了一个灵活的框架，能够适应流行病学系统中的不确定性和不精确性。本文提出了一种新的登革热SVEIR-SEI区室模型，该模型将传播率、死亡率、康复率、咬伤率和疫苗接种率等5个关键参数作为模糊数处理。其中，传播率、死亡率和恢复率被定义为病毒载量的函数，而叮咬率和疫苗接种率分别被定义为蚊帐使用者数量和媒体对疫苗接种意识的函数。采用下一代矩阵法确定脆度再现数，从而导出模糊再现数为三角模糊数（TFN）。数值结果表明，咬伤率和疫苗接种率是影响脆化繁殖数最敏感的两个参数。我们还研究了在模糊环境下使用蚊帐和媒体对疫苗接种的认识对模型系统的影响。研究发现，使用蚊帐是一种比媒体报道疫苗接种更有效的登革热控制策略。

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引用次数: 0

Mathematical modeling of refugee population dynamics and its impact on deforestation in Tanzania: An ODE-based and neural network-enhanced approach 坦桑尼亚难民人口动态及其对森林砍伐影响的数学建模：基于ode和神经网络增强的方法

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-26 DOI: 10.1016/j.rico.2025.100591

Joseph Kajuli, Maranya Mayengo, Ibrahim Fanuel

Understanding the interplay between refugee population dynamics and environmental factors is crucial for sustainable policy planning and public health preparedness. This study integrates an ordinary differential equation (ODE)-based model with a Neural Network-Enhanced Approach to estimate key parameters governing these interactions. A system of differential equations models refugee settlement, land-use changes, and deforestation, while Physics-Informed Neural Networks (PINNs) refine parameter estimates by minimizing discrepancies between observed and predicted states. Results show that combining traditional ODE modeling with neural networks improves predictive accuracy, capturing nonlinear interactions more effectively than regression-based methods. Specifically, the study examines bifurcation behavior concerning the refugee influx rate (

μ

), deforestation rate (

β

), and reforestation effort coefficient (

γ

). The analysis reveals that all three distributions are unimodal, peaking around 0.10 for

α

, 0.12 for

β

, and 0.08 for

γ

, with positive skewness indicating longer tails towards higher values. These findings underscore the urgent need for policy interventions to curb deforestation while enhancing reforestation efforts. Importantly, environmental degradation and rapid population pressures identified in the model have direct implications for public health, including increased risk of waterborne and vector-borne diseases, reduced access to clean air and food sources, and long-term mental and physical health challenges for displaced populations. Overall, this study highlights key environmental impact drivers and their health consequences, emphasizing the necessity of integrated, cross-sectoral planning in refugee-hosting regions.

了解难民人口动态与环境因素之间的相互作用对于可持续政策规划和公共卫生准备至关重要。本研究将基于常微分方程（ODE）的模型与神经网络增强方法相结合，以估计控制这些相互作用的关键参数。微分方程系统为难民安置、土地利用变化和森林砍伐建模，而物理信息神经网络（pinn）通过最小化观测状态和预测状态之间的差异来改进参数估计。结果表明，将传统的ODE建模与神经网络相结合可以提高预测精度，比基于回归的方法更有效地捕获非线性相互作用。具体而言，该研究考察了难民流入率（μ）、森林砍伐率（β）和再造林努力系数（γ）的分岔行为。分析表明，所有三个分布都是单峰分布，α的峰值约为0.10，β的峰值约为0.12，γ的峰值约为0.08，正偏度表明向较高值方向的尾巴较长。这些研究结果强调，迫切需要采取政策干预措施，遏制森林砍伐，同时加强再造林工作。重要的是，模型中确定的环境退化和迅速的人口压力对公共健康有直接影响，包括水媒和病媒传播疾病的风险增加，获得清洁空气和食物来源的机会减少，以及流离失所者面临的长期身心健康挑战。总的来说，这项研究突出了主要的环境影响驱动因素及其健康后果，强调了在难民收容地区进行综合跨部门规划的必要性。

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引用次数: 0

A fractional order PID-based sliding mode controller approach for chemical processes 基于分数阶pid的化工过程滑模控制器方法

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-24 DOI: 10.1016/j.rico.2025.100592

Oscar Camacho , Sebastian Vega , Marco Herrera , Antonio Di Teodoro , Juan J. Gude

This paper proposes a novel control strategy for chemical processes by integrating fractional-order PID (FOPID) controllers with sliding mode control (SMC). Through the use of the enhanced flexibility and superior tuning capabilities of FOPID controllers over traditional PID schemes, the method replaces the classical discontinuous switching mechanism of SMC with a smooth fractional-order control action. The proposed hybrid approach is evaluated through simulations in two nonlinear systems, a mixing tank with variable time delay and a pH neutralization process, and experimentally validated using the TCLab device. Throughout three case studies, the method demonstrates improvements in performance and response between 40% and 10% compared to the other two SMC alternatives. Furthermore, the approach effectively reduces chattering, improves convergence speed, and improves robustness to measurement noise, contributing to extended actuator lifespan. This makes the proposed methodology particularly attractive for chemical process applications, offering a practical and accessible solution for plant operators by enabling the utilization of robust control techniques without requiring deep expertise in nonlinear control design.

将分数阶PID （FOPID）控制器与滑模控制（SMC）相结合，提出了一种新的化工过程控制策略。该方法利用FOPID控制器相对于传统PID方案增强的灵活性和优越的整定能力，以平滑的分数阶控制动作取代SMC的经典不连续切换机制。通过在两个非线性系统、变时延混合槽和pH中和过程中进行仿真，对所提出的混合方法进行了评估，并使用TCLab装置进行了实验验证。在三个案例研究中，与其他两种SMC替代方案相比，该方法的性能和响应提高了40%到10%。此外，该方法有效地减少了抖振，提高了收敛速度，提高了对测量噪声的鲁棒性，有助于延长执行器的使用寿命。这使得所提出的方法对化学过程应用特别有吸引力，通过启用鲁棒控制技术的利用，为工厂操作员提供了实用且易于访问的解决方案，而无需在非线性控制设计方面拥有深厚的专业知识。

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引用次数: 0

Estimation of performance measures in a novel M/M/1 queueing model with reverse balking: A simulation-based approach 基于仿真的新型M/M/1排队模型的性能评估

Q3 Mathematics

Results in Control and Optimization

Pub Date : 2025-06-24 DOI: 10.1016/j.rico.2025.100590

Asmita Tamuli , Dhruba Das , V. Deepthi , Amit Choudhury , Dibyajyoti Bora , Bhushita Patowari , Supahi Mahanta

This article introduces a novel M/M/1 queueing model that incorporates the concept of reverse balking, where customers are more likely to join the queue as the system size increases. Traditional queuing models often assume a constant balking rate or state-dependent balking rate where the balking rate decreases with increase in system size. In contrast, reverse balking reflects scenarios where customer behavior is influenced by more number of customers present in the system. We use a simulation-based approach to estimate key performance measures, including traffic intensity, average system size and average queue length of the proposed model using both classical and Bayesian approaches. In the classical approach, we used the Maximum Likelihood (ML) Estimation procedure to estimate the parameters using the Metropolis-Hastings (MH) algorithm. Moreover, the Bayesian approach employed the Sampling Importance Resampling (SIR) technique to estimate the parameters. The effectiveness of all the estimation techniques has been evaluated based on the root mean squared error (RMSE) of the estimates. The computational results demonstrate that estimates under both approaches converge to the true values as the sample size increases. Moreover, Bayesian estimates yield lower RMSE compared to ML estimates, highlighting their superior accuracy and robustness. Additionally, predictive probabilities for the number of customers in the system are obtained. A real-life application is presented to demonstrate the practical relevance of the proposed study. The findings offer valuable implications for managing and optimizing service systems where reverse balking is common.

本文介绍了一种新的M/M/1排队模型，该模型包含了反向排队的概念，即随着系统大小的增加，客户更有可能加入队列。传统的排队模型通常假设一个恒定的停滞率或状态依赖的停滞率，其中停滞率随着系统大小的增加而降低。相反，反向退缩反映了客户行为受到系统中存在的更多客户数量影响的情况。我们使用基于模拟的方法来估计关键性能指标，包括使用经典和贝叶斯方法的交通强度，平均系统大小和平均队列长度。在经典方法中，我们使用最大似然（ML）估计过程使用Metropolis-Hastings （MH）算法估计参数。此外，贝叶斯方法采用采样重要性重采样（SIR）技术来估计参数。基于估计的均方根误差（RMSE）对所有估计技术的有效性进行了评估。计算结果表明，随着样本量的增加，两种方法下的估计都收敛于真实值。此外，与ML估计相比，贝叶斯估计产生更低的RMSE，突出了其优越的准确性和鲁棒性。此外，还获得了系统中客户数量的预测概率。提出了一个现实生活中的应用，以证明所提出的研究的实际意义。研究结果为管理和优化服务系统提供了有价值的启示，其中反向阻碍是常见的。

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