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        <title>Frontiers in Applied Mathematics and Statistics | Optimization section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/applied-mathematics-and-statistics/sections/optimization</link>
        <description>RSS Feed for Optimization section in the Frontiers in Applied Mathematics and Statistics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-08T14:01:45.242+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1742828</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1742828</link>
        <title><![CDATA[Fuzzy hyperheuristic optimization of a facilitated hub-and-spoke drone-enabled logistics network: a case study of Australia Post]]></title>
        <pubdate>2026-04-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kassem Danach</author><author>Samir Haddad</author><author>Wissam Khalil</author><author>Ziad El Balaa</author><author>Jinane Sayah</author>
        <description><![CDATA[IntroductionThe rapid growth of e-commerce has increased pressure on postal logistics networks, especially in remote regions.MethodsThis study proposes a fuzzy hyperheuristic genetic algorithm for optimizing a facilitated hub-and-spoke network with drone integration under uncertainty.ResultsThe proposed approach improves service robustness by 25–35% and expands drone coverage to 93.4% of remote demand, with only a modest cost increase (8–12%).DiscussionResults highlight the effectiveness of fuzzy optimization and adaptive hyperheuristics in designing resilient and cost-efficient postal logistics systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1809903</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1809903</link>
        <title><![CDATA[Low-rank tensor completion with fractal-inspired multi-scale energy regularization]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chuling Wen</author><author>Weijie Liang</author><author>Chen Xu</author><author>Yuru Zou</author>
        <description><![CDATA[Low-rank tensor completion has become a fundamental tool for recovering high-dimensional data from incomplete observations. However, conventional methods rely primarily on algebraic low-rank priors and do not explicitly regulate how signal energy is distributed across scales. This study introduces a fractal-inspired multi-scale energy regularization that enforces approximate power-law scaling of tensor energy across resolution levels. The proposed formulation integrates scale-consistency constraints with tensor nuclear norm regularization in a unified framework. An inexact ADMM algorithm is developed to solve the resulting non-convex problem. Experimental results demonstrate consistent improvements in reconstruction accuracy across tensor sizes and observation ratios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1763637</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1763637</link>
        <title><![CDATA[Advancing bearing fault detection through a modified metaheuristic optimization approach]]></title>
        <pubdate>2026-03-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lana A. Abullah</author><author>Chnoor M Rahman</author>
        <description><![CDATA[IntroductionDetecting bearing faults plays a vital role in industrial maintenance since discovering problems early can help avoid unexpected breakdowns and expensive production losses. Yet, spotting these faults in their initial stages is still difficult because vibration signals are often complex and change over time.MethodsIn this study, optimized Mel Frequency Cepstral Coefficients (MFCC) feature extraction approach enhanced through a modified FOX optimization algorithm. The enhancement focuses on fine-tuning MFCC hyperparameters to maximize the discriminative power of extracted features for fault detection tasks. The proposed Enhanced FOX (EFOX) algorithm integrates different random distribution method and improved exploration–exploitation balance, enabling more effective parameter optimization compared to conventional methods.ResultsExperimental evaluations were conducted using benchmark datasets, and the optimized MFCC features were compared against those obtained via standard MFCC settings and other metaheuristic optimization techniques. Results demonstrate that our approach consistently outperforms competing methods in terms of classification accuracy and the robustness of the proposed model was assessed by testing it on two distinct bearing’s datasets with different noise ratios including −3 dB and −6 dB.DiscussionThe analysis highlights the impact of each of hyperparameter’s of MFCC to bearing fault detection.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2026.1774262</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2026.1774262</link>
        <title><![CDATA[Flexibility-oriented robust optimization planning for electro-hydrogen energy storage in high-renewable grids]]></title>
        <pubdate>2026-02-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wang Yan</author>
        <description><![CDATA[The large-scale integration of renewable energy sources poses significant challenges to grid stability due to inherent intermittency and volatility. This paper presents a novel robust optimization framework for planning electro-hydrogen energy storage systems (EHESS) that differs from traditional capacity planning by explicitly incorporating flexibility margin indices. We develop a comprehensive electro-hydrogen coupling model that captures the coordinated operational characteristics of battery storage (short-term regulation) and hydrogen systems (long-term shifting). Unlike existing works that treat flexibility qualitatively, we introduce a quantified flexibility margin index to measure the supply-demand gap of ramping capabilities. We formulate a two-layer robust optimization model: the upper layer minimizes investment costs, while the lower layer minimizes operational and flexibility penalty costs under worst-case scenarios. Wasserstein distance-based uncertainty sets are employed to handle the distributional uncertainty of renewable output. Case simulations on a modified IEEE 33-node system validate that the proposed method effectively determines the optimal configuration, reducing total costs by 10.6% compared to baselines by mitigating high-cost flexibility violations.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1698876</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1698876</link>
        <title><![CDATA[A multiple stakeholder-based target-oriented robust optimization approach and its applications]]></title>
        <pubdate>2026-01-21T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Jivulter Mangubat</author><author>Celbert Himang</author><author>Marlon Solloso</author><author>Dexter Alit</author><author>Melanie Himang</author><author>Milcah Mangubat</author><author>Patrobinson Salumag</author><author>Miriam Bongo</author>
        <description><![CDATA[In real-world decision-making, multiple stakeholders often participate, each with diverse and sometimes conflicting interests, which may fall exclusively under the expertise of individual decision-makers. Existing multiple-criteria decision-making (MCDM) methods can accommodate multiple criteria but typically fail to reconcile conflicting stakeholder priorities into a satisficing solution. To address this gap, this paper proposes the multiple stakeholder-based target-oriented robust-optimization (MS-TORO) approach, which explicitly embeds stakeholder interests into an optimization framework that minimizes deviations among priorities. The implementation procedure involves eliciting ordinal stakeholder preferences, parameterizing trade-offs, and solving the optimization model to generate an aggregated solution. Three case studies demonstrate the applicability and viability of MS-TORO, showing that it effectively produces solutions that satisfy the performance targets defined by each decision-maker across all criteria.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1764289</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1764289</link>
        <title><![CDATA[Editorial: Optimization for low-rank data analysis: theory, algorithms and applications]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>HanQin Cai</author><author>Dong Xia</author><author>Ernest Domanaanmwi Ganaa</author><author>Abiy Tasissa</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1640044</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1640044</link>
        <title><![CDATA[EESB-FDO: enhancing the fitness-dependent optimizer through a modified boundary handling mechanism]]></title>
        <pubdate>2025-10-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aram Kamal Faraj</author><author>Aso M. Aladdin</author><author>Azad A. Ameen</author>
        <description><![CDATA[The fitness-dependent optimizer (FDO) has recently gained attention as an effective metaheuristic for solving different optimization problems. However, it faces limitations in exploitation and convergence speed. To overcome these challenges, this study introduces two enhanced variants: enhancing exploitation through stochastic boundary for FDO (EESB-FDO) and enhancing exploitation through boundary carving for FDO (EEBC-FDO). In addition, the ELFS strategy is proposed to constrain Levy flight steps, ensuring more stable exploration. Experimental results show that these modifications significantly improve the performance of FDO compared to the original version. To evaluate the performance of the EESB-FDO and EEBC-FDO, three primary categories of benchmark test functions were utilized: classical, CEC 2019, and CEC 2022. The assessment was further supported by the application of statistical analysis methods to ensure a comprehensive and rigorous performance evaluation. The performance of the proposed EESB-FDO and EEBC-FDO algorithms was evaluated through comparative analysis with several existing FDO modifications, as well as with other well-established metaheuristic algorithms, including the Arithmetic Optimization Algorithm (AOA), the Learner Performance-Based Behavior Algorithm (LPB), the Whale Optimization Algorithm (WOA), and the Fox-inspired Optimization Algorithm (FOX). The statistical analysis indicated that both EESB-FDO and EEBC-FDO exhibit better performance compared to the aforementioned algorithms. Furthermore, a final evaluation involved applying EESB-FDO and EEBC-FDO to four real-world optimization problems: the gear train design problem, the three-bar truss problem, the pathological igg fraction in the nervous system, and the integrated cyber-physical attack on a manufacturing system. The results demonstrate that both proposed variants significantly outperform both the FDO and the modified fitness-dependent optimizer (MFDO) in solving these complex problems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1594873</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1594873</link>
        <title><![CDATA[Plug-and-play low-rank tensor completion and reconstruction algorithms with improved applicability of tensor decompositions]]></title>
        <pubdate>2025-09-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manabu Mukai</author><author>Hidekata Hontani</author><author>Tatsuya Yokota</author>
        <description><![CDATA[In this paper, we propose a new unified optimization algorithm for general tensor completion and reconstruction problems, which is formulated as an inverse problem for low-rank tensors in general linear observation models. The proposed algorithm supports at least three basic loss functions (ℓ2 loss, ℓ1 loss, and generalized KL divergence) and various TD models (CP, Tucker, TT, TR decompositions, non-negative matrix/tensor factorizations, and other constrained TD models). We derive the optimization algorithm based on a hierarchical combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM). We show that the proposed algorithm can solve a wide range of applications and can be easily extended to any established TD model in a plug-and-play manner.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1628652</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1628652</link>
        <title><![CDATA[Accounting data anomaly detection and prediction based on self-supervised learning]]></title>
        <pubdate>2025-09-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yingying Zhang</author><author>Bingbing Duan</author>
        <description><![CDATA[This study proposes a Hierarchical Fusion Self-Supervised Learning (HFSL) framework to address the challenge of scarce labeled data in accounting anomaly detection, integrating domain knowledge with advanced deep learning techniques. Based on financial data from Chinese listed companies in the CSMAR database spanning 2000–2020, this framework integrates temporal contrastive learning, a dual-channel LSTM autoencoder structure, and financial domain knowledge to construct a three-tier cascaded detection system. Empirical research demonstrates that the HFSL framework achieves a precision of 0.836, recall of 0.805, and F1 score of 0.820 in accounting anomaly detection, significantly outperforming traditional methods. In terms of practical metrics, the framework attains an early detection rate of 0.726 while maintaining a false alarm rate of just 0.068, providing technical support for early risk warning. Financial feature contribution analysis reveals that core indicators such as Return on Assets (ROA), Return on Equity (ROE), and their interaction effects play crucial roles in anomaly identification. Through analysis of 2,150 samples in the test set, the study identifies five typical financial fraud patterns (revenue inflation 38.6%, expense concealment 21.7%, asset overvaluation 17.4%, liability understatement 15.2%, and composite manipulation 7.1%) and their temporal evolution characteristics. The research also finds that financial anomalies typically exhibit three evolutionary patterns: progressive deterioration (64%), sudden anomalies (22%), or cyclical fluctuations (15%), providing empirical evidence for regulatory practice. This study applies self-supervised learning to accounting anomaly detection, not only solving the detection challenges in unlabeled data scenarios but also providing effective tools for financial supervision and risk management.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1589033</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1589033</link>
        <title><![CDATA[A sparse tensor generator with efficient feature extraction]]></title>
        <pubdate>2025-07-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tugba Torun</author><author>Ameer Taweel</author><author>Didem Unat</author>
        <description><![CDATA[Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most suitable storage format, decomposition algorithm, and reordering methods. However, due to the large size of real-world tensors, even extracting these features can be computationally expensive without careful optimization. To address these limitations, we have developed a smart sparse tensor generator that replicates key characteristics of real sparse tensors. Additionally, we propose efficient methods for extracting a comprehensive set of sparse tensor features. The effectiveness of our generator is validated through the quality of extracted features and the performance of decomposition on the generated tensors. Both the sparse tensor feature extractor and the tensor generator are open source with all the artifacts available at https://github.com/sparcityeu/FeaTensor and https://github.com/sparcityeu/GenTensor, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1629658</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1629658</link>
        <title><![CDATA[Corrigendum: Expectation-maximization alternating least squares for tensor network logistic regression]]></title>
        <pubdate>2025-07-01T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Naoya Yamauchi</author><author>Hidekata Hontani</author><author>Tatsuya Yokota</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1593680</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1593680</link>
        <title><![CDATA[Expectation-maximization alternating least squares for tensor network logistic regression]]></title>
        <pubdate>2025-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Naoya Yamauchi</author><author>Hidekata Hontani</author><author>Tatsuya Yokota</author>
        <description><![CDATA[In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function model, a huge number of basis functions and coefficients are generally required, but the TN model makes it possible to avoid the curse of dimensionality by representing the huge coefficients using TNs. However, there is a problem with TN learning, namely the gradient vanishing, and learning using the gradient method cannot be performed efficiently. In this study, we propose a novel optimization algorithm for learning TN classifiers by using alternating least square (ALS) algorithm. Unlike conventional gradient-based methods, which suffer from vanishing gradients and inefficient training, our proposed approach can effectively minimize squared loss and logistic loss. To make ALS applicable to logistic regression, we introduce an auxiliary function derived from Pólya-Gamma augmentation, allowing logistic loss to be minimized as a weighted squared loss. We apply the proposed method to the MNIST classification task and discuss the effectiveness of the proposed method.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1587681</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1587681</link>
        <title><![CDATA[Internet traffic data recovery via a low-rank spatio-temporal regularized optimization approach without d-th order T-SVD]]></title>
        <pubdate>2025-05-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuxuan Duan</author><author>Chen Ling</author><author>Jinjie Liu</author><author>Xinmin Yang</author>
        <description><![CDATA[Accurate recovery of Internet traffic data can mitigate the adverse impact of incomplete data on network task processes. In this study, we propose a low-rank recovery model for incomplete Internet traffic data with a fourth-order tensor structure, incorporating spatio-temporal regularization while avoiding the use of d-th order T-SVD. Based on d-th order tensor product, we first establish the equivalence between d-th order tensor nuclear norm and the minimum sum of the squared Frobenius norms of two factor tensors under the unitary transformation domain. This equivalence allows us to leave aside the d-th order T-SVD, significantly reducing the computational complexity of solving the problem. In addition, we integrate the alternating direction method of multipliers (ADMM) to design an efficient and stable algorithm for precise model solving. Finally, we validate the proposed approach by simulating scenarios with random and structured missing data on two real-world Internet traffic datasets. Experimental results demonstrate that our method exhibits significant advantages in data recovery performance compared to existing methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2025.1477774</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2025.1477774</link>
        <title><![CDATA[Numerical optimization of large-scale monotone equations using the free-derivative spectral conjugate gradient method]]></title>
        <pubdate>2025-01-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ghulam Abbass</author><author>Nek Muhammad Katbar</author><author>Israr Ahmed Memon</author><author>Haibo Chen</author><author>Fikadu Tesgera Tolasa</author><author>Gemeda Tolessa Lubo</author>
        <description><![CDATA[This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the resolution of complex nonlinear problems. This innovative technique ensures global convergence and descent condition supported by carefully considered assumptions. The efficiency and effectiveness of the proposed method is highlighted by its outstanding numerical performance. To validate our claims, large-scale numerical simulations were conducted. These tests were designed to evaluate the capabilities of our proposed algorithm rigorously. In addition, we performed a comprehensive comparative numerical analysis, benchmarking our method against existing techniques. This analysis revealed that our approach consistently outperformed others in terms of theoretical robustness and numerical efficiency. The superiority of our method is evident in its ability to solve large-scale problems with accuracy in function evaluations, fewer iterations, and improved computational performance thereby, making it a valuable contribution to the field of numerical optimization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2024.1445390</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2024.1445390</link>
        <title><![CDATA[A DC programming to two-level hierarchical clustering with ℓ1 norm]]></title>
        <pubdate>2024-09-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adugna Fita Gabissa</author><author>Legesse Lemecha Obsu</author>
        <description><![CDATA[The main challenge in solving clustering problems using mathematical optimization techniques is the non-smoothness of the distance measure used. To overcome this challenge, we used Nesterov's smoothing technique to find a smooth approximation of the ℓ1 norm. In this study, we consider a bi-level hierarchical clustering problem where the similarity distance measure is induced from the ℓ1 norm. As a result, we are able to design algorithms that provide optimal cluster centers and headquarter (HQ) locations that minimize the total cost, as evidenced by the obtained numerical results.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2024.1385590</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2024.1385590</link>
        <title><![CDATA[A new Steiner symmetrization defined by a subclass of analytic function in a complex domain]]></title>
        <pubdate>2024-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ibtehal Alazman</author><author>Rabha W. Ibrahim</author>
        <description><![CDATA[In this effort, we present a new definition of the Steiner symmetrization by using special analytic functions in a complex domain (the open unit disk) with respect to the origin. This definition will be used to optimize the class of univalent analytic functions. Our method is based on the concept of differential subordination and the Carathéodory theory. Examples are illustrated in the sequel involving the modified Libera–Livingston–Bernardi integral operator over the open unit disk. The result gives that this integral satisfies the definition of bounded turning function (univalent analytic function).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2024.1287074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2024.1287074</link>
        <title><![CDATA[Sparseness-constrained nonnegative tensor factorization for detecting topics at different time scales]]></title>
        <pubdate>2024-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lara Kassab</author><author>Alona Kryshchenko</author><author>Hanbaek Lyu</author><author>Denali Molitor</author><author>Deanna Needell</author><author>Elizaveta Rebrova</author><author>Jiahong Yuan</author>
        <description><![CDATA[Temporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time. We first demonstrate that nonnegative CANDECOMP/PARAFAC decomposition (NCPD) can automatically identify topics of variable persistence. We then introduce sparseness-constrained NCPD (S-NCPD) and its online variant to control the duration of the detected topics more effectively and efficiently, along with theoretical analysis of the proposed algorithms. Through an extensive study on both semi-synthetic and real-world datasets, we find that our S-NCPD and its online variant can identify both short- and long-lasting temporal topics in a quantifiable and controlled manner, which traditional topic modeling methods are unable to achieve. Additionally, the online variant of S-NCPD shows a faster reduction in reconstruction error and results in more coherent topics compared to S-NCPD, thus achieving both computational efficiency and quality of the resulting topics. Our findings indicate that S-NCPD and its online variant are effective tools for detecting and controlling the duration of topics in temporal text data, providing valuable insights into both persistent and transient trends.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2024.1284706</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2024.1284706</link>
        <title><![CDATA[Random vector functional link networks for function approximation on manifolds]]></title>
        <pubdate>2024-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Deanna Needell</author><author>Aaron A. Nelson</author><author>Rayan Saab</author><author>Palina Salanevich</author><author>Olov Schavemaker</author>
        <description><![CDATA[The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this study, we begin to fill this theoretical gap. We then extend this result to the non-asymptotic setting using a concentration inequality for Monte-Carlo integral approximations. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error squared decaying asymptotically like O(1/n) for the number n of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided n is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2024.1304268</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2024.1304268</link>
        <title><![CDATA[Convergence analysis of particle swarm optimization algorithms for different constriction factors]]></title>
        <pubdate>2024-02-14T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Dereje Tarekegn Nigatu</author><author>Tekle Gemechu Dinka</author><author>Surafel Luleseged Tilahun</author>
        <description><![CDATA[Particle swarm optimization (PSO) algorithm is an optimization technique with remarkable performance for problem solving. The convergence analysis of the method is still in research. This article proposes a mechanism for controlling the velocity by applying a method involving constriction factor in standard swarm optimization algorithm, that is called CSPSO. In addition, the mathematical CSPSO model with the time step attractor is presented to study the convergence condition and the corresponding stability. As a result, constriction standard particle swarm optimization that we consider has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, CSPSO modifies all terms of the PSO velocity equation. We test the effectiveness of the CSPSO algorithm based on constriction coefficient with some benchmark functions and compare it with other basic PSO variant algorithms. The theoretical convergence and experimental analyses results are also demonstrated in tables and graphically.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fams.2023.1305367</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fams.2023.1305367</link>
        <title><![CDATA[Evaluation of personal protective equipment to protect health and safety in pesticide use]]></title>
        <pubdate>2024-01-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Güler Aksüt</author><author>Tamer Eren</author>
        <description><![CDATA[IntroductionAgriculture emerges as one of the most dangerous industries in the world, considering injury and illness rates. After the service sector in Turkey, the next large-scale sector is the agricultural sector, which constitutes 20% of the general employment. The exposure of farmers to pesticides, used to increase the quality and productivity of agricultural products, causes health risks via the mouth, respiration, skin, and eyes. Pesticide use in Turkey is increasing; the annual average increase is estimated at 1.2%. Exposure to pesticides can be reduced by wearing personal protective equipment to protect against health and safety hazards.ObjectiveThis study aimed to determine the importance of personal protective equipment using the multi-criteria decision-making method to prevent the risk of injury and disease resulting from pesticide use.Materials and methodsThe Analytical Hierarchy Process (AHP) method was used to find the weights of the criteria determined by expert opinion and a literature review. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used to rank personal protective equipment.ResultsPersonal protective equipment includes masks, gloves, overalls, safety shoes, glasses, and hats. The use of multi-criteria decision-making methods in health and safety in the agricultural sector will contribute to the literature.ConclusionEmphasizing the use of personal protective equipment, especially when using pesticides, will increase the rate of use of protective measures.]]></description>
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