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A predictive model of photosynthetic rates for eggplants: Integrating physiological and environmental parameters

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 234卷

作者： Gao, Pan Lu, Miao Yang, Yongxia Li, Huiming Tian, Shijie Hu, Jin Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO2], and light intensity. The Fo (initial fluorescence) and Fv/Fm (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fo and Fv/Fm, along with temperature, [CO2], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant *** results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 mu mol center dot m- 2 center dot s- 1, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize

关键词： Photosynthetic rate Chlorophyll fluorescence Predictive model Neural networks optimization algorithms

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MutaSwarmClus: enhancing data clustering efficiency with mutation-enhanced swarm algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-24页

作者： Malik, Saleem Patro, S. Gopal Krishna Mahanty, Chandrakanta Lasisi, Ayodele Al-sareji, Osamah J. PA Coll Engn CSE Dept Mangalore 574153 India Woxsen Univ Sch Technol Hyderabad Telangana India GITAM Deemed Univ GITAM Sch Technol Dept Comp Sci & Engn Visakhapatnam 530045 India King Khalid Univ Coll Comp Sci Dept Comp Sci Abha Saudi Arabia Univ Pannonia Fac Engn Sustainabil Solut Res Lab Egyet Str 10 H-8200 Veszprem Hungary

Data clustering is a fundamental technique in data mining, pivotal for various applications such as statistical analysis and data compression. Traditional clustering algorithms often struggle with noisy or high-dimensional datasets, hindering their efficacy in addressing real-world challenges. In response, this research introduces MutaSwarmClus, a novel hybrid metaheuristic algorithm that combines Mouth Brooding Fish (MBF), Ant Colony optimization (ACO), and mutation operators to enhance clustering quality. MutaSwarmClus intends to adaptively control the exploration and exploitation phases of the solution space, solve issues with local optima and changes in the distribution of available data. Moreover, it incorporates an Iterated Local Search (ILS) to refine solutions and avoiding getting stuck in local optima. MutaSwarmClus therefore increases the robustness of the clustering process by incorporating controlled randomness through mutation operators in order to handle noisy and outlier data points well. According to the contributions analysis, the proposed algorithm improves the clustering solution with the combined system of MBF, ACO, and mutation operators, which enables the mechanism of exploration and exploitation in the process of information search. As shown through the results of experimental studies, MutaSwarmClus has high performance when used with various benchmarks, and outperforms or performs as well as or better than compared to other clustering algorithms such as K-means, ALO, Hybrid ALO, and MBF. It achieves an average error rate of only 10%, underscoring its accuracy in clustering tasks. The utilization of MutaSwarmClus offers a solution to the existing problems in clustering large datasets in terms of scalability, efficiency and accuracy. Possible directions for future work can continue to optimize the model parameters of the algorithm and study its adaptability in dynamic conditions and with large amounts of data.

关键词： Data clustering optimization algorithms Hybrid approaches MBF

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Training hybrid neural networks with multimode optical nonlinearities using digital twins

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NANOPHOTONICS 2025年

作者： Oguz, Ilker Suter, Louis J. E. Hsieh, Jih-Liang Yildirim, Mustafa Dinc, Niyazi Ulas Moser, Christophe Psaltis, Demetri Ecole Polytech Fed Lausanne Inst Elect & Micro Engn CH-1015 Lausanne Switzerland

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the complexity of trainable layers. Here, we utilize ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations, for this purpose. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity. Moreover, the framework demonstrates exceptional resistance to experimental drifts. By integrating low-energy physical systems into neural networks, this approach enables scalable, energy-efficient AI models with significantly reduced computational demands.

关键词： optical computing digital twins neural networks artificial intelligence fiber optics optimization algorithms

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Reducing the space used by the sieve of Eratosthenes when factoring

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INFORMATION PROCESSING LETTERS 2025年 188卷

作者： Hartman, Samuel Sorenson, Jonathan P. Butler Univ Comp Sci & Software Engn Indianapolis IN 46208 USA

/ We present a version of the sieve of Eratosthenes that can factor all integers <= x in O(x log log x) arithmetic operations using at most O( x/ log log x) bits of space. Among algorithms that take the optimal O(xlog log x) time, this new space bound is an improvement of a factor proportional to log xlog log x over the implied / previous bound of O( xlogx). We also show our algorithm performs well in practice.

关键词： optimization algorithms

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An intelligent cyber threat detection: A swarm-optimized machine learning approach

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ALEXANDRIA ENGINEERING JOURNAL 2025年 115卷 553-563页

作者： Qiqieh, Issa Alzubi, Omar Alzubi, Jafar Sreedhar, K. C. Al-Zoubi, Ala' M. Al Balqa Appl Univ Fac Engn Al Salt 19117 Jordan Al Balqa Appl Univ Fac Artificial Intelligence Al Salt 19117 Jordan Sreenidhi Inst Sci & Technol Dept CSE Hyderabad India Al Zaytoonah Univ Jordan Dept Data Sci & Artificial Intelligence Amman Jordan

Cyber threats are an ongoing problem that is hard to prevent completely. This can occur for various reasons, but the main causes are the evolving techniques of hackers and the neglect of security measures when developing software or hardware. Asa result, several countermeasures will need to be applied to mitigate these threats. Cyber-threat detection techniques can fulfill this role by utilizing different identification methods for various cyber threats. In this work, an intelligent cyber threat detection system employing a swarm-based machine learning approach is proposed. The approach involves using Harris Hawks optimization (HHO) to enhance the Support Vector Machine (SVM) for improved threat detection through parameter tuning and feature weighting. Furthermore, various cyber-threat types have been considered, including Fake News, IoT Intrusion, Malicious URLs, Spam Emails, and Spam Websites. The proposed HHO-SVM has been compared to other approaches for detecting all these types collectively. The HHO-SVM outperforms all algorithms inmost types (datasets). The proposed approach demonstrated the highest accuracy across seven datasets: FakeNews-1, FakeNews-2, FakeNews-3, IoT-ID, URL, SpamEmail-2, and SpamWebsites, achieving average accuracy of 68.251%, 68.729%, 79.049%, 95.254%, 100%, 96.681%, and 93.975%, respectively. Additionally, a thorough analysis of each cyber-threat type has been conducted to understand their characteristics and detection strategies.

关键词： Cyber threats Detection optimization algorithms Fake news IoT intrusion Malicious URLs Spam emails

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How the choice of model calibration procedure affects projections of lake surface water temperatures for future climatic conditions

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JOURNAL OF HYDROLOGY 2025年 659卷

作者： Napiorkowski, Jaroslaw J. Piotrowski, Adam P. Osuch, Marzena Zhu, Senlin Karamuz, Emilia Polish Acad Sci Inst Geophys Ks Janusza 64 PL-01452 Warsaw Poland Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225127 Peoples R China

Almost all hydrological models require calibration. The same model but with different parameters may lead to diverse simulations of the hydrological phenomena. Hence, the choice of a calibration method may affect the model performance. The present paper is the first study analyzing how the choice of air2water model calibration procedure may influence projections of surface water temperature in lowland lakes under future climatic conditions. To address this issue, projections from 14 atmospheric circulation models, data from 22 lowland Polish lakes located in a temperate climate zone, and 12 different optimization algorithms are employed. The studied lake areas range from 1.5 km2 to 115 km2, and their maximum depths range from 2.5 m to 70 m. Depending on which calibration algorithm is applied, the differences in mean monthly surface water temperatures projected for future climatic conditions may exceed 1.5 degrees C for a small deep lake. On the contrary, the differences observed for shallow and relatively large lakes, due to the optimization procedure used, were lower than 0.6 degrees C each month. The largest differences in projected lake water temperatures were observed for the winter and summer months, which are especially critical for aquatic biota. Among the optimization algorithms resulting in the largest differences were those that fit historical data well, as well as those that do not reproduce historical data appropriately. Therefore, strong performance for historical data does not guarantee reliable projections for future conditions. We have shown that projected lake water temperatures largely depend on the calibration method used for a particular model.

关键词： Surface lake water temperature Air2water Climate change optimization algorithms Model reliability Impact studies Hydrological projections

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Comparative analysis of intelligent retrofit design methods of RC frame structures using buckling-restrained braces

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BULLETIN OF EARTHQUAKE ENGINEERING 2025年 1-22页

作者： Qin, Sizhong Liao, Wenjie Tan, Zhuang Hu, Kongguo Gao, Yuan Lu, Xinzheng Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China State Key Lab Bridge Intelligent & Green Construct Chengdu 610031 Peoples R China Tsinghua Univ Dept Civil Engn Key Lab Civil Engn Safety & Durabil China Educ Minist Beijing 100084 Peoples R China China Elect Engn Design Inst Co Ltd Beijing 100840 Peoples R China

The development of intelligent design methods for buckling-restrained brace (BRB) retrofit schemes can effectively enhance the seismic performance of reinforced concrete (RC) frame structures to address their insufficient seismic capacity. This study further explores the two-stage intelligent design framework for BRB retrofitting by combining generative artificial intelligence (AI) and optimization algorithms. In Stage 1, generative AI models, including diffusion models, generative adversarial networks (GANs), and graph neural networks, extract features from design drawings to identify potential BRB locations. In Stage 2, optimization algorithms, such as genetic algorithms, simulated annealing, and online learning, integrated with YJK Y-GAMA software, determine the optimal placement and sizing of the BRBs. A comprehensive comparative analysis of design performance and efficiency is conducted for different algorithm combinations in both stages. The results indicate that GANs and diffusion models effectively capture both global and local design features, and genetic algorithms provide an efficient exploration of the design space. Combining these methods yields near-optimal solutions in a short time, ensuring compliance with mechanical standards and cost-effectiveness. In conclusion, this study offers valuable recommendations for the selection of generative AI methods and optimization algorithms in the design process, with the potential to promote the application of intelligent design in engineering practice.

关键词： Intelligent structural design Retrofit using BRBs Generative AI optimization algorithms RC frame structure

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Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithms

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Soft Computing 2025年第4期29卷 1967-1980页

作者： Sarkar, Debojyoti Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India

In this paper, an evaluation strategy is proposed for evaluation of optimization algorithms, called the Complex Preference Analysis, that assesses the efficiency of different evolutionary algorithms by considering multiple performance metrics concurrently across diverse dimensions and generating a single score for ranking. The proposed strategy allows for a thorough evaluation by assigning distinct priorities in the form of weights, to various performance parameters. The algorithm’s performances on each performance parameter are captured and the best and worst performances in each performance category are identified. The deviation of each algorithm from the best and worst performances in the respective performance category is formulated to generate a single score for each of the algorithms for comparison. The flexibility to adjust weights provides a valuable tool for customizing evaluations according to individual needs and preferences. The proposed evaluation strategy is studied by running evaluations on nine evolutionary algorithms based on their performance over five performance indicators on six datasets of the Travelling Salesman Problem. Conducting Complex Preference Analysis on the different evolutionary algorithms reveals distinctive patterns in their performance, with some algorithms such as Simulated Annealing, Tabu Search, Bat Algorithm, Genetic Algorithm, etc. consistently demonstrating superior results across multiple Travelling Salesman Problem instances, while other algorithms such as Ant Colony optimization, Teaching Learning Based optimization, etc. rank lower. The algorithms’ performances on individual performance parameters, when reflected on the final rank of the algorithms, generated based on the collective impact of all the parameters, elucidate the efficiency of the proposed evaluation strategy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： optimization algorithms

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Energy-Efficient Coverage Enhancement Strategy for WSNs Based on a Competitive Learning Optimizer

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IEEE Internet of Things Journal 2025年第10期12卷 14548-14558页

作者： Ren, Jin Li, Yibo North China University of Technology School of Information Science and Technology Department of Electrical Engineering Beijing100144 China

Energy-efficient coverage enhancement (EEC) is a highly non-convex and challenging optimization problem in wireless sensor networks (WSNs) deployment. Traditional intelligent optimization algorithms often suffer from premature convergence and low efficiency when solving the EEC problem. In this paper, a competitive learning optimizer (CLO) is proposed and upgraded to a multi-objective competitive learning optimizer (MOCLO), which is inspired by the behavior of human competitive learning in workplace. In order to verify the single-objective optimization capability of CLO, it is applied to the coverage problem in 2D and 3D WSNs, and the simulation results indicate that CLO achieves the overall best results in terms of solution accuracy, stability and convergence by comparing with five well-regarded optimization algorithms. For solving the EEC problem in 2D and 3D WSNs, MOCLO is applied. By comparing three popular multi-objective optimization algorithms, it is found that MOCLO can consume less energy to achieve higher coverage, which proves the effectiveness and superiority of MOCLO in addressing the EEC problem. © 2014 IEEE.

关键词： optimization algorithms

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Enhanced prediction of energy dissipation rate in hydrofoil-crested stepped spillways using novel advanced hybrid machine learning models

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RESULTS IN ENGINEERING 2025年 25卷

作者： Afaridegan, Ehsan Amanian, Nosratollah Yazd Univ Fac Engn Dept Civil Engn Yazd *** Iran

Accurately estimating the Energy Dissipation Rate (EDR) in Hydrofoil-Crested Stepped Spillways (HCSSs) is crucial for ensuring the safety and optimizing the performance of these hydraulic structures. This study investigates the prediction of EDR using advanced hybrid Machine Learning (ML) models, including the Tabular Neural Network with Moth Flame optimization (TabNet-MFO), Long Short-Term Memory with Ant Lion Optimizer (LSTM-ALO), Extreme Learning Machine with Jaya and Firefly optimization (ELM-JFO), and Support Vector Regression with Improved Whale optimization (SVR-IWOA). Notably, two novel models-TabNet-MFO and SVR-IWOA-are introduced for the first time, providing dynamic hyperparameter optimization to enhance prediction accuracy in complex hydraulic conditions. To develop the models, a dataset comprising 462 laboratory data points from HCSS experiments was used, with 75 % allocated for the training stage and 25 % for the testing stage. The Isolation Forest (IF) algorithm was employed to detect and remove outliers, resulting in the exclusion of 5 % of the original dataset. Dimensional analysis was conducted to identify key factors influencing EDR, including step number (NS), chute angle (theta), hydrofoil formation index (t), and the ratio of critical depth to total chute height (yC / PS). ANOVA and SHAP analyses confirmed the significant impact of the yC / PS ratio on EDR. Model performance was evaluated using metrics such as the coefficient of determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Weighted Mean Absolute Percentage Error (WMAPE), and symmetric Mean Absolute Percentage Error (sMAPE). Performance was further compared using Taylor diagrams, residual error curves (REC), and the Performance Index (PI). During the training stage, TabNet-MFO outperformed the other models with a PI of 0.784 and a normalized Root Mean Squared Error (E') of 1.231, followed by ELM-JFO with a PI of 0.605 and E' of 1.125. In the testing stage, TabNet-MFO m

关键词： Energy dissipation rate Hybrid machine learning models Isolation forest optimization algorithms Shapley additive explanations

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