Study on the Process of Soil Clod Removal and Potato Damage in the Front Harvesting Device of Potato Combine Harvester by Zewen Li, Wei Sun, Hucun Wang, Juanling Wang, Petru A. Simionescu

“…A dynamic mathematical model of the bar-lift chain is established, from which the dynamic equations are formulated. …”
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Population dynamics and monitoring applied to decision-making by M. J. Conroy, D. C. Lee

“…Provided estimates of uncertainty in funding, a model for trend in funding, and a model relating funding levels to viability, stochastic dynamic programming can be used to solve for an optimal amount of expenditure during any budget period. …”
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Evidence Generation for a Host-Response Biosignature of Respiratory Disease by Kelly E. Dooley, Michael Morimoto, Piotr Kaszuba, Margaret Krasne, Gigi Liu, Edward Fuchs, Peter Rexelius, Jerry Swan, Krzysztof Krawiec, Kevin Hammond, Stuart C. Ray, Ryan Hafen, Andreas Schuh, Nelson L. Shasha Jumbe

“…The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94–1.0), with 100% sensitivity (95% CI: 82–100%) and 99.6% specificity (95% CI: 85–100%). …”
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Functional and Structural Network Alterations in HIV-Associated Asymptomatic Neurocognitive Disorders: Evidence for Functional Disruptions Preceding Structural Changes by Zhou Z, Gong W, Hu H, Wang F, Li H, Xu F, Li H, Wang W

“…The performance of different models was compared to identify the optimal diagnostic model for detecting HIV-ANI.Results: Structural network analysis showed no significant changes in the global or local topological properties of persons with ANI. …”
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Forecasting the development of poultry farming based on time series by Anatolii Kulyk, Katerina Fokina-Mezentseva, Alla Saiun, Daryna Saiun

“…The study successfully applied advanced data science methods to predict changes in poultry population using a number of efficient models. Analysis of historical data allowed us to determine the optimal parameters of the models and obtain forecast values for time periods (months). …”
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Intelligent Recognition and Automated Production of Chili Peppers: A Review Addressing Varietal Diversity and Technological Requirements by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang, Xiaohu Guo

“…., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. …”
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A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones by Mohamed Abdellatief, Mahmoud E. Abd-Elmaboud, Mohamed Mortagi, Ahmed M. Saqr

“…The CNN’s performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). …”
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Structural Design and Analysis of Bionic Shovel Based on the Geometry of Mole Cricket Forefoot by Shengbo Lin, Hongyan Sun, Guangen Yan, Kexin Que, Sijia Xu, Zhong Tang, Guoqiang Wang, Jiali Li

“…The results show that the optimal combination of operating parameters for the bionic loosening shovel is the rotational speed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ω</mi></mrow></semantics></math></inline-formula> = 5 r/s and the traveling speed of the whole machine v = 0.5 m/s. …”
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An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection by Farouq Zitouni, Abdulaziz S. Almazyad, Guojiang Xiong, Ali Wagdy Mohamed, Saad Harous

“…The feature selection problem involves selecting a subset of relevant features to enhance the performance of machine learning models, crucial for achieving model accuracy. …”
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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. …”
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Strengthening health information systems and inherent statistical outputs for improved malaria control and interventions in western Kenya by Taliyah Griffin, Felix Pabon-Rodriguez, Felix Pabon-Rodriguez, George Ayodo, Yan Zhuang

“…Methods such as spatiotemporal models using climate and case data can improve outbreak predictions, while machine learning techniques can optimize insecticide-treated bed net distributions by pinpointing high-risk households. …”
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Additive vs. Subtractive Manufacturing: A Comparative Life Cycle and Cost Analyses of Steel Mill Spare Parts by Luis Segovia-Guerrero, Nuria Baladés, Juan J. Gallardo-Galán, Antonio J. Gil-Mena, David L. Sales

“…These findings support the hybrid approach as a more sustainable manufacturing alternative with the potential for long-term cost optimization as additive technologies mature.…”
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Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review by Seyed Abolfazl Aghili, Amin Haji Mohammad Rezaei, Mohammadsoroush Tafazzoli, Mostafa Khanzadi, Morteza Rahbar

“…Rather than focusing on abstract applications of machine learning models, this study underlines their applicability in HVAC systems, bridging the science–practice gap. …”
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An experimental analysis of mechanical properties for a dissimilar pattern structure in the 3-D printing of a PLA5F filament using the Taguchi method by Thamizh Selvan S, Mohandass M

“…Many automobile components and machine parts can be fabricated using the Fused Deposition Modeling (FDM) process with materials such as Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PET-G), and polymeric composite materials (e.g., PLA with carbon fiber, PLA with glass fiber). …”
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Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments by Jianqiang He, Yonglin Jia, Yi Li, Asim Biswas, Hao Feng, Qiang Yu, Shufang Wu, Guang Yang, Kadambot.H.M. Siddique

“…Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. …”
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The Identification and Analysis of Novel Umami Peptides in Lager Beer and Their Multidimensional Effects on the Sensory Attributes of the Beer Body by Yashuai Wu, Ruiyang Yin, Liyun Guo, Yumei Song, Xiuli He, Mingtao Huang, Yi Ren, Xian Zhong, Dongrui Zhao, Jinchen Li, Mengyao Liu, Jinyuan Sun, Mingquan Huang, Baoguo Sun

“…The peptides were characterized by LC-MS/MS combined with de novo sequencing, and 906 valid sequences were obtained. Machine-learning models (UMPred-FRL, Tastepeptides-Meta, and Umami-MRNN) predicted 76 potential umami peptides. …”
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Experimental Investigation of Material Removal Rate Parameters in ECM for Aluminum Hybrid Matrix Composites Using the RSM Technique by M. Naga Swapna Sri, P. Anusha, Vittel Rao Rajendranrao Krishnajirao, Manickam Selvaraj, B. Muthuvel, N. Karthikeyan

“…The ANOVA result reveals that the feed rate of electrode is the highest contributing parameter, trailed by the electrolyte discharge rate and other process parameters for MRR and SR. A linear model of regression and interaction plots is also included to show the relationship between the parameters. …”
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Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory by Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery

“…During the model development stage, the optimal variables were determined successfully via heatmaps for precise assessment of ETo in both stations. …”
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Pest classification: Explainable few-shot learning vs. convolutional neural networks vs. transfer learning by Nitiyaa Ragu, Jason Teo

“…Accurate and automated detection of crop insect pests is crucial for effective pest control and optimal utilization of agricultural resources. This study addresses the problem of limited datasets in pest detection by exploring the potential of Explainable Few-Shot Learning (FSL), a machine learning approach that not only enables learning from a small amount of data but also provides interpretable insights into the decision-making process. …”
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Novel post-translational modification learning signature reveals B4GALT2 as an immune exclusion regulator in lung adenocarcinoma by Ge Zhang, Zhenfa Zhang, Lianmin Zhang, Guangyao Zhou, Pengpeng Zhang, Dingli Wang, Shuai Jiang

“…The identification of B4GALT2 as a previously unrecognized oncogenic factor involved in immune exclusion presents a novel therapeutic avenue for LUAD treatment and immunotherapy optimization.…”
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