Hardware-Accelerated Infrared Small Target Recognition Based on Energy-Weighted Local Uncertainty Measure by Xiaoqing Wang, Zhantao Zhang, Yujie Jiang, Kuanhao Liu, Yafei Li, Xuri Yao, Zixu Huang, Wei Zheng, Jingqi Zhang, Fu Zheng

“…Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. …”
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DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View by Caixia Liu, Minhong Zhu, Haisheng Li, Xiulan Wei, Jiulin Liang, Qianwen Yao

“…However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shapes directly from 3D scans remains challenging, as previous reconstruction methods tend to lose details. …”
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Investigating effect of cold plasma pretreatment on drying kinetics, bioactive compounds, and antibacterial properties of ginger slices by Nattapong Chanchula, Kanoktip Pansuksan, Atipong Bootchanont, Chakkaphan Wattanawikkam, Dheerawan Boonyawan, Porramain Porjai

“…The modified Overhults model best described drying kinetics, exhibiting the highest regression coefficient, lowest chi-square, root mean square errors, and akaike information criterion. Cold plasma pretreatment also significantly enhanced total phenolic and flavonoid contents by 63.87 % and 59.5 %, respectively, with optimal results at CPJ treatments (30 and 15 s) at 70 °C. …”
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Estimating small area population from health intervention campaign surveys and partially observed settlement data by Chibuzor Christopher Nnanatu, Amy Bonnie, Josiah Joseph, Ortis Yankey, Duygu Cihan, Assane Gadiaga, Hal Voepel, Thomas Abbott, Heather R. Chamberlain, Mercedita Tia, Marielle Sander, Justin Davis, Attila N. Lazar, Andrew J. Tatem

“…Reductions in relative error rates were 32–73% in a simulation study, and ~32% when applied to malaria survey data in Papua New Guinea. …”
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Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm by Rolla Almodfer, Mohammed Mudhsh, Samah Alshathri, Laith Abualigah, Laith Abualigah, Mohamed Abd Elaziz, Mohamed Abd Elaziz, Mohamed Abd Elaziz, Khurram Shahzad, Mohamed Issa

“…In the presented method, the minimal value of the sum square error (SSE) is applied to determine the optimal fitness function. …”
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Improving the Flexibility of Coal-Fired Power Units by Dynamic Cold-End Optimization by Yanpeng Zhang, Xinzhen Fang, Zihan Kong, Zijiang Yang, Jinxu Lao, Wei Zheng, Lingkai Zhu, Jiwei Song

“…The model validation results show that the error between the simulated results and measured values is <3% at the common load range and <5% at the low load range. …”
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An Over-the-Air Multi-User Convolutional Code for URLLC by Rafael Santos, Daniel Castanheira, Adao Silva, Atilio Gameiro

“…These applications typically involve the exchange of small information blocks. …”
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Evaluation and analysis of the precipitable water vapor in Inner Mongolia of China by Qi Bai, Qiaoli Kong, Xiaolong Mi, Wu Chen, Junsheng Ding, Yunqing Huang, Meiqi Li, Qian Li

“…This study aims to comprehensively evaluate and analyze the PWV in Inner Mongolia using the global navigation satellite system (GNSS), radiosonde (RS), the fifth-generation European Center for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) data. The comparison between GNSS PWV and RS PWV reveals an average bias of −0.68 mm and a root mean square error (RMSE) of 2.17 mm, indicating the high accuracy of GNSS PWV and its potential as an assessment tool of other PWV products. …”
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Plasmonic Stripes in Aqueous Environment Co-Integrated With Si3N4 Photonics by George Dabos, Dimitra Ketzaki, Athanasios Manolis, Laurent Markey, Jean Claude Weeber, Alain Dereux, Anna Lena Giesecke, Caroline Porschatis, Bartos Chmielak, Dimitris Tsiokos, Nikos Pleros

“…An interface insertion loss of 2.3 &#x00B1; 0.3&#x00A0;dB and a plasmonic propagation length ( <inline-formula><tex-math notation="LaTeX">$L_{{\rm{spp}}}$</tex-math></inline-formula>) of 75&#x00A0;<italic>&#x03BC; </italic>m have been experimentally measured at 1.55&#x00A0;<italic>&#x03BC;</italic>m for a VO of 400&#x00A0;nm and an LO of 500&#x00A0;nm, with simulation results suggesting high tolerance to VO and LO misalignment errors. The proposed integration approach enables seamless co-integration of plasmonic stripes, in aqueous environment, with a low-loss and low-cost LPCVD-based Si₃N₄ waveguide platform, revealing its strong potential for future employment in biochemical sensing applications.…”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…For soluble solid content, the best model was 1st-D+GA-BP, with a coefficient of determination (R2) of 0.903 and a root mean square error (RMSE) of 1.731, indicating high accuracy in predicting kiwifruit SSC, and reflecting the potential relationship between spectral data and SSC. …”
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Blockage detection in pipelines based on linear frequency modulated acoustic reflection method by Haiyuan Yao, Dan Li, Yan Li, Bo Yang, Yang Meng

“…Validated via controlled experiments simulating industrial-scale conditions, the system achieved millimeter-level positioning accuracy (error <1 %) across varying blockage intensities (20–100 %), representing a 40 % precision improvement over conventional methods. …”
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Design of annular-shaped heater for measurement of anisotropic thermal conductivity using 3-ω method by Doheon Koo, Junyoung Park, Mugyeom Jung, Sangyeun Park, Hye Seok Na, Wondo Kim, Hyun Sung Park, Hongyun So

“…Consequently, it was confirmed that the thermal conductivity could be measured with a maximum error of 5% for the isotropic thermal conductivity and that of 8% for the anisotropic thermal conductivity when the anisotropic ratio was 2. …”
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Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution by Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder, Jeremy C. Smith

“…The ML models developed in the present study showed accurate predictions with high determination coefficient (R2) and low root mean square error (RMSE) values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO2) is discussed. …”
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Machine learning assisted design of low-carbon aluminosilicate cementitious composites with diverse raw materials and target mechanical strength by Jinyang Jiang, Yi Liu, Junlin Lin, Tao Yang, Fengjuan Wang

“…Despite great efforts have been drawn for decades on exploring various low-carbon raw materials, there remain significant challenges in the universal and precise design of cementitious composites satisfying desired performance without abundant trial-and-error. This study proposes a machine learning assisted design framework for the aluminosilicate cementitious composites based on the data extracted from hundreds of relevant literatures in recent five years. …”
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Multivariate Method Based on Raman Spectroscopy for Quantification of Dipyrone in Oral Solutions by Luciana Lopes Guimarães, Letícia Parada Moreira, Bárbara Faria Lourenço, Walber Toma, Renato Amaro Zângaro, Marcos Tadeu Tavares Pacheco, Landulfo Silveira

“…This spectral model was then used to predict the concentration of dipyrone in commercial formulations from distinct brands with 500 mg/mL. A prediction error of 6.5 mg/mL (1.3%) was found for this PCR model using the diluted samples. …”
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First-passage approach to optimizing perturbations for improved training of machine learning models by Sagi Meir, Tommer D Keidar, Shlomi Reuveni, Barak Hirshberg

“…However, the design of such perturbations is usually done ad hoc by intuition and trial and error. To rationally optimize training protocols, we frame them as first-passage processes and consider their response to perturbations. …”
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Predicting the Onset Date of Cucumber Powdery Mildew Based on Growing Degree Days and Leaf Wetness Duration in Greenhouse Environment by Min Son, Haejun Jeong, Jin-Yong Jung, Jiwon Park, Jiyoon Park, Hoyoung Park, Jonghan Yoon, Se-Hoon Jung, Chun-Bo Sim, Kwang-Hyung Kim, Sook-Young Park

“…As a result, we successfully simulated the symptom onset date with a margin of error of 5.5 days across two validation trials in 2023 and 2024. …”
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High-Level Synthesis: Productivity, Performance, and Software Constraints by Yun Liang, Kyle Rupnow, Yinan Li, Dongbo Min, Minh N. Do, Deming Chen

“…FPGAs are an attractive platform for applications with high computation demand and low energy consumption requirements. …”
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Tidal Volume Monitoring via Surface Motions of the Upper Body—A Pilot Study of an Artificial Intelligence Approach by Bernhard Laufer, Tamer Abdulbaki Alshirbaji, Paul David Docherty, Nour Aldeen Jalal, Sabine Krueger-Ziolek, Knut Moeller

“…The results showed that the linear regression approach, after individual calibration, could be used in clinical applications for 13/16 subjects (mean absolute error < 150 mL), while the CNN approach achieved this accuracy in 5/16 subjects. …”
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Non-Invasive and Long-Term Electrophysiological Monitoring Sensors for Cerebral Organoids Differentiation by Yan Jin, Yixun Guo, Qiushi Li, Lei Wu, Yuqing Ge, Jianlong Zhao

“…Understanding the electrophysiological properties of these organoids is crucial for evaluating their functional maturity and potential applications. However, the differentiation and maturation of stem cells into cerebral organoids is a long, slow, and error-prone process. …”
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