LightSTATE: A Generalized Framework for Real-Time Human Activity Detection Using Edge-Based Video Processing and Vision Language Models by Anik Debnath, Yong-Woon Kim, Yung-Cheol Byun

“…The system processes video streams through efficient frame extraction, edge detection and Root Mean Square Error (RMSE)-based frame filtering to identify significant motion events. …”
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A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana. by Eric Nyarko, Edmund Fosu Agyemang, Ebenezer Kwesi Ameho, Louis Agyekum, José María Gutiérrez, Eduardo Alberto Fernandez

“…However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. …”
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Aligning Evapotranspiration from MOD16A2.061 Product to Ground Estimates in Piemonte (NW Italy): an analysis of temporal and spatial biases by E. Ronco, E. C. Borgogno Mondino

“…The corrected dataset (<em>&Ecirc;T</em>₀) shows significantly improved agreement with ground-based ET₀, reducing the Mean Absolute Error from 10.06 mm/8 d to 2.48 mm/8 d, a 75% improvement. …”
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Validation of Enzyme Immunoassay for Preclinical Pharmacokinetic Trials of Rituximab by V. V Pisarev, Maria M Ulyashova, Gelia N Gildeeva

“…The within-run and between-run precision of the assay did not exceed 7.4 %, the total error of the method did not exceed 20.1 %. The linearity of dilution makes it possible to use the assay for the analysis of biological samples with a wide range of rituximab concentrations. …”
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Takagi–Sugeno Fuzzy Nonlinear Control System for Optical Interferometry by Murilo Franco Coradini, Luiz Henrique Vitti Felão, Stephany de Souza Lyra, Marcelo Carvalho Minhoto Teixeira, Claudio Kitano

“…This method has been applied in various fields of scientific research: buck converters, biomedicine, civil engineering, etc. …”
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Translation and Validation of the Premenstrual Assessment Form-Short Form Questionnaire in Hungarian by Olívia Dózsa-Juhász, Alexandra Makai, Viktória Prémusz, Pongrác Ács, Márta Hock

“…During the CFA, the three-factor structure (Affect, Water Retention, and Pain) was supported (root mean-square error approximation [RMSEA] = 0.054; Tucker–Lewis Index = 0.965; Comparative Fit Index = 0.976; χ2 = 48.642; df = 31; p = 0.023; χ2/df = 1.569). …”
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PlantGaussian: Exploring 3D Gaussian splatting for cross-time, cross-scene, and realistic 3D plant visualization and beyond by Peng Shen, Xueyao Jing, Wenzhe Deng, Hanyue Jia, Tingting Wu

“…The mesh results indicate an average relative error of 4% between the calculated values and the true measurements. …”
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5G New Radio for Non-Terrestrial Networks: Analysis and Comparison of HARQ and RLC ARQ Performance Over Satellite Links by Riccardo Tuninato, Gabriel Maiolini Capez, Nicolo Mazzali, Roberto Garello

“…Finally, we provide a detailed comparison and discussion of HARQ and RLC ARQ performance in terms of block error rate, spectral efficiency, and latency. This extensive analysis provides valuable insights for researchers and space agencies interested in applying 5G NR to satellite-based Non-Terrestrial Networks.…”
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Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data by Yunhe Li, Guiying Li, Sirong Wang, Dengsheng Lu

“…Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. …”
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A neural network-based model for cultivating applied talents in the context of industry-education integration by Lei Tong, Jingjing Zhang, Lixiao Yue, Li Chen, Mengxiang Wang

“…Results showed that the optimized model performed well with small datasets (1,000 data points), achieving a Log Loss of 0.106, a Mean Absolute Error (MAE) of 0.057, an Area Under the Precision-Recall Curve (AUC-PR) of 0.896, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.975. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…Empirical evaluations reveal a root mean square error (RMSE) of 0.975 g/cm³ and a coefficient of determination (<italic>R</italic>²) value of 0.0063, underscoring the superior predictive accuracy of this approach compared to conventional methods. …”
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Exploring the impact of noise, language familiarity, and experimental settings on emotion recognition by Terry Amorese, Marialucia Cuciniello, Anna Alterio, Daniele Pepe, Odette Scharenborg, Gennaro Cordasco, Anna Esposito

“…Regarding familiarity with the language, differences in emotion recognition performance between the Italian and Dutch listeners were observed, but the error magnitude was contingent on the emotional categories. …”
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Edge-optimized multimodal cross-fusion architecture for efficient crop disease detection by Thomas Kinyanjui Njoroge, Kelvin Mugoye Shindu, Rachael Kibuku

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Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…Furthermore, it outperforms the second-best comparative model by approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>49%</mn></mrow></semantics></math></inline-formula> in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology.…”
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MONITORING DATA AGGREGATION OF DYNAMIC SYSTEMS USING INFORMATION TECHNOLOGIES by Dmytro Shevchenko, Mykhaylo Ugryumov, Sergii Artiukh

“…Looking at the results in general, the autoencoders work effectively for the dimensionality reduction task and the data recovery quality metric shows that they recover the data well with an error of 3–4 digits after 0. In conclusion, the vanilla autoencoder is the best deep learning model for aggregating monitoring data of dynamic systems. …”
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A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

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Death recollection moderates stress-influenced depression in Thai boarding school students by Justin DeMaranville, Tinakon Wongpakaran, Nahathai Wongpakaran, Danny Wedding

“…The moderation effect was significant: B = 0.133, standard error = 0.061, 95% CI = .253 to .013 after controlling for the meditation frequency of the population. …”
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Comparative Evaluation of Decision Tree (M5) and Least Square Support Vector Machine (LS-SVM) Models for Groundwater Level Prediction in the Mashhad Plain by Vajihe Ramezani saani, Mahdi Zarei, Seyed Morteza Seyedian

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BIM-Based Automatic Extraction of Daily Concrete and Formwork Requirements for Site Work Planning by Van-Hoan Pham, Po-Han Chen, Quan Nguyen, Diep-Thuy Duong

“…Conventionally, quantity takeoff was a manual process based on 2D drawings and human interpretation and was error-prone. Presently, with the popularity of Building Information Modelling (BIM), in BIM-based projects, using inbuilt quantity takeoff functions, quantities of work can be generated automatically from BIM models to aid the quantity takeoff. …”
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Machine-learning aided calibration and analysis of porous media CFD models used for rotating packed beds by Ahmed M. Alatyar, Abdallah S. Berrouk

“…The proposed research is an attempt to advance the state-of-the-art of the numerical modelling of RPB by combining Computational Fluid Dynamics and Machine Learning approaches. …”
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