Unraveling overestimated exposure risks through hourly ozone retrievals from next-generation geostationary satellites by Siwei Li, Ge Song, Jia Xing, Jiaxin Dong, Maolin Zhang, Chunying Fan, Shiyao Meng, Jie Yang, Lechao Dong, Wei Gong

“…Here, we utilize a next-generation geostationary satellite with ultraviolet capabilities to retrieve hourly O3 concentrations, achieving high accuracy (R2 = 0.94) and improving daily maximum 8-hour estimates, particularly in semi-urban areas (R2 + 0.10, error reduction >7 μg/m³). Our analysis reveals a 30% drop in O3-related health risks compared to traditional polar-orbit estimates, with the greatest impact in semi-urban and rural areas where satellite data plays an important role due to the lack of ground measurements. …”
Get full text

Landcover-specific calibration of the optical trapezoid model (OPTRAM) for soil moisture monitoring in the Central Valley, California by Neda Mohamadzadeh, Morteza Sadeghi, Noemi Vergopolan, Lan Liang, Uditha Bandara, Craig Altare, Marcellus M. Caldas

“…In this study, we explored to what extent a landcover-specific calibration of OPTRAM can improve its accuracy. In this analysis, we used Sentinel-2 (S2) reflectance and the Cropland Data Layer (CDL) landcover datasets via the Google Earth Engine to generate 20-m resolution soil moisture maps for California’s Central Valley (CV). …”
Get full text

Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…The model also reduces the error rate to 0.48%, demonstrating substantial improvements in diagnostic performance, including increased accuracy, sensitivity, and specificity. …”
Get full text

Energy efficiency and greenhouse gas emissions: A panel ARDL model of top five emitters in Africa by Abayomi Samuel Oyekale

“…The panel Autoregressive Distributed Lag (ARDL) model was used for data analysis. The results showed that all the included series were stationary at first difference. …”
Get full text

Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
Get full text

The colours of the ocean: using multispectral satellite imagery to estimate sea surface temperature and salinity on global coastal areas, the Gulf of Mexico and the UK by Solomon White, Tiago Silva, Laurent O. Amoudry, Evangelos Spyrakos, Adrien Martin, Adrien Martin, Encarni Medina-Lopez

“…Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. …”
Get full text

A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction by Christopher Nielsen, Matthias Wilms, Nils D. Forkert

“…Traditionally, machine learning predictions of biological retinal age utilize convolutional neural network (CNN) architectures and data from color fundus photography (CFP). Despite being previously unexplored, the multimodal fusion of two-dimensional CFP with three-dimensional optical coherence tomography (OCT) data has significant potential to enhance retinal age prediction accuracy and the diagnostic utility of the RAG biomarker. …”
Get full text

Enhanced quantitation of pathological α-synuclein in patient biospecimens by RT-QuIC seed amplification assays. by Ankit Srivastava, Qinlu Wang, Christina D Orrù, Manel Fernandez, Yaroslau Compta, Bernardino Ghetti, Gianluigi Zanusso, Wen-Quan Zou, Byron Caughey, Catherine A A Beauchemin

“…We used end-point dilution (ED) RT-QuIC assays to determine αSynD seed concentrations in patient biospecimens and tested the influence of various assay variables such as serial dilution factor, replicate number and data processing methods. The use of 2-fold versus 10-fold dilution factors and 12 versus 4 replicate reactions per dilution reduced ED-RT-QuIC assay error by as much as 70%. …”
Get full text

Paediatric norms for photopic electroretinogram testing based on a large cohort of Chinese preschool children by Henry Ho-lung Chan, Sonia Seen-hang Chan, Kai Yip Choi

“…This study aimed to establish a reference data set of photopic electroretinogram (ERG) of Chinese preschool children in Hong Kong to facilitate clinical and research studies.Methods and analysis Preschool children aged 3–7 years with normal vision were recruited from local kindergartens. …”
Get full text

AI-driven design: powered by large language model and intelligent computation by Guodong Sa, Zhinan Li, Zhenyu Liu, Jianrong Tan

“…This paper conducts comprehensive analysis of the underlying causes of these challenges and proposes a knowledge-generation-simulation integrated intelligent design ecosystem as a development pathway. …”
Get full text

Evaluation of the Possibility of Calculating LD50 and LD10 Using a Modified Script in the R Environment by P. V. Shadrin

“…The MS-derived LD50 values were within the confidence limits for the values obtained using the PM (P=0.95). The regression analysis confirmed the accuracy of the MS-based LD50 and LD10 calculations, which was demonstrated by a statistically insignificant systematic error, a significant dose dependence at P=0.999, and a high coefficient of determination (R2). …”
Get full text

Orthogonal Frequency Division Multiplexing With Generalized Joint Index Modulation by Zeng Hu, Yanke Che, Jing Wang, Xuhong Peng, Chaobo He, Dehuan Wan, Yun Liu, Ting Wu

Get full text

Impact of snow thermal conductivity schemes on pan-Arctic permafrost dynamics in the Community Land Model version 5.0 by A. Damseaux, A. Damseaux, A. Damseaux, H. Matthes, V. R. Dutch, V. R. Dutch, L. Wake, N. Rutter

“…Evaluation using two Arctic-wide soil temperature observation datasets reveals that the new snow thermal conductivity scheme reduces the cold-soil temperature bias (root-mean-square error, RMSE <span class="inline-formula">=</span> 3.17 to 2.4 <span class="inline-formula">°C</span>, using remote sensing data; RMSE <span class="inline-formula">=</span> 3.9 to 2.19 <span class="inline-formula">°C</span>, using in situ data), demonstrates robustness through sensitivity analysis under lower tundra snow densities, and addresses the overestimation of permafrost extent in the default CLM5.0. …”
Get full text

Assessment of Quality of Life in Cervical Cancer Patients at a Tertiary Care Centre by Koppolu Keerthana, Savolu Cynthia Subhaprada

“…The total score of FACT-Cx questionnaire ranges from 0 to 168, with higher score indicating better QoL. Data on socio-demographic profile, stage and grade of cancer and treatment modalities were collected and subjected to quantitative analysis using SPSS Version 21. …”
Get full text

Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain by Manish Kumar, Aneesh Sreevallabh Chivukula, Gunjan Barua

“…Security analysis results are shown (such as histogram plots in Figs. 11–14 and correlation plots in Figs. 19–21). …”
Get full text

Internet of Things-Cloud Control of a Robotic Cell Based on Inverse Kinematics, Hardware-in-the-Loop, Digital Twin, and Industry 4.0/5.0 by Dan Ionescu, Adrian Filipescu, Georgian Simion, Adriana Filipescu

“…A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. …”
Get full text

Physics‐Guided Deep Learning Model for Daily Groundwater Table Maps Estimation Using Passive Surface‐Wave Dispersion by José Cunha Teixeira, Ludovic Bodet, Agnès Rivière, Amélie Hallier, Alexandrine Gesret, Marine Dangeard, Amine Dhemaied, Joséphine Boisson Gaboriau

“…Results show a coefficient of determination (R2) of 80% at the training piezometer and of 68% at the test piezometer, and a remarkably low root‐mean‐square error (RMSE) of 0.03 m at both locations. These findings highlight the potential of deep learning to estimate GWT maps from seismic data with spatially limited piezometric information, offering a practical and efficient solution for monitoring groundwater dynamics across large spatial extents.…”
Get full text

A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting by Jiawen Li, Binfan Lin, Peixian Wang, Yanmei Chen, Xianxian Zeng, Xin Liu, Rongjun Chen

“…As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R²) increase of 22% and 24%, respectively. …”
Get full text

Simulation and Comparative Study of Interaction Between Ray and Medium in Borehole Based on FLUKA Software by LIU Juntao, LI Wenxin, MARINA R. ABDELNOUR, TIAN Heng, WANG Yiwei, GAO Yuan, LIU Zhiyi

“…Results demonstrate that for low-energy gamma transport, the maximum relative error between FLUKA and MCNP in formation-scattered gamma-ray spectra is 5.37%, with density response errors below 3.75%. …”
Get full text

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

“…A comparison of the results of the models indicated that the LS-SVM model is more sensitive to changes in input parameters than the M5 model, such that the decision tree model, unlike the least squares support vector machine model, provided acceptable results in all scenarios.Conclusions: In summary, the comparison of the models used suggests that the appropriate selection of climatic parameters and the examination and analysis of data have a significant impact on the accuracy of predictions.…”
Get full text