Integrated soil moisture fusion for enhanced agricultural drought monitoring in China by Aifeng Lv, Xianglei Yang, Wenxiang Zhang, Yan Han

Get full text

LSTM time series prediction of soil moisture content in kiwifruit root zone based on meteorological data fusion by Jingyuan He, Weifeng Li, Shijia Pan, Nikolaos Sygrimis, Zijie Niu, Dongyan Zhang, Dong Han, Petro A. Roussos

“…On this basis, combined with the Shapley additive explanations (SHAP) interpretable analysis method, this study systematically quantified the key influences on SMC prediction accuracy. …”
Get full text

A Green Cloud-Based Framework for Energy-Efficient Task Scheduling Using Carbon Intensity Data for Heterogeneous Cloud Servers by B. M. Beena, Prashanth Cheluvasai Ranga, Thotapalli Sri Surya Manideep, Sneha Saragadam, Garikipati Karthik

Get full text

Optimising dynamic treatment regimens using sequential multiple assignment randomised trials data with missing data by Jessica Xu, Anurika P. De Silva, Katherine J. Lee, Robert K. Mahar, Julie A. Simpson

“…Resulting empirical standard errors were lower or similar for MI compared to CCA under all missing data scenarios. …”
Get full text

DisCo P-ad: Distance-Correlation-Based <i><b>p</b></i>-Value Adjustment Enhances Multiple Testing Corrections for Metabolomics by Debmalya Nandy, Debashis Ghosh, Katerina Kechris

Get full text

Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

Get full text

Audit Quality Measurement Criteria by Hamidreza Rezaei, Mahdi Moradi, Mohammad Ali Bagherpour Velashani, Mahdi Jabbari noghabi

“…To achieve the goal of the research, 18 members of public accountants, financial managers, investors, and bank facility managers were interviewed, and the foundation data method and thematic analysis were used to collect, classify, and analyze the collected data. …”
Get full text

Electrical impedance-based tissue classification for bladder tumor differentiation by Carina Veil, Franziska Krauß, Bastian Amend, Falko Fend, Oliver Sawodny

Get full text

Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error by Simon Samuroff, Andresa Campos, Anna Porredon, Jonathan Blazek

“…Our findings suggest that IA model error can manifest itself as internal tension between $\xi_\pm$ and $\gamma_t + w$ data vectors. …”
Get full text

A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis by Ji Li, Xiangyu Yan, Xingjie Chu, Ying Zhang, Guoliang Liu, Lin Li, Yue Li, Xiaochun Dong, Zihan Mei, Zhengkun Liu, Jinyue Yuan, Xiaohan Sun, Chunxia Cao

“…Meanwhile, the prediction of ILI% after dividing the epidemic and nonepidemic seasons (mean absolute percentage error [MAPE]=10.730%, mean square error [MSE]=0.884, mean absolute error [MAE]=0.649, root-mean-square error [RMSE]=0.940, and R2R2R2 ConclusionsThis study shows strong potential for influenza prediction by combining Baidu index data with traditional surveillance and specific keywords for epidemic and nonepidemic seasons. …”
Get full text

Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study by Anwar Fitrianto, Jap Ee Jia, Budi Susetyo, La Ode Abdul Rahman

“…The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. …”
Get full text

DESIGN OF AN IMPROVED MODEL FOR CARDIOVASCULAR DISEASE DETECTION USING DEEP CANONICAL CORRELATION ANALYSIS AND BIOINSPIRED OPTIMIZATION by Prakash Chandra Sahoo, Binod Kumar Pattanayak, Rajani Kanta Mohanty, Ayasa Kanta Mohanty

“…On this, we propose a novel framework for the detection of cardiovascular diseases and presiding analysis through multimodal data fusion, optimized neural networks, and explainable AI techniques. …”
Get full text

Evaluating time-lagged relationships between groundwater storage and river discharge using GRACE-based data: insights from the Potomac Basin by AJ Villaruel, Alimatou Seck, Cherie Schultz

“…As the primary drinking water source for the Washington Metropolitan Area (WMA), effective management of the Potomac River’s resources is critical, especially in the context of climate change, with the expected increase in severity and frequency of extreme events. Our analysis integrates 22 years of data, including GRACE-based groundwater storage (GWS) index estimates, river discharge (Q) measurements, and meteorological records, to investigate trends and predictive relationships between past GWS, as determined by the GRACE-based drought index, and streamflow. …”
Get full text

Analysis of Factors Affecting Economic Growth in Bangka Belitung Province, Indonesia with LSDV And FGLS Methods by Darman Saputra

Get full text

Comparative analysis of three key atmospheric correction techniques for bathymetric mapping in nearshore areas with Sentinel-2 Data - case study: Kish Island, Persian Gulf by Keivan Kabiri

Get full text

Snow Depth Retrieval Using Sentinel-1 Radar Data: A Comparative Analysis of Random Forest and Support Vector Machine Models with Simulated Annealing Optimization by Yurong Cui, Sixuan Chen, Guiquan Mo, Dabin Ji, Lansong Lv, Juan Fu

Get full text

A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India by Dolon Banerjee, Sayantan Ganguly, Wen-Ping Tsai

Get full text

Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…For the 3-day forecasting interval, Exponential Gaussian Process Regression (Exponential GPR) marginally outperformed Long Short-Term Memory (LSTM), with Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values of 15.84, 547.04, and 23.39, respectively. …”
Get full text

Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method. by Yang Zhang, Liang Zhao

“…The application of the Lyapunov method ensures that all signals within the closed-loop system remain semi-globally uniformly bounded, significantly reducing tracking errors. Moreover, we introduce the use of Long Short-Term Memory (LSTM) networks for predictive analysis of state data, which further confirms the robustness and effectiveness of our control method through extensive simulations. …”
Get full text

On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data by Kai-en Yang, Shuo Li, Guangtao Duan, Mikio Sakai

“…Specifically, a feasibility index is proposed based on posterior error analysis. It is demonstrated that when the training data is determined under the proposed feasibility index [Formula: see text] 2, the consistency of granular dynamics between SM and the high-fidelity model can be guaranteed. …”
Get full text