Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods by Hongran Li, Nuo Wang, Zixuan Du, Deyu Huang, Mengjie Shi, Zhaoman Zhong, Dongqing Yuan

“…To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. …”
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Determinants and spatial patterns of solid fuel use in East Africa based on demographic and health survey data from 2012 to 2023 by Gelila Yitageasu, Amensisa Hailu Tesfaye, Eshetu Abera Worede, Tigist Kifle, Mitkie Tigabie, Zemichael Gizaw, Helen Brhan, Lidetu Demoze

“…We analyzed data from 218,282 households across 12 East African countries using Demographic and Health Survey (2012–2023) data. …”
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Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning by Keran Li, Yan Lin

“…Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. …”
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Himawari sea surface temperature data reveal regular internal wave activity producing cooling in the northern South China Sea by Xiaoyan Dang, Yi Sui, Danling Tang, Jiujuan Wang, Shengli Chen

“…To address the problems of short observation time, limited range based on measured data, and low accuracy based on mesoscale satellite data for the study of IW-induced sea surface temperature (SST) change, this paper introduce high-frequency geostationary orbit satellite data combined with SST data of different times and analyze and discuss the changes and mechanisms of immediate and long-term spatio-temporal SST distributions in the northern South China Sea (SCS) caused by IWs. …”
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A case study on the scaling-up of double fortified salt through the public distribution system of a food security program in Uttar Pradesh, India: experiences, challenges, and achi... by Meena H Jadhav, M G Venkatesh Mannar, Annie S Wesley

“…# Background Iron deficiency anemia (IDA) exerts an enormous public health burden in low-income countries, primarily affecting women and children. …”
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Spatial, temporal and demographic distribution characteristics of adenomyosis symptom clusters from the perspective of traditional Chinese medicine: a multicenter cross-sectional s... by Xin Wang, Yu Tao, YuDan Fu, XinChun Yang, RuiHua Zhao

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Blockchain-Enabled Cross-Chain Coordinated Trading Strategy for Electricity-Carbon-Green Certificate in Virtual Power Plants: Multi-Market Coupling and Low-Carbon Operation Optimiz... by Chao Zheng, Wei Huang, Suwei Zhai, Kaiyan Pan, Xuehao He, Xiaojie Liu, Shi Su, Cong Shen, Qian Ai

“…First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. …”
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An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data by Huan Tao, Ziyang Li, Shengdong Nie, Hengkai Li, Dan Zhao

“…Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. …”
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Extending GLUE With Multilevel Methods to Accelerate Statistical Inversion of Hydrological Models by Max Gustav Rudolph, Thomas Wöhling, Thorsten Wagener, Andreas Hartmann

“…Abstract Inverse problems aim at determining model parameters that produce observed data to subsequently understand, predict or manage hydrological or other environmental systems. …”
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High-precision measurements of the atomic mass and electron-capture decay Q value of 95Tc by Zhuang Ge, Tommi Eronen, Vasile Alin Sevestrean, Ovidiu Niţescu, Sabin Stoica, Marlom Ramalho, Jouni Suhonen, Antoine de Roubin, Dmitrii Nesterenko, Anu Kankainen, Pauline Ascher, Samuel Ayet San Andres, Olga Beliuskina, Pierre Delahaye, Mathieu Flayol, Mathias Gerbaux, Stéphane Grévy, Marjut Hukkanen, Arthur Jaries, Ari Jokinen, Audric Husson, Daid Kahl, Joel Kostensalo, Jenni Kotila, Iain Moore, Stylianos Nikas, Marek Stryjczyk, Ville Virtanen

“…Combined with the nuclear energy-level data for the decay-daughter 95Mo, two potential ultra-low Q-value transitions are identified for future long-term neutrino-mass determination experiments. …”
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Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…However, traditional PV power forecasting models designed for distributed PV power stations often struggle with accuracy due to unpredictable meteorological variations, data noise, non-stationary signals, and human-induced data collection errors. …”
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Review and Evaluation of Slip-ratio-based Void Fraction Prediction Models by HE Wen, HAN Jinyu, ZHAO Chenru, LI Yanlin, BO Hanliang

“…The gas phase content is relatively low, and the velocities of two phases may be unevenly distributed on the cross-section of the channel, resulting in the low accuracy of the slip-ratio-based models. …”

Modeling of peatland fire risk early warning based on water dynamics by B. Kartiwa, . Maswar, A. Dariah, . Suratman, N.L. Nurida, N. Heryani, P. Rejekiningrum, H. Sosiawan, S.H. Adi, I. Lenin, S. Nurzakiah, Ch. Tafakresnanto

“…Afterward, this soil moisture criterion was transferred into precipitation value to develop a peat fire early warning model for estimating the days left before a high peat fire risk status was attained based on the latest daily rainfall rates.CONCLUSION: This study has developed a simple peat fire early warning model using daily precipitation data. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…[Results] The residual error of the model were 3.6-3.7 days, and the correlation coefficient were 0.97, so the models had high reliability; The model was further verified with blind test data set of two-year's phenological ecological characteristics, and the correlation coefficient was between 0.98-0.99. …”
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Comparison of methods of optimal cut-point selection for biomarkers in diagnostic medicine: a simulation study with application of clinical data in health informatics by Mojtaba Hassanzad, Karimollah Hajian-Tilaki, Zinatossadat Bouzari, Shahla Yazdani

“…The IU yielded more precise findings than the Youden for moderate and low AUC in binormal pairs, but its performance was lower with skewed distributions. …”
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Identification and interpretation of nonnormality in atmospheric time series by Cristian Proistosescu, Andrew Rhines, Peter Huybers

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Climate change, socioeconomic, environmental, and political drivers of road traffic fatalities in Somalia: a multivariate time series analysis by Ahmed Awale Ahmed, Abdikadir Abdi Ali, Abdisalan Sh. Yousuf Duale, Abdirashid M. Yousuf, Abdisalam Hassan Muse

“…This study investigates the impact of climate change (temperature, rainfall), environmental degradation (CO2 emissions), socioeconomic conditions (urban population, foreign aid), and political instability on RTFs in Somalia, aiming to contribute to the limited understanding of these dynamics in fragile states.MethodsA multivariate time series analysis was conducted using data from 1984 to 2021. An Autoregressive Distributed Lag (ARDL) model was employed to examine the short-run and long-run effects of the selected drivers on RTFs. …”
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Experimental and Numerical Modelling of Nonlinear Flow Behavior in Single Fractured Granite by Xiaopeng Su, Lei Zhou, Honglian Li, Binwei Xia, Zhonghui Shen, Yiyu Lu

“…A simplified Hertz contact model was used to fit the experimental data. The model uses the reciprocal of the standard deviation of the aperture as the average curvature radius of the fracture. …”
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Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE by Jumanto Unjung, Rofik Rofik, Endang Sugiharti, Alamsyah Alamsyah, Riza Arifudin, Budi Prasetiyo, Much Aziz Muslim

“…These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. …”
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Wind-Concerned Sea Ice Detection and Concentration Retrieval From GNSS-R Data Using a Modified Convolutional Neural Network by Wei Ban, Linhu Zhang, Xiaohong Zhang, Han Nie, Xiaoli Chen, Xuejing Chen

“…In summary, the comparison of WCNN SIC retrieval performance across varying wind speeds demonstrates that incorporating wind speed data into the WCNN model significantly reduces the misclassification of seawater as sea ice in low wind conditions (0–10 m/s) and lowers the misclassification of sea ice as seawater in high wind conditions (10–20 m/s). …”
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