Indoor and Ambient Air Pollution in Chennai, India during COVID-19 Lockdown: An Affordable Sensors Study by Naveen Puttaswamy, V. Sreekanth, Ajay Pillarisetti, Adithi R. Upadhya, Sudhakar Saidam, Balachandar Veerappan, Krishnendu Mukhopadhyay, Sankar Sambandam, Ronak Sutaria, Kalpana Balakrishnan

“…Field calibration coefficients for the atmos PM were derived by collocating them with reference-grade PM monitors. The normalized root mean square error (NRMSE) of the atmos hourly PM2.5 (PM10) improved from 41% to 15% (33% to 18%) after applying the field calibration coefficients. …”
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Impact of national and supranational policies on social inequalities in major NCDs and related risk factors in Europe: a scoping review protocol by Mall Leinsalu, Rainer Reile, Norun Hjertager Krog, Digo Chakraverty, Gerd Monika Flodgren, Julia Doetsch, Trude Robsahm, Astri Syse, Kadri Tammur, Julia Waldhauer

“…We will search 13 databases from 2008 up to present for eligible experimental or quasi-experimental studies in any language, and reference lists of relevant reviews and included studies. …”
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Low-cost phone-based LiDAR scanning technology provides sub-centimeter accuracy when measuring the main dimensions of motor-manual tree felling cuts by Stelian Alexandru Borz, Andrea Rosario Proto

“…By the commonly used error metrics such as the bias (−0.73–0.10), mean absolute error (0.51–0.78) and root mean squared error (0.68–0.92), the differences between the two were in the sub-centimeter domain. …”
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A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor by Shaikh Abdur Razzak, Nahid Sultana, S.M. Zakir Hossain, Muhammad Muhitur Rahman, Yue Yuan, Mohammad Mozahar Hossain, Jesse Zhu

“…The coefficient of determination (R2), residual, mean absolute error (MAE), root mean square error (RMSE), and fractional bias (FB) were used to analyze BOA-SVR model performance. …”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…We calculated the mean deviation, mean absolute error, and root mean square error of these data. Using these metrics, we analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. …”
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Experiments on Improving Temperature and Humidity Profile Retrieval for Ground-based Microwave Radiometer by Zhang Xuefen, Wang Zhicheng, Mao Jiajia, Wang Zhangwei, Zhang Dongming, Tao Fa

“…Taking sounding profiles as reference, root mean square errors (RMSE) of retrieval results of 4 models are 2.3℃, 2.3℃, 9%, 16%. …”
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Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar by N. Maherndl, N. Maherndl, A. Battaglia, A. Battaglia, A. Kötsche, M. Maahn

“…Normalized root mean square errors (NRMSEs) are below 25 <span class="inline-formula">%</span> for <span class="inline-formula">IWC&gt;0.1</span> <span class="inline-formula">g m⁻³</span>. …”
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Multi-Source Rainfall Data Assimilation based on Broad Learning System over Yunnan Province by Yuanyuan ZHOU, Xiaohui YANG, Tiangui XIAO

“…The accurate estimation of rainfall is always a topic of concern， given its pivotal role in accurately predicting rainfall-related disasters.This study proposed a multi-source rainfall assimilation technology based on a broad learning system （BLS） to improve the accuracy of rainfall estimation.Yunnan Province， located in China's low-latitude plateau， was chosen as the geographical area of interest to establish a multi-source rainfall assimilation model within this region.In particular， the model utilizes five satellite-derived rainfall datasets （3B42V7， IMERG， GSMaP， CMORPH， PERSIANN） and the latitude and longitude information as the source data， and the ground-based rainfall gauge data serves as the reference data.The time span of all the datasets is from April 2014 to December 2017.A leave-one-year-out cross-validation （LOYOCV） method was applied to verify the performance of the established assimilation model， where statistical indicators including Pearson’s correlation coefficient （CC）， root-mean square error （RMSE）， mean absolute error （MAE）， Nash efficiency coefficient （NSE） and Kling-Gupta efficiency （KGE） were used to quantify the accuracy of assimilation rainfall at different spatiotemporal scales.Concurrently， assimilation models based on support vector machine （SVM） and deep neural network （DNN） were established to highlight the accuracy and efficiency of the BLS， respectively.Additionally， the effectiveness of the latitude and longitude information within the proposed assimilation model was examined.The results show that the daily average statistical index of assimilation rainfall based on BLS is better than that of the other five satellite-based products in LOYOCV.At the temporal scale， the proposed assimilation technique effectively reflects the temporal variations observed in gauge-recorded rainfall.Moreover， it can accurately estimate the rainfall amounts during rainstorms in Yunnan Province throughout 2017.It is worth noting that the rainfall data generated through the BLS method outperforms the CMORPH product （the most accurate one among the five satellite-derived rainfall products） in both rainy and dry seasons （May to October and November to April of next year， respectively）.At the spatial scale， BLS-based rainfall results in most areas of Yunnan Province showed higher CC and NSE as well as smaller RMSE and MAE than the satellite-based products.The evaluation of the assimilation models based on BLS， SVM， and DNN highlights that the BLS exhibits superior functional mapping capabilities compared to SVM and demands fewer computational resources than DNN.It is reasonable to conclude that the multi-source rainfall assimilation approach utilizing the BLS while incorporating latitude and longitude information can enhance the precision of rainfall estimates in Yunnan Province.The proposed method presents practical significance in multi-source rainfall data assimilation.…”
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Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture by Mo Zhang, Yong Ge, Jianghao Wang

“…The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. …”
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Use of NIRS technology for predicting the nutritional value of silage made from tropical grasses enriched with corn ethanol co-products and intercropped with corn by Joadil Gonçalves de Abreu, Daniele Cristina da Silva Kazama, Wender Mateus Peixoto, Edegar Matter, Luciano da Silva Cabral, Ernando Balbinot, Patrícia Orlando Royer, Eduardo André Ferreira

“…The calibration model was selected based on the lowest root mean square error of the cross-validation (RMSECV) and the highest coefficient of determination (R2cv). …”
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Statement on the toxicological properties and maximum residue levels of acetamiprid and its metabolites by EFSA (European Food Safety Authority), Antonio Hernandez‐Jerez, Tamara Coja, Martin Paparella, Anna Price, Jerome Henri, Andreas Focks, Jochem Louisse, Andrea Terron, Marco Binaglia, Irene Munoz Guajardo, Iris Mangas, Irene Munoz Guajardo, Lucien Ferreira, Dimitra Kardassi, Chloe De Lentdecker, Tunde Molnar, Giorgia Vianello

“…Regarding pulses/oilseeds, root crops and cereals, the new data received did not indicate a need to modify the existing residue definition for risk assessment, which therefore remains as parent acetamiprid. …”
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Self-Aligning Finger Exoskeleton for the Mobilization of the Metacarpophalangeal Joint by Emanuele Peperoni, Stefano Laszlo Capitani, Tommaso Fiumalbi, Eugenio Capotorti, Andrea Baldoni, Filippo Dell'Agnello, Ilaria Creatini, Elisa Taglione, Nicola Vitiello, Emilio Trigili, Simona Crea

“…Torque tracking performance shows an RMSE lower than 8 mNm in following sinusoidal reference profiles. The results encourage further investigations of the device in a clinical scenario.…”
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Projected changes and burden of type 2 diabetes in China from 1990 to 2021: an analysis of GBD 2021 data by Junhao MA, Guangqiang MIAO, Guixiao WANG, Shen LI, Liping YANG

“…The mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were used to compare the fitting and prediction performance of the models, and the best model was selected to predict the disease burden of T2DM in China from 2022 to 2026. …”
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Multi-source data joint processing framework for DEM calibration and fusion by Cuilin Yu, Qingsong Wang, Zibo Zhang, Zixuan Zhong, Yusheng Ding, Tao Lai, Haifeng Huang, Peng Shen

“…The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) elevation data serves as the independent reference dataset for assessment. Our experiments, conducted in Guangdong Province, China, and the Northern Territory of Australia, demonstrate that the DemFormer model reduces the root mean square error (RMSE) by 18.38 %, 17.28 %, 54.53 %, and 65.24 % for TanDEM-X, AW3D30, SRTM, and ASTER, respectively. …”
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Design and Simulation of Chinese Cabbage Harvester by Simo Liu, Xuhui Yang, Zhe Zhang, Jianing Xu, Ping Zhao, Subo Tian, Lihua Wei, Xiaofeng Ning

“…This study provides reference to the further design and development of Chinese cabbage harvesters in the future.…”
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Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device by Weixuan Chen, Rafael Cordero, Jessie Lever Taylor, Domenico R. Pangallo, Rosalind W. Picard, Marisa Cruz, Giulia Regalia

“…The primary acceptance threshold was an accuracy root-mean-square (ARMS) ≤3 beats per minute (bpm) or 5 bpm under no-motion and motion conditions, respectively. …”
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Impact of log file source and data frequency on accuracy of log file-based patient specific quality assurance by Akbar Azzi, Gerd Heilemann, Dietmar Georg, Supriyanto Ardjo Pawiro, Terry Mart, Wolfgang Lechner

“…The original treatment plan was used as a reference. Moreover, DVH parameters of D98%, D50%, and D2% of the planning-target volume (PTV) and dose to several organs at risk (OARs) were reported. …”
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Early Study on Visible (Vis) and Short-Wave Infrared (SWIR) Spectroscopy for Assessing Water Content in Olive Fruits: Towards Sustainable Land and Agricultural Practices by Giuseppe Bonifazi, Riccardo Gasbarrone, Davide Gattabria, Eugenio Lendaro, Luciana Mosca, Roberto Mattioli, Silvia Serranti

“…Partial least squares (PLS) regression models for the olive fruit weight, weight loss, and water content were developed while using the dehydration process in a drying oven as the reference analysis. The models demonstrated strong predictive performance, with the PLS model for the olive fruit weight achieving a coefficient of determination in cross-validation (R²_CV) of 0.78 and a root mean squared error (RMSECV) of 0.6 g. …”
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Integrating RS data with fuzzy decision systems for innovative crop water needs assessment by Faezeh Sadat Hashemi, Mohammad Javad Valadan Zoej, Fahimeh Youssefi, Huxiong Li, Sanaz Shafian, Mahdi Farnaghi, Saied Pirasteh

“…The estimated evapotranspiration was compared to Reference Evapotranspiration (ETr) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. …”
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Predicting tree species composition using airborne laser scanning and multispectral data in boreal forests by Jaime Candelas Bielza, Lennart Noordermeer, Erik Næsset, Terje Gobakken, Johannes Breidenbach, Hans Ole Ørka

“…Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r2) of 0.74, 0.79 and 0.51 for Norway spruce (Picea abies (L.) …”
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