Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials by Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian

“…A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. …”
Get full text

Recent Trends in the Incidence of Cystic Fibrosis in Paraguay: Analysis of the Period 2018-2023 by Marta Ascurra, Adriana Valenzuela, Marco Casartelli, María Stella Rodríguez, Giovanna Porzio, Mirna Salinas, Anahí Insaurralde, Andrea Nuñez, Adrian Medina-Vera

Get full text

Multi-Source DEM Vertical Accuracy Evaluation of Taklimakan Desert Hinterland Based on ICESat-2 ATL08 and UAV Data by Mingyu Wang, Huoqing Li, Yongqiang Liu, Haojuan Li

“…This study systematically evaluates the vertical accuracy of six open-access DEMs in the hinterland of the Taklimakan Desert using ICESat-2 ATL08 data and unmanned aerial vehicle (UAV) data. Additionally, it examines the relationship between DEM errors and terrain characteristics, including slope, aspect, and terrain relief. …”
Get full text

PRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences by Benjamin Dubois, Mathieu Delitte, Salomé Lengrand, Claude Bragard, Anne Legrève, Frédéric Debode

“…However, Nanopore sequencing data exhibit a particular profile, with a higher error rate compared with Illumina sequencing, and existing bioinformatics pipelines for the analysis of such data are scarce and often insufficient, requiring specialized tools to accurately process long-read sequences.ResultsWe present PRONAME (PROcessing NAnopore MEtabarcoding data), an open-source, user-friendly pipeline optimized for processing raw Nanopore sequencing data. …”
Get full text

Evaluation of Annual Rainfall Erosivity Index Based on Daily, Monthly, and Annual Precipitation Data of Rainfall Station Network in Southern Taiwan by Ming-Hsi Lee, Huan-Hsuan Lin

“…Furthermore, the root mean square error (RMSE) and mean absolute percentage error (MAPE) analysis results show that the estimation models based on annual and monthly precipitation data have a more accurate prediction performance than that based on daily precipitation data.…”
Get full text

Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…Five different machine learning regression models, namely linear regression (LR), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were employed and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) along with R2 for predicting the daily ground-level PM10 concentration using AOD, land cover data, and meteorological parameters. …”
Get full text

Vital Sign and Biochemical Data Collection Using Non-contact Photoplethysmography and the Comestai Mobile Health App: Protocol for an Observational Study by Gianvincenzo Zuccotti, Paolo Osvaldo Agnelli, Lucia Labati, Erika Cordaro, Davide Braghieri, Simone Balconi, Marco Xodo, Fabrizio Losurdo, Cesare Celeste Federico Berra, Roberto Franco Enrico Pedretti, Paolo Fiorina, Sergio Maria De Pasquale, Valeria Calcaterra

“…The outcomes of the study are expected at the end of 2025. The analysis plan involves verifying and validating the parameters collected from mobile devices via the app, reference devices, and prescheduled blood tests, along with patient demographic data. …”
Get full text

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

“…Specifically, the model achieves an <i>R</i>² of 0.9938 for TN, a mean absolute error (<i>MAE</i>) of 0.0728 for DO, a root mean square error (<i>RMSE</i>) of 0.3881 for total TSS, and a mean absolute percentage error (<i>MAPE</i>) as low as 0.2568% for Chla. …”
Get full text

Assessing Building Seismic Exposure Using Geospatial Technologies in Data-Scarce Environments: Case Study of San José, Costa Rica by Javier Rodríguez-Saiz, Beatriz González-Rodrigo, Juan Gregorio Rejas-Ayuga, Diego A. Hidalgo-Leiva, Miguel Marchamalo-Sacristán

“…This approach is essential for quick pre- or post-disaster seismic risk assessment, allowing time and cost-effective data collection and analysis. This contribution is particularly relevant for Central America and other seismically active regions with limited data, supporting improved risk analysis and urban resilience planning.…”
Get full text

Mixed-methods evaluation of acceptability of the District Health Information Software (DHIS2) for neglected tropical diseases program data in Cameroon by Henri C Moungui, Hugues C Nana-Djeunga, Georges B Nko’Ayissi, Aboubakary Sanou, Joseph Kamgno

“… # Results We found 81.9% (95% confidence interval, CI=0.784-0.859; standard error=0.019) of intention to use DHIS2 for NTDs program data and 18.4% (95% CI=0.130-0.289; standard error=0.041) of actual use among survey participants. …”
Get full text

Smartphone-Based Automated Non-Destructive Testing Devices by V. F. Petryk, A. G. Protasov, R. M. Galagan, A. V. Muraviov, I. I. Lysenko

“…To ensure the operation of the smartphone in the ultrasonic flaw detector mode, the smartphone has software installed that runs in the Android operating system and implements the proposed algorithm of the device, and can serve as a repeater for processing data over a considerable distance (up to hundreds and thousands of kilometers) if it necessary.The experimental data comparative analysis of the developed device with the Einstein-II flaw detector from Modsonic (India) and the TS-2028H+ flaw detector from Tru-Test (New Zealand) showed that the proposed device is not inferior to them in terms of such characteristics as the range of measured thicknesses, the relative error in determining the depth defect and the object thickness. …”
Get full text

Retrieving total transpirable soil water in a rainfed vineyard using vine shoot growth, weather, and Sentinel-2 data by Yulin Zhang, Léo Pichon, Sébastien Roux, Anne Pellegrino, Guillaume Coulouma, Bruno Tisseyre

“…This study proposes a low-cost, scalable methodology for TTSW estimation, adapting an Inverse Modeling approach to incorporate accessible data sources: vine shoot growth indices based on simple visual observations, weather data, and Sentinel-2 imagery. …”
Get full text

A Rule-Based Predictive Model for Estimating Human Impact Data in Natural Onset Disasters—The Case of a PRED Model by Sara Rye, Emel Aktas

“…<i>Results:</i> Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. …”
Get full text

Integration of GIS and TM Data in Extraction of Early Rice Planted Area of South of China: A Case Study of Longyou County by Ahmad Yaghi, HUANG Jing-feng, WANG Ren-chao, Abou-Ismail Ousama, Al-Abed Mohammad

“…This paper introduced the methodology of early rice planted area estimation by integration of GIS and TM data. The methodology enhanced the classification precision of TM image in both plain and mountainous areas. …”
Get full text

Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process by Zhengsong Wang, Dakuo He, Xu Zhu, Jiahuan Luo, Yu Liang, Xu Wang

“…A novel data-driven model-free adaptive control (DDMFAC) approach is first proposed by combining the advantages of model-free adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC), and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. …”
Get full text

AN ANALYSIS ON MODERN METHODS OF POPULATION AND HOUSING CENSUSES by Arockiasamy LAZAR, N. CHENDRAYUDU

Get full text

Prediction of Mature Body Weight of Indigenous Camel (<i>Camelus dromedarius</i>) Breeds of Pakistan Using Data Mining Methods by Daniel Zaborski, Wilhelm Grzesiak, Abdul Fatih, Asim Faraz, Mohammad Masood Tariq, Irfan Shahzad Sheikh, Abdul Waheed, Asad Ullah, Illahi Bakhsh Marghazani, Muhammad Zahid Mustafa, Cem Tırınk, Senol Celik, Olha Stadnytska, Oleh Klym

“…The highest Pearson correlation coefficient between the observed and predicted values (0.84, <i>p</i> < 0.05) was obtained for MLP, which was also characterized by the lowest root-mean-square error (RMSE) (20.86 kg), standard deviation ratio (SD_ratio) (0.54), mean absolute percentage error (MAPE) (2.44%), and mean absolute deviation (MAD) (16.45 kg). …”
Get full text

Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach by Mohammad Mazyad Hazzazi, Mujeeb Ur Rehman, Arslan Shafique, Amer Aljaedi, Zaid Bassfar, Aminu Bello Usman

“…Several statistical tests, including mean square error analysis, histogram variance analysis, entropy assessment, peak signal-to-noise ratio evaluation, correlation analysis, key space evaluation, and key sensitivity analysis, demonstrate the effectiveness of the proposed work. …”
Get full text

Air quality prediction-based big data analytics using hebbian concordance and attention-based long short-term memory by Sathishkumar Sekar, Zhang Wei

Get full text

Comparing Weibull Stress – Strength Reliability Bayesian Estimators for Singly Type II Censored Data under Different loss Functions by Awatif Rezoky Mazaal, Nada Sabah Karam, Ghada Sabah Karam

Get full text