Exploring the adsorption desulfurization efficiency using RSM and ANN methodologies by Mahyar Mansouri, Mohsen Shayanmehr, Ahad Ghaemi

“…The RBF network achieved superior precision with an R2 of 0.9951 and a mean square error (MSE) of 0.0015, outperforming the MLP. Furthermore, a global sensitivity analysis (GSA) was performed to identify the most influential input parameters, highlighting micropore volume as the dominant factor. …”
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Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis by Josh Leeman, Yuhan Liu, Joseph Stiles, Scott B. Lee, Prajna Bhatt, Leslie M. Schoop, Robert G. Palgrave

“…(i) Automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. …”
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Magnetometry, Acoustical and Inertial Indoor-Positioning in Healthcare by I. V. Cherepanova, I. V. Pospelova, D. S. Bragin, V. N. Serebryakova

“…Inertial sensors possess high accuracy, but over time, the measurement error increases. There-fore, the sensors need to regular correction. …”
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Numerical simulation of tidal hydrodynamics in the Arabian Gulf by Fawaz Madah, Sameer H. Gharbi

“…The performance of the numerical model was evaluated using the hourly water level observations and the TOPEX/Poseidon altimetry data. Statistical analysis showed a good agreement between the simulated and observed water levels. …”
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Numerical Modeling of the Concentration of Microplastics in Lakes and Rivers in Kazakhstan by Natalya S. Salikova, María-Elena Rodrigo-Clavero, Lyudmila A. Makeyeva, Zinep M. Shaimerdenova, Javier Rodrigo-Ilarri

“…The methodology integrates field survey data collected during three different seasons (spring, summer, and autumn) from both sediment and water samples. …”
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Revolutionising English language education:empowering teachers with BERT-LSTM-driven pedagogical tools by Sheik Hameed Nagoor Gani, Vijayakumar Selvaraj, Sahidul Islam Md, Sugadev Thalapathy, Mohamed Jalaludeen Abdulkadhar, Kanmani Kalimuthu

“…The outcomes demonstrate that the proposed BERT-LSTM models are highly accurate in predicting errors and possess standard error metrics for scoring. …”
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Evaluating the Accuracy of the ERA5 Model in Predicting Wind Speeds Across Coastal and Offshore Regions by Mohamad Alkhalidi, Abdullah Al-Dabbous, Shoug Al-Dabbous, Dalal Alzaid

“…Accurate wind speed and direction data are vital for coastal engineering, renewable energy, and climate resilience, particularly in regions with sparse observational datasets. …”
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Geoestatística no estudo de modelagem temporal da precipitação Geostatistics in the temporal modeling study of precipitation by André Q. de Almeida, Aristides Ribeiro, Yhasmin G. Paiva, Nilton Jr. L. Rascon, Evaldo P. Lima

“…With respect to the temporal dependence, few analysis are performed with this kind of methodology. …”
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Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification. by Laura L Faye, Mitchell J Machiela, Peter Kraft, Shelley B Bull, Lei Sun

“…Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. …”
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Time-series characterization of various honey types under different storage conditions based on total polyphenol content and fluorescence properties by Takumi Murai, Teruki Tobari, Sota Kudo, Yoshito Saito

“…Partial least squares regression (PLSR) models constructed using EEM data demonstrated robust TPC prediction capability with R²cv of 0.92, root mean square error cross validation (RMSECV) of 40.66 μg gallic acid equivalent/g and residual prediction deviation (RPD) of 3.61. …”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…The model used in this study is Random Forest (RF), which belongs to the family of ensemble learning models.The data used in this study come from numerical simulations carried out with Matlab and consist of 1400 samples, derived from the analysis of 35 thermal insulators distributed across 20 climate zones. …”
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DEVELOPMENT OF AN EDUCATIONAL CHATBOT WITH A CONTEXTUAL INTENT SYSTEM ON THE DIALOGFLOW PLATFORM by Oksana Ivashchenko, Stanislav Filip, Bohdan Ratushnyi

“…The prototyping model was selected as the life cycle methodology due to the need for active user feedback and iterative improvement. Based on the analysis of the departmental website and survey data from students, an intent system was created that organizes user queries by categories, each with its own fallback intent and context-based clarification mechanisms. …”
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Addressing underestimated carbon monoxide emissions in Taiwan using CMAQ and impacts on local ozone concentration by Chieh-Sen Tsai, Ping-Chieh Huang, Hsin-Chih Lai, John C. Lin, Hui-Ming Hung

“…With the minimum root mean square error (RMSE) analysis between simulation and observation, the optimal emission correction factors are estimated as 2, 4, and 3.6 for northern, central, and southern Taiwan, respectively. …”
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Analisis Status Keberlanjutan Sumber Mata Air Senjoyo pada Dimensi Ekologi dengan Metode RAP-WARES (Rapid Appraissal for Water Resources) by Anggia Agatha Reza, Desti Christian Cahyaningrum, Susanti Pudji Hastuti

“…Also, the analysis process that is repeated is stable and errors in data entry and loss can be avoided. …”
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Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat by Yuanyuan Pan, Jingyu Li, Jiayi Zhang, Jiaoyang He, Zhihao Zhang, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

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Mapping the evidence on the assessment of fitness to work at heights: a scoping review by Lyndsey Swart, Nicolaas Claassen, Tania Buys

“…Deductive qualitative content analysis was applied to the extracted data.Results 68 articles met the inclusion criteria, but only 7 directly addressed fitness to work at heights, with the rest focusing on fitness to work in high-risk settings requiring work at heights or human risk factors associated with work at heights. …”
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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag mode... by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…The aim of this study was to cross-validate TB positivity rate predictions in ACF settings of an existing Bayesian machine learning (BML) with a simpler frequentist model.Methods We conducted a retrospective analysis of cross-sectional data to identify predictors for detection of bacteriologically confirmed TB cases during ACF events in Pakistan. …”
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Exploring the causal relationship between 16 eye diseases and stroke and their subtypes from a genome-wide perspective by Bingxue Su, Yinxiang Sun, Wenlong Yu, Chaoqun Wang, Qing Xia, Yizhun Zhu

“…We excluded 6 results with heterogeneity or pleiotropy by sensitivity analysis, and finally the following reliable results were left: (1) patients with age-related macular degeneration had a 5%, 2%, and 7% lower risk of subarachnoid hemorrhage, ischemic stroke, and small-vessel stroke; (2) patients with keratitis had a 12% higher risk of cardioembolic; (3) patients with optic atrophy had a 3% higher risk of stroke; (4) patients with amblyopia had a 3% higher risk of stroke; and (5) patients with other inflammation of eyelid had a small-vessel had a 20% elevated risk; (6) patients with ptosis of the eye had a 17% elevated risk of cardioembolic; (7) patients with strabismus have a 23% elevated risk of small-vessel; (8) patients with stroke had a refractive error by 17%; (9) patients with intracerebral hemorrhage had a 15% increased risk of uveitis; (10) patients with IS had an 11% increased risk of diabetic retinopathy; (11) patients with large-artery atherosclerosis had a 2% increased risk of glaucoma; (12) patients with cardioembolic had a 24% increased risk of amblyopia. …”
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Digital mapping of soil erodibility factor in response to land use change using machine learning models by Wudu Abiye, Orhan Dengiz

“…These models were trained using the repeated tenfold cross-validation method and evaluated based on root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). …”
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