Revision, reliability and validity testing, and development of normative data of Inventory of Alienation Toward Parents in military personnel by TAN Jieying, TAN Jieying, SUN Xiaoxiao, DAI Zongpei

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Chronic Rhinosinusitis with Nasal Polyps and Biologics: A Call for Better Data Standardisation and Presentation in Clinical Studies by Kariyawasam HH, Langan D, Rimmer J

“…All such trials should report the data in an easy-to-understand way. This will allow better meta-analysis interpretation of data.Primary researchers should aim to present summary statistics relating to outcomes separately for each arm of their project. …”
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Exploring Global Interest in Propolis, Nanosilver, and Biomaterials: Insights and Implications for Dentistry from Big Data Analytics by Magdalena Sycińska-Dziarnowska, Liliana Szyszka-Sommerfeld, Krzysztof Woźniak, Gianrico Spagnuolo

“…The objective is to evaluate temporal variations, quantify correlations between the terms, and explore how external events, such as the COVID-19 pandemic, have influenced public and clinical interest in these topics. Search data were extracted, normalized, and analyzed using multivariate time series methods, including vector autoregression (VAR) modeling, Impulse Response Function (IRF) analysis, and forecast error variance decomposition (FEVD). …”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…To address this challenge, this paper proposes a novel and integrated data-driven methodology for fault diagnosis of induction motors (IMs) in EV powertrain applications. …”
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Leveraging Spatial and Temporal Data to Predict Heavy Freight Vehicle Traffic Flow on Rural Road Network by Alireza Gholami, Seyedehsan Seyedabrishami

“…In addition to R-squared, model performance has been further evaluated using RMSE and MAE metrics, offering a more nuanced and reliable understanding of prediction accuracy across different error dimensions. Analysis based on SHAP values and sensitivity assessment demonstrates that particular times of day and traffic counter classifications exert the greatest influence on HFV traffic flow predictions. …”
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Short Term Photovoltaic Power Combination Prediction Method Based on Similar Day Selection and Data Reconstruction by Qingbin CHEN, Genghuang YANG, Liqing GENG, Juan SU, Jingsheng SUN

“…A short-term photovoltaic power combination prediction method based on similar day selection and data reconstruction is proposed to address the strong randomness of photovoltaic power. …”
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Intelligent Operation and Maintenance of Wind Turbines Gearboxes via Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun, Binkai Wang

“…Furthermore, an algorithm model for multi-source operational data analysis of wind turbines is designed, leveraging a Whale Optimization Algorithm-optimized Temporal Convolutional Network with an Attention mechanism (WOA-TCN-Attention). …”
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Low-Redshift Observational Constraints on Dark Energy Cosmologies by Mohammad Malekjani

“…Our main aim is to show the influence of each data sample on the procedure of constraining. The main results of our analysis are as follows. …”
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Bias correction and application of labeled smartphone pressure data for evaluating the best track of landfalling tropical cyclones by G. Qiao, Y. Cao, Q. Zhang, J. Sun, H. Yu, L. Bai

“…The significantly higher spatial resolution and broader coverage of the smartphone data led to notable differences between the two analysis fields. …”
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Determination of Pear Cultivars (Pyrus communis L.) Based on Colour Change Levels by Using Data Mining by Dilara Gerdan, Abdullah Beyaz, Mustafa Vatandaş

“…For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. …”
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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

“…However, the existing data collection process faces difficulties in error corrections, rehabilitation, and training validations. …”
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An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…The traditional methods, which use primarily manual reporting or sensor-based methods, can be error-prone, inefficient, and costly. This study introduces an innovative real-time lane closure detection approach using connected vehicle (CV) data and machine learning techniques. …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

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Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…Furthermore, improvements in the 3°C error threshold are observed, with VDSR and EDSR showing increases of 2.10 and 2.16%, respectively. …”
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AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports by Musab Aloudah, Mahdi Alajmi, Alaa Sagheer, Abdulelah Algosaibi, Badr Almarri, Eid Albelwi

“…These findings reinforce the promise of interpretable AI-powered forecasting frameworks in delivering actionable, data-informed insights to support strategic economic planning.…”
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Evidence synthesis in neuromyelitis optica spectrum disorders: modeling a clinical trial based on published data by D. G. Tolkacheva, I. V. Fateev, K. V. Sapozhnikov, O. N. Mironenko, A. A. Lazarev, V. D. Batorova, A. A. Porozova, A. V. Zinkina-Orikhan

“…The aim of this study was to estimate the power for the analysis of the primary endpoint in the single-arm BCD-132-6/AQUARELLE trial, which was planned to be performed using an adjusted indirect comparison (AIC) of the study drug divosilimab with published data on the placebo group in the randomized N-MOmentum trial.   …”
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Point-to-Interval Prediction Method for Key Soil Property Contents Utilizing Multi-Source Spectral Data by Shuyan Liu, Dongyan Huang, Lili Fu, Shengxian Wu, Yanlei Xu, Yibing Chen, Qinglai Zhao

“…For point predictions, metrics such as the coefficient of determination (R²) and error metrics demonstrated significant enhancements compared to predictions based solely on single-source spectral data. …”
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…Subsequently, Spearman correlation analysis was utilized to select the combination of canopy coverage and environmental data, followed by the random forest feature importance ranking method to identify the most optimal feature variables. …”
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Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…This study utilizes 425 air pollution stations and the driving factors data globally from 2013 to 2024. The GML considers geographical characteristics in the analysis by calculating the optimal bandwidth area in its algorithm. …”
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Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data by Zige Lan, Xiandie Jiang, Guiying Li, Yagang Lu, Hongwen Yao, Dengsheng Lu

“…Ordinary Linear Regression (OLR), Geographically Weighted Regression (GWR), and Hierarchical Bayesian Approach (HBA) were employed to model GSV through comparative analysis of using various sample sizes. The results indicate that: (1) HBA(Site), which models different pine forest types (i.e. pure pine forest (PPF) and mixed pine forest (MXF)) separately, with typical site as a stratification factor, provided the best estimation results with coefficient of determination (R2) of 0.80 and 0.74, root mean square error (RMSE) of 25.15 m3/ha and 23.86 m3/ha for PPF and MXF, respectively. …”
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