IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms by Chu Qiu, Jinhui Cai, Zheng Wang, Pengdong Gao, Guojun Wang, Quan Qi, Bo Wang, Zhengwei Cheng, Jiankui Shi, Yajun Zhu, Xiao Wang, Kai Ding

“…The IonoGAN extends the spatiotemporal information‐preserving and perception‐augmented (STIP) ability by incorporating a Local‐Global discriminator to focus on the F region in ionograms. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…The ANN model demonstrated superior predictive capability, achieving a Root Mean Squared Error (RMSE) of 2.34, a Mean Absolute Error (MAE) of 1.24, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a Coefficient of Determination (R2) of 0.97. Furthermore, an Adjusted R 2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. …”
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Soil Moisture Retrieval in Winter Wheat Fields at Different Growth Stages: Integrating a Two-Component Polarimetric SAR Decomposition With CIEM by Wenxin Xue, Qinghua Xie, Xing Peng, J. David Ballester-Berman, Jinfei Wang, Jiali Shang, Haiqiang Fu, Jianjun Zhu

“…Polarimetric synthetic aperture radar (PolSAR) offers strong volume penetrability, high resolution, sensitivity to surface dielectric properties, and the ability to acquire abundant ground target information, thus having considerable advantages in soil moisture (SM) inversion at the agricultural field scale. …”
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A two phase differential evolution algorithm with perturbation and covariance matrix for PEMFC parameter estimation challenges by Mohammad Aljaidi, Pradeep Jangir, Arpita, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, G. Gulothungan, Ali Fayez Alkoradees, Mohammad Khishe

“…This mechanism utilizes a novel weight coefficient, which is determined based on the fitness values and positional attributes of archived individuals, to improve exploration efficiency. …”
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Identification of Exiguobacterium acetylicum by a double real- time polymerase chain reaction assay by GAO Cancan, LIU Jiamei, LI Junjie, LU Zhaoxin, LÜ Fengxia, ZHANG Chong, ZHAO Haizhen, BIE Xiaomei

“…Further, Exiguobacterium has strong proteolysis ability to damage nutrient and food, and it can form biofilm, not only increasing the resistance to detergents and preservatives, but also consuming nutrients to grow and reproduce. …”
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A hydrogeomorphology-informed method for mapping continuous river networks from satellite imagery by Xiaodong Yi, Kang Yang, Wei Mao, Mengtian Man, Dinghua Chen, Jinyu Liu

“…We compare our mapping results with four 10 m resolution land use and land cover (LULC) datasets (FROM GLC10, ESA WorldCover, Esri Land Cover, and Dynamic World) across 16 diverse study sites globally, and investigate the ability of our method to extract multi-temporal river networks at two representative sites. …”
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A dynamic knee function scoring system for anterior cruciate ligament injuries based on normative six-degree-of-freedom gait pattern by Junqiao Li, Yunhe Mao, Tian Lan, Liang Huangfu, Yan Xiong, Jian Li

“…Abstract Background Traditional examinations of anterior cruciate ligament (ACL) injuries focus primarily on static assessments and lack the ability to evaluate dynamic knee stability. Hence, a dynamic scoring system for knee function is needed in clinical settings. …”
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Validation of a rapid on-farm culture system for group classification of clinical mastitis-causing pathogens by Fernando J. Guardado Servellon, David L. Renaud, Bruno Joaquin Paredes Osorio, Kelsey L. Spence, Trevor J. DeVries, Rita Couto Serrenho

“…Using this on-farm test as a primary guide in CM treatment decisions should be approached with caution, as the risk of false negatives may affect the ability to effectively treat CM cases.…”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…Features screened by intraclass correlation coefficient, Mann-Whitney U test, Spearman correlation, minimum redundancy maximum relevance, and Least absolute shrinkage and selection operator regression. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Results The FCN ResNet101 demonstrated the highest segmentation performance for adnexal masses (Dice similarity coefficient: 89.1%). Support vector machine achieved the best AUC (0.961, 95% CI: 0.925–0.996). …”
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The importance of different preoperative factors in the prognosis of functional results of uncomplicated surgery for idiopathic epimacular membranes by S. G. Toropygin, S. V. Nazarova, A. N. Maslov

“…Regression with a 0,597-determination coefficient showed that the baseline visual acuity and central retinal thickness had the biggest impact on the visual acuity measured 1 year after surgery (p < 0.001 and p = 0.002, respectively). …”
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Self-rehabilitation strategy for rural community-dwelling stroke survivors in a lower-middle income country: a modified Delphi study. by Rabiu Ibrahim, Conran Joseph, Aimée Stewart, Isa Usman Lawal

“…Consensus was pre-defined to be the point where the proportion of items given a rating of 3 (quite relevant) or 4 (highly relevant) by expert panellists is ≥  0.8. Kendall's coefficient of concordance (W) was used to assess the level of agreement among the expert panellists.…”
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Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data by Yasuyuki Kawai, Koji Yamamoto, Keisuke Tsuruta, Keita Miyazaki, Hideki Asai, Hidetada Fukushima

“…The Spearman’s correlation coefficients ranged from 0.54 to 0.86. The model outperformed other methods, especially for R² scores and residual standard deviations, demonstrating its superior ability to predict actual vital signs values. …”
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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

“…The superior performance of ANN is attributed to its ability to model complex, non-linear interactions among variables influencing soil erodibility. …”
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Snow cover setting-up dates in the north of Eurasia: relations and feedback to the macro-scale atmospheric circulation by V. V. Popova, A. V. Shiryaeva, P. A. Morozova

“…In the same time negative linear correlation revealed between snow extent and AO index changes during 1950–2008 from statistically insignificant values (in 1950–70 and 1996–2008) to coefficient values –0.82 ÷ –0.85 in 1973–1994 (Fig. 4, а, б). …”
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Improving agricultural commodity allocation and market regulation: a novel hybrid model based on dual decomposition and enhanced BiLSTM for price prediction by Lihua Zhang, Fushun Wang, Fushun Wang, Kejian Wang, Kejian Wang, Zhenxue He, Zhenxue He, Chen Chen, Chen Chen, Jiahao Liu, Jiahao Liu, Chao Wang, Chao Wang, Zhe Wang

“…As an essential part of daily life, the drastic fluctuations in agricultural commodity prices significantly impact producers’ motivation and consumers’ quality of life, further exacerbating market uncertainty and unsustainability. The ability to scientifically and effectively predict agricultural commodity prices is of great significance for the rational deployment of market mechanisms, the timely adjustment of supply chains, and the promotion of food policy adjustments. …”
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Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program by Samuel Dagogo-Jack, Søren Brunak, Carlos Lorenzo, Kieren J Mather, Marinella Temprosa, Tibor V Varga, Jinxi Liu, Ronald B Goldberg, Guannan Chen, Xavier Pi-Sunyer

“…The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.Results Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. …”
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THE IMPACT OF THE TYPE OF CARE ON LONGEVITY IN UKRAINE by Nataliya I. Kitsera, Ruslan V. Kozovyi, Zoriana P. Dvulit, Larysa Ye. Kovalchuk, Maya V. Bondarenko

“…They develop their language, thinking, form their interests, aspirations, recognize the ability to talent. A father serves as an example of courage for children and teaches them to care for and protect others. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Current machine learning (ML) models demonstrate suboptimal predictive performance in KD treatment response prediction, primarily due to their limited ability to effectively process categorical variables and interpret tabular clinical data. …”
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How accurately can supervised machine learning model predict a targeted psychiatric disorder? by Haitham Jahrami, Amir H. Pakpour, Waqar Husain, Achraf Ammar, Zahra Saif, Ali Husain Alsalman, Adel Aloffi, Khaled Trabelsi, Seithikurippu R. Pandi-Perumal, Michael V. Vitiello

“…This study evaluates the ability of a supervised machine learning (ML) model to match the diagnostic skills of psychiatrists when presented with equivalent information pertinent to symptoms of HD. …”
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