Wind, Water Level, and Fluid Mud Thresholds in Lake Apopka, Florida by Earl J. Hayter, Ashish J. Mehta, John M. Jaeger, Arnoldo Valle-Levinson, Andrew J. Manning, Carola Forlini

“…The rise, which is considered significant when fluid mud occupies the 0.2 m high benthic boundary layer (BBL), occurs when the threshold wind exceeds about 9 m s⁻¹ corresponding to a 4% cumulative probability of occurrence. Predictive modeling suggests that in 2 m water depth the required wind would be about 14 m s⁻¹ with a low probability of 2%. …”
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Galaxy evolution in the post-merger regime. III -- The triggering of active galactic nuclei peaks immediately after coalescence by Sara Ellison, Leonardo Ferreira, Robert Bickley, Tess Grindlay, Samir Salim, Shoshannah Byrne-Mamahit, Shobita Satyapal, David R. Patton, Jillian M. Scudder

“…The Multi-Model Merger Identifier (MUMMI) machine vision pipeline has been demonstrated to provide reliable predictions of time post-merger (T_PM) for galaxies selected from the Ultraviolet Near Infrared and Optical Northern Survey (UNIONS) up to T_PM=1.76 Gyr after coalescence. …”
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Deep Learning Based Dual Channel Banana Grading System Using Convolution Neural Network by Raghavendra S, Souvik Ganguli, P. Thirumarai Selvan, Mitali Madhusmita Nayak, Sushovan Chaudhury, Randell U. Espina, Isaac Ofori

“…Via a combination of CNN and MLP applied to data collected using RGB and hyperspectral imaging, the multi-input model reliably recognizes bananas with an accuracy level of 98.4 percent as well as an F1-score of 0.97. The AI algorithm predicted the size (large, medium, and microscopic) and perspective (front or rear half) of banana classes with 99 percent accuracy. …”
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Analyzing exosomal miRNA profiles in tetralogy of fallot fetuses’ amniotic fluid by Hainan Yang, Yadan Li, Qiuyue Chen, Shangqing Li, Yiru Yang, Guorong Lyu

“…KEGG pathway enrichment analysis revealed that the predicted targets of these differentially expressed miRNAs were enriched in pathways associated with congenital disorders. …”
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Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning by Rafiqul Islam, Al Imran, Md. Fazle Rabbi

“…The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. …”
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Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification by Chun-Chao Huang, Hsin-Fan Chiang, Cheng-Chih Hsieh, Bo-Rui Zhu, Wen-Jie Wu, Jin-Siang Shaw

“…<b>Background:</b> This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). …”
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Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search by Jürgen Landauer, Simon Maddison, Giacomo Fontana, Axel G. Posluschny

“…While automation significantly reduces manual workload, human verification remains critical for refining AI predictions and addressing false positives. This study also applies different expert validation workflows, emphasizing their efficiency and adaptability to regional differences. …”
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Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1 Receptor Antagonists by Mukesh C. Sharma, D. V. Kohli, Smita Sharma

“…Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. …”
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The Relationship between Landing Error Scoring System Performance and Injury in Female Collegiate Athletes by Peter Lisman, Joshua N. Wilder, Joshua Berenbach, Enric Jiao, Bethany Hansberger

“…A Receiver Operating Characteristic curve determined the optimal cutpoint for the total LESS score for predicting injury. Pearson’s Chi squared statistics examined the association between injury and LESS total scores >5. …”
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The Prognostic Model Established by the Differential Expression Genes Based on CD8+ T Cells to Evaluate the Prognosis and the Response to Immunotherapy in Osteosarcoma by Yu Chen, Wei Yan, Hongqing Wang, Zhiliang Ou, Huihong Chen, Zhenhua Huang, Jinlian Yang, Biqiong Liu, Fanjie Ou, Huang Zhang

“…Furthermore, we constructed a prognostic model (CD8-DEG model) based on the obtained DEGs of CD8+ T cells, and verified the excellent predictive ability of this model for the prognosis of OS. …”
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Cognition Deficits in Parkinson’s Disease: Mechanisms and Treatment by Congcong Fang, Longqin Lv, Shanping Mao, Huimin Dong, Baohui Liu

“…Considered that the timing, profile, and rate of cognitive impairment vary greatly among PD individuals, it is extremely urgent for researchers and clinicians alike to identify and predict future cognitive decline in this population. …”
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Modeling the impact of extreme weather events and future climate on the radiologically contaminated sites of Enewetak Atoll by Lakshitha Premathilake, Saikat Ghosh, Rajiv Prasad, Sourav Taraphdar, Taiping Wang, Tarang Khangaonkar, Bruce Napier, Tracy Ikenberry, Lai-yung Leung

“…Further, due to the limitations of the study and uncertainties/biases in the historical data used, further research supported by field surveys to better characterize the current contamination level may be needed to make more accurate predictions.…”
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Effects of neutrophil granule proteins on sepsis-associated lymphopenia and their relationship with CD4+ T-cell pyroptosis by Jia-yu Mao, Ya-wen Xie, Xian-li Lei, Jia-hui Zhang, Wei Cheng, Na Cui

“…The area under the curve of MPO levels predicting SAL occurrence was 0.939 (95% CI, 0.846–0.984), and that of NE was 0.950 (95% CI, 0.862–0.989). …”
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Creep Energy Evolution of Red-Bed Soft Rocks in South China under Chemical-Stress-Seepage Coupling by Shuguang Zhang, Fanyao Peng, Yingbo Li, Zhifeng Liu, Wenbo Liu

“…The research results provide a new perspective for us to reveal the mechanical properties and failure mechanism of red-bed soft rocks and provide an important theoretical basis for predicting and evaluating the creep instability and long-term stability of such rocks.…”
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Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer’s disease: insights from machine learning analyses and WGCNA by Sanying Mao, Xiyao Zhao, Lei Wang, Yilong Man, Kaiyuan Li

“…In addition, 25 miRNAs and 55 lncRNAs were predicted to potentially target ZDHHC22, forming the basis for a lncRNA–miRNA–mRNA ceRNA network. …”
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Evaluating Producer Welfare Benefits of Whole-Farm Revenue Insurance by Moharram Ainollahi Ahmadabadi, Mohammad Ghahremanzadeh, Ghader Dashti, Seyed-Ali Hosseini-Yekani

“…This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. …”
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INTRASPECIFIC VARIABILITY OF THE Sus1 SUCROSE SYNTHASE GENE IN Pisum sativum ACCESSIONS by E. A. Dyachenko, M. A. Slugina

“…Computational analysis of the amino acid sequences has predicted Sus1 tertiary structure. The protein is a tetramer and each subunit in its turn consists of three domains. …”
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Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation by Ling Hao, Yang Chen, Xuejiao Su, Buyun Ma

“…Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. …”
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RELATIONSHIP OF SELF-REGULATION AND PERSONAL PROFESSIONAL PERSPECTIVE OF STUDENTS by D. P. Zavodchikov, P. O. Manyakova

“…Creation of personal professional perspective is shown as practical actions for predicting of the professional future under the developed behavioural self-regulation structures of the personality.The relationship of self-regulation and professional perspective (as the measured parameters of professional self-determination) was revealed at the empirical level on the sample of students of the Sverdlovsk Regional Teachers Training College. …”
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Elucidation of Nature of Gene Action and Estimation of Combining Ability Effects for Fruit Yield Improvement and Yield Attributing Traits in Brinjal Landraces by Neha Rajan, Sandip Debnath, Avijit kr. Dutta, Brijesh Pandey, Ajeet Kr. Singh, Ravindra Kumar Singh, Anjani Kumar Singh, Betty Nokobi Dugbakie

“…All of the qualities were shown to be highly variable based on the ANOVA (analysis of variance) results among lines, testers, and hybrids. An estimated predictability ratio showed a high prevalence of nonadditive gene action, which was further confirmed by the lower narrow-sense heritability values for all traits. …”
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