Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin by Abdul-Muaizz Koray, Emmanuel Gyimah, Mohamed Metwally, Hamid Rahnema, Olusegun Tomomewo

“…Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. …”
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An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series by Junxian Tang, Peijuan Wang, Rui Feng, Yang Li, Qing Li

“…Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. …”
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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models by Jitendra Khatti, Mohammadreza Khanmohammadi, Yewuhalashet Fissha

“…Each optimized RVM and LSTM model has been trained and tested by 100 and 26 datasets. In addition, the effect of structural and database multicollinearities has been analyzed on models’ prediction capabilities. …”
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BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches by Pinakshi Panda, Sukant Kishoro Bisoy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Zheshan Guo, Haipeng Liu, Prince Jain

“…The ensemble ML-based model BIMSSA was evaluated using microarray data from four different cancer types: Adult acute lymphoblastic leukemia and Acute myelogenous leukemia (ALL-AML), Lymphoma, Mixed-lineage leukemia (MLL), and Small round blue cell tumors (SRBCT).ResultsIn terms of accuracy, the proposed BIMSSA (Boruta + IMRMR + SSA) achieved 96.7% for ALL-AML, 96.2% for Lymphoma, 95.1% for MLL, and 97.1% for the SRBCT cancer datasets, according to the empirical evaluations.ConclusionThe results show that the proposed approach can accurately predict different forms of cancer, which is useful for both physicians and researchers.…”
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Exploring the predictive power of antinuclear antibodies and Rheumatoid factor correlations in anticipating therapeutic outcomes for female patients with coexisting Sjögren's syndr... by Anitha Krishnan Pandarathodiyil, Hema Shree K, Pratibha Ramani, B. Sivapathasundharam, Ramya Ramadoss

“…These findings suggest that machine learning models could enhance personalized treatment strategies for patients with SS and RA, but additional validation with larger datasets is required.…”
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Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease by David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Daniel Kling, Maria Lerm, Mika Gustafsson

“…We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn’s disease.DiscussionIn this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. …”
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Potential traumatic events in the workplace and depression, anxiety and post-traumatic stress: a cross-sectional study among Dutch gynaecologists, paediatricians and orthopaedic su... by Maria G van Pampus, Karel Willem Frank Scheepstra, Hannah S Pauw, Minouk Esmee van Steijn, Claire A I Stramrood, Miranda Olff

“…Objective To compare the prevalence of work-related potential traumatic events (PTEs), support protocols and mental health symptoms across Dutch gynaecologists, orthopaedic surgeons and paediatricians.Design Cross-sectional study, supplementary analysis of combined data.Setting Nationwide survey between 2014 and 2017.Participants An online questionnaire was sent to all Dutch gynaecologists, orthopaedic surgeons and paediatricians, including resident physicians (4959 physicians). 1374 questionnaires were eligible for analysis, corresponding with a response rate of 27.7%.Outcome measures Primary outcome measures were the prevalence of work-related PTEs, depression, anxiety, psychological distress and traumatic stress, measured with validated screening instruments (Hospital Anxiety and Depression Scale, Trauma Screening Questionnaire). …”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…Five scenarios of missing data (10%, 20%, 30%, 40%, 50%) were simulated, in which datasets were randomly withdrawn from the ETo base. …”
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A comparative analysis of sequence composition in different lots of a phage display peptide library during amplification by Anders Wilgaard Sinkjaer, Ane Beth Sloth, Amanda Oester Andersen, Malte Jensen, Babak Bakhshinejad, Andreas Kjaer

“…DNA from the samples was subjected to Next-Generation Sequencing (NGS) using an Illumina platform. The NGS datasets underwent a variety of bioinformatic analyses using Python and MATLAB scripts. …”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…On the other hand, the low availability of 3D datasets significantly impedes learning high-quality 3D features because of the high data processing cost. …”
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Integrative analyses of mendelian randomization and bioinformatics reveal casual relationship and genetic links between COVID-19 and knee osteoarthritis by Xiao Zheng, Jinhao Li, Qinfeng Ma, Jianping Gong, Jianbo Pan

“…Additionally, we conducted differential gene expression analysis using public datasets related to these three conditions. Subsequent analyses, including transcriptional regulation analysis, immune cell infiltration analysis, single-cell analysis, and druggability evaluation, were performed to explore potential mechanisms and prioritize therapeutic targets. …”
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Seasonal and Regional Variability of Long-Wave Effective Radiation in China and Associated Modulating Factors by Qianrong Ma, Jie Zhang, Yu Gu, Yujun Ma, Yu Cao

“…Variations in all-sky and clear-sky long-wave effective radiation (LER) in China during the period 2001–2016 were determined using monthly radiative datasets from the Clouds and the Earth’s Radiant Energy System (CERES). …”
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FedBFGCN: A Graph Federated Learning Framework Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution by Hefei Wang, Ruichun Gu, Jingyu Wang, Xiaolin Zhang, Hui Wei

“…The FedBFGCN was evaluated on standard reference network datasets (Cora, Citeseer, Polblogs), and experimental results showed that it has lower losses and higher performance in multiple aspects. …”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

“…Extensive experiments on three benchmark ORSI-SOD datasets demonstrate that our proposed network achieves state-of-the-art performance.…”
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Payment Reforms for Prehospital Care Services in a Middle Income Country: Assessing Implementation and Patient Outcomes Using a Mixed-Methods Approach by Paibul Suriyawongpaisal, Samrit Srithamrongsawad, Pongsakorn Atiksawedparit, Khanisthar Phooseemungkun, Krongkan Bunchaiyai, Thanita Thongtan

“…Using a mixed-methods approach, retrospective datasets of 3,769,399 individual records of call responses from 2015 to 2017 were analyzed which compared EMS units with the direct-pay system against those without in terms of time flow of claim data and patient outcomes. …”
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Changes in incidence and epidemiology of antimicrobial resistant pathogens before and during the COVID-19 pandemic in Germany, 2015–2022 by Jonathan H. J. Baum, Achim Dörre, Felix Reichert, Ines Noll, Marcel Feig, Tim Eckmanns, Mirco Sandfort, Sebastian Haller

“…Methods We used two independent datasets, statutory surveillance and laboratory-based Antibiotic Resistance Surveillance (ARS). …”
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Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review by Md Mohshin Ali, Md. Arif Hossen, Azrina Abd Aziz

“…These models can handle extensive experimental datasets, optimize operational parameters, and provide insights into CO2 reduction (CO2R) mechanisms. …”
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Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images by U. M. Prakash, S. Iniyan, Ashit Kumar Dutta, Shtwai Alsubai, Janjhyam Venkata Naga Ramesh, Sachi Nandan Mohanty, Khasim Vali Dudekula

“…The experimental validation of the MFFDCNN-CTDC approach exhibited a superior accuracy value of 98.78% and 99.02% over existing techniques under ISIC 2017 and HAM10000 datasets.…”
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Influence of type traits on stayability in the Ital- ian Mediterranean buffalo by Damiano Altieri, Stefano Biffani, Mayra Gómez, Yuri Gombia, Dario Rossi, Luca Gubitosi, Nadia Piscopo, Francesca Carnovale, Rossella Di Palo

“…Data from 276.415 buffalo cows registered in the ANASB studbook starting from 1989 were extracted and merged with the official milk test-day recording datasets from the Italian Breeder’s Association (AIA). …”
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Impact of low-dose CT screening for lung cancer on ethnic health inequities in New Zealand: a cost-effectiveness analysis by Sue Crengle, Giorgi Kvizhinadze, Melissa McLeod, Peter Sandiford, Karen Bartholomew

“…Input parameters came from literature and NZ-linked health datasets.Setting New Zealand.Participants Population aged 55–74 years in 2011.Interventions Biennial LDCT screening for lung cancer compared with usual care.Outcome measures Incremental cost-effectiveness ratios were calculated using the average difference in costs and HALYs between the screened and the unscreened populations. …”
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