The different member equivalent circulating density prediction model and drilling parameter optimization under narrow density window by Wanchun Zhao, Wanchun Zhao, Zhipeng Yang, Tingting Wang, Yan Zhou, Wei Song, Jingrun Li, Peihong Zhai

“…The model uses nonlinear regression algorithms to predict ECD values for different members. …”
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Advancing road maintenance with EfficientDet-based pothole monitoring by Jenefa Archpaul, Taurshia Antony, Kuriakose Bessy Mani, Kumar Edward Naveen Vijaya, Lincy Archpaul

“…Moreover, we provide a comparative analysis with five contemporary pothole detection algorithms: YOLOv5, RetinaNet, CenterNet, SSD, and Faster R-CNN, among which EfficientDet consistently shows superior performance in terms of precision, recall, F1-Score, and average precision. …”
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Expert Consensus on the Management of Acute Otitis Media in India: A Delphi Study by Tanu Singhal, Shyam Kukreja, Jaydeep Choudhury, Vijay Yewale, Balasubramanian Sundaram, Jagdish Chinnappa, Suhas Prabhu, Balaji Veeraraghavan, Camilla Rodrigues, Vidya Krishna, Winsley Rose, Samir Bhargava

“…Finally, experts agreed to include 67 statements pertaining to risk factors, resistant strains, conservative and symptomatic management, and antibiotic treatment algorithms for AOM and OME. Conclusion: The consensus recommendations provide systematic guidance to clinicians on the appropriate management of AOM and OME. …”
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A comprehensive analysis of the prognostic value, expression characteristics and immune correlation of MKI67 in cancers by Xiaolan Pan, Caibiao Wei, Jingyu Su, Min Fang, Qiumei Lin, Yuling Qin, Jie Gao, Jie Zhao, Huiliu Zhao, Fengfei Liu

“…The correlation between MKI67 expression and survival outcome, clinical features, MSI, TMB, and tumor-infiltrating immune cells by TCGA database, xCell, and TIMER algorithms.ResultsMKI67 expression was significantly upregulated across varied cancer types verified by datasets. …”
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METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery by Xie Weng, Yangyue Huang, Zhuoya Fu, Xingli Liu, Fuli Xie, Jiale Wang, Qiaohua Zhu, Dayong Zheng, Dayong Zheng

“…Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes that were subsequently used to construct robust prognostic models via multiple machine learning algorithms. These computational findings were validated through in vitro experiments, immune infiltration assessments, and single-cell RNA sequencing analysis.ResultsOur analyses demonstrate that METTL1 is markedly upregulated in HCC, driving a reprogramming of nucleotide metabolism that modulates the expression of key immune checkpoints, including PD-L1 and CTLA-4. …”
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Gut microbiome composition changes in obstructive sleep apnoea syndrome also in relation to excessive daytime sleepiness by Mariana Fernandes, Orazio Palmieri, Stefano Castellana, Matteo Spanetta, Tiziana Latiano, Clementina Lupo, Claudia De Masi, Christian Cardile, Carmen Calvello, Francesca Izzi, Fabio Placidi, Tommaso Mazza, Nicola Biagio Mercuri, Anna Latiano, Claudio Liguori

“…Composition, community diversity, differences in taxa abundance profiles and sample dysbiosis were evaluated through 16S metagenomics and multiple bioinformatics algorithms. OSAS patients were distributed in two groups according to EDS (ESS score≥10) to assess differences in clinical, polysomnographic and faecal data. …”
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A Novel Geometry Agnostic Delay and Doppler Tracking Technique for GNSS-Reflectometry: Application to the GNOS-II Payload Onboard the FY-3E by Changyang Wang, Adriano Camps, Xiuqing Hu, Hyuk Park, Kegen Yu, Xiaochun Zhai, Wenqiang Lu, Feixiong Huang, Mi Liao, Peng Zhang, Nanshan Zheng, Kefei Zhang, Zhongmin Ma

“…Besides, the geometry of transmitter-specular reflection point-receiver of GNOS-II is unobtainable, so classical algorithms cannot be used. Therefore, an innovative processing technique is presented, which can estimate the peak coordinates of all individual DDMs by appropriately grouping the individual DDMs and incoherently accumulating within each group. …”
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AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING by Muhammad Anas, Muhammad Atif Imtiaz, Saad Khan, Arshad Ali, Noor Fatima Naghman, Hamayun Khan, Sami Albouq

“…The four machine learning algorithms that were used for the analysis include Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks to determine which of them is most appropriate for predicting heart diseases. …”
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Two-dimensional differential form of distributed Xinanjiang model by J. Zhao, Z. Liang, V. P. Singh, V. P. Singh, V. P. Singh, T. Wen, Y. Hu, B. Li, J. Wang

“…Our results suggested that (a) numerical errors in the existing distributed Xinanjiang model are significant and may be exacerbated by a potential terrain amplification effect, which could be effectively controlled by the fully coupled numerical framework within the TDD-XAJ model; (b) the 2D slope concentration methods showed enhanced terrain capture ability and eliminated the reliance on the flow direction algorithms used in 1D methods; and (c) the TDD-XAJ model exhibited improved simulation capabilities compared to the existing model when applied in the Tunxi watershed, particularly for flood volume. …”
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Does advancement in marker-less pose-estimation mean more quality research? A systematic review by Shivam Bhola, Shivam Bhola, Hyun-Bin Kim, Hyeon Su Kim, BonSang Gu, Jun-Il Yoo, Jun-Il Yoo

“…Despite the emergence of state-of-the-art keypoint-detection algorithms, the extent to which these tools are employed and the nature of their application in scientific research has yet to be systematically documented. …”
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NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care by Tarun Vats, Shrey Mehra, Uday Madan, Amit Chhabra, Akashdeep Sharma, Kunal Chhabra, Sarabjeet Singh, Utkarsh Chauhan

“…By merging IoT technology with machine learning algorithms, smart farming is poised to enter a transformative phase, providing a scalable response to the pressing issues of global food security. …”
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Adverse events in the nervous system associated with blinatumomab: a real-world study by Wen Gao, Jingwei Yu, Yifei Sun, Zheng Song, Xia Liu, Xue Han, Lanfan Li, Lihua Qiu, Shiyong Zhou, Zhengzi Qian, Xianhuo Wang, Huilai Zhang

“…The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence interval progressive neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were utilized for data mining. Results A total of 5,962 blinatumomab-related cases were analyzed. …”
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THE EFFECTS OF LIQUIRITIGEN ON THE LUKEMIA INHIBITORY FACTOR RECEPTOR IN GRANULOSA CELLS OF INFERTILE PATIENTS by Emel Usta, Sibel Köktürk, Sibel Doğan, Ayşe Altun, Sibel Bulgurcuoğlu Kuran, Feride Özdemir

“…Conclusions: In granulosa cells of LQ-groups, LIFR immunocytochemistry staining intensity algorithms were obtained as significantly higher than in the especially the OF and OF-DMSO groups, and showed the positive effects on apoptotic cell morphology. …”
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Right ventricular mechanical support as a bridge to lung transplantation: A literature review of current practices by Michaela Orlitová, Bert Demeulenaere, Dieter Van Beersel, Dirk E. Van Raemdonck, Robin Vos, Laurens J. Ceulemans, Steffen Rex, Laurent Godinas, Tom Verbelen, Arne P. Neyrinck

“…We aim to synthesize current evidence on RVD/RVF assessment and management in BTT patients requiring ECLS, providing a foundation to aid development of standardized clinical algorithms.A scoping literature search across PubMed, Embase, Web of Science, Cochrane Library, and grey literature was performed. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…The proposed methodology encompasses four distinct phases: initial segmentation of raw chest radiographs employing Conditional Generative Adversarial Networks (CGAN), followed by feature extraction through a tailored pipeline integrating both manual computer vision algorithms and pre-trained Deep Neural Network (DNN) models. …”
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Many-Objective Truss Structural Optimization Considering Dynamic and Stability Behaviors by João Marcos P. Vieira, José Pedro G. Carvalho, Dênis E. C. Vargas, Érica C. R. Carvalho, Patrícia H. Hallak, Afonso C. C. Lemonge

“…The methodology involves the finite element method (FEM) to obtain the objective functions and constraints and multi-objective evolutionary algorithms (MOEAs) based on differential evolution to solve the MOSOPs analyzed in this study. …”
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Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment by Leonardo Costanza, Beatriz Lorente, Francisco Pedrero Salcedo, Francesco Pasanisi, Vincenzo Giannico, Francesca Ardito, Carlota María Martí Martínez, Simone Pietro Garofalo

“…Sentinel-2 reflectance bands and vegetation indices were used as predictors and different machine learning algorithms were tested and compared. Random Forest showed the highest predictive accuracy, particularly when Sentinel-2 reflectance bands were used as predictors. …”
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Screening colorectal cancer associated autoantigens through multi-omics analysis and diagnostic performance evaluation of corresponding autoantibodies by Zan Qiu, Yifan Cheng, Haiyan Liu, Tiandong Li, Yinan Jiang, Yin Lu, Donglin Jiang, Xiaoyue Zhang, Xinwei Wang, Zirui Kang, Lei Peng, Keyan Wang, Liping Dai, Hua Ye, Peng Wang, Jianxiang Shi

“…Ten machine learning algorithms were utilized to develop diagnostic models, with the optimal one selected and integrated into an R Shiny-based GUI to enhance usability and accessibility. …”
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An innovative UAV-based approach for estimating crop stand counts amidst weed infestation by Gurjinder S. Baath, Arun Bawa, Bala Ram Sapkota, K. Colton Flynn, Sayantan Sarkar, Douglas R. Smith

“…However, most existing algorithms assume clean crop rows and do not account for common weed infestation. …”
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MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

“…The CNN models are combined using nine distinct ensemble meta-learning algorithms, with five of them being modifications of averaging and voting techniques. …”
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