Progress on the world’s primate hotspots and coldspots: modeling ensemble super SDMs in cloud-computers based on digital citizen-science big data and 200+ predictors for more susta... by Moriz Steiner, Falk Huettmann

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diaTracer enables spectrum-centric analysis of diaPASEF proteomics data by Kai Li, Guo Ci Teo, Kevin L. Yang, Fengchao Yu, Alexey I. Nesvizhskii

“…The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker’s timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. …”
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Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

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Applying Big Data for Maritime Accident Risk Assessment: Insights, Predictive Insights and Challenges by Vicky Zampeta, Gregory Chondrokoukis, Dimosthenis Kyriazis

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Exploring the application of knowledge transfer to sports video data by Shahrokh Heidari, Gibran Zazueta, Riki Mitchell, David Arturo Soriano Valdez, Mitchell Rogers, Mitchell Rogers, Jiaxuan Wang, Ruigeng Wang, Marcel Noronha, Alfonso Gastelum Strozzi, Mengjie Zhang, Patrice Jean Delmas, Patrice Jean Delmas

“…Results demonstrate that models pre-trained on sports player re-identification data outperformed those pre-trained on general person re-identification datasets. …”
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Big Data Intelligent Analysis Technology for the Study of Spatial and Time Trends in the Development of Large Cities by K. V. Mulyukova, I. V. Mulyukov, V. M. Kureichik

“…The study includes a review of bibliographic sources on the problems of big data analysis and their application in the modern field of construction of large cities. …”
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Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events by F. A. Sharifullin, D. D. Dolotova, T. G. Barmina, S. S. Petrikov, L. S. Kokov, G. R. Ramazanov, Y. R. Blagosklonova, I. V. Arkhipov, I. M. Skorobogach, N. N. Cheremushkin, V. V. Donitova, B. A. Kobrinski, A. V. Gavrilov

“…The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.…”
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Analysis of atmospheric deposition in the territory of the Republic of North Ossetia-Alania by biomonitoring data by I. Z. Kamanina, S. P. Kaplina, M. V. Gustova, Dz. N. Chigoeva, I. I. Vinogradov

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Analysis of follow-up data in large biobank cohorts: a review of methodology by Anastassia Kolde, Anastassia Kolde, Merli Koitmäe, Merli Koitmäe, Meelis Käärik, Märt Möls, Krista Fischer, Krista Fischer

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A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

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A scientometric analysis of the critical role of composite materials in dealing with climate change (2004 – 2024) by Suryanto Suryanto, Prabowo Aditya Rio, Adiputra Ristiyanto, Melnyk Oleksiy

“…The review attempts to give a comprehensive picture of composite materials that play a vital role in conservation by combining data from other studies, pointing out knowledge gaps, and recommending future research approaches. …”
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Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

“…To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods.…”
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Experimental and regression analysis of chicken feather-based chopped glass fibre mat hybrid composites by Lokesh K S, Shrinivasa Mayya D, Santhosh Kumar T C, Somaiah K S, Sharath Chandra H S, Varun Kumar K R, Sneha, Pavan Kumar P M

“…Adding 15wt.% of CF lowers the mechanical properties with reduced natural frequency due to agglomeration. Regression analysis was performed on tensile and flexural test data to determine the best fit curve using the experimental results. …”
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Analysis of malocclusion prevalence data in primary dentition in XXI century: literature review by M. L. Belfer, T. F. Kosyreva

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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. by Krzysztof J Gorgolewski, Fidel Alfaro-Almagro, Tibor Auer, Pierre Bellec, Mihai Capotă, M Mallar Chakravarty, Nathan W Churchill, Alexander Li Cohen, R Cameron Craddock, Gabriel A Devenyi, Anders Eklund, Oscar Esteban, Guillaume Flandin, Satrajit S Ghosh, J Swaroop Guntupalli, Mark Jenkinson, Anisha Keshavan, Gregory Kiar, Franziskus Liem, Pradeep Reddy Raamana, David Raffelt, Christopher J Steele, Pierre-Olivier Quirion, Robert E Smith, Stephen C Strother, Gaël Varoquaux, Yida Wang, Tal Yarkoni, Russell A Poldrack

“…Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. …”
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Research on management mode of talent team in E-government based on big data analysis by Shixuan Hao

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fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience by Meryem A. Yücel, Robert Luke, Rickson C. Mesquita, Alexander von Lühmann, David M. A. Mehler, Michael Lührs, Jessica Gemignani, Androu Abdalmalak, Franziska Albrecht, Iara de Almeida Ivo, Christina Artemenko, Kira Ashton, Paweł Augustynowicz, Aahana Bajracharya, Elise Bannier, Beatrix Barth, Laurie Bayet, Jacqueline Behrendt, Hadi Borj Khani, Lenaic Borot, Jordan A. Borrell, Sabrina Brigadoi, Kolby Brink, Chiara Bulgarelli, Emmanuel Caruyer, Hsin-Chin Chen, Christopher Copeland, Isabelle Corouge, Simone Cutini, Renata Di Lorenzo, Thomas Dresler, Adam T. Eggebrecht, Ann-Christine Ehlis, Sinem B. Erdoğan, Danielle Evenblij, Talukdar Raian Ferdous, Victoria Fracalossi, Erika Franzén, Anne Gallagher, Christian Gerloff, Judit Gervain, Noy Goldhamer, Louisa K. Gossé, Ségolène M. R. Guérin, Edgar Guevara, SM Hadi Hosseini, Hamish Innes-Brown, Isabell Int-Veen, Sagi Jaffe-Dax, Nolwenn Jégou, Hiroshi Kawaguchi, Caroline Kelsey, Michaela Kent, Roman Kessler, Nadeen Kherbawy, Franziska Klein, Nofar Kochavi, Matthew Kolisnyk, Yogev Koren, Agnes Kroczek, Alexander Kvist, Chen-Hao Paul Lin, Andreas Löw, Siying Luan, Darren Mao, Giovani G. Martins, Eike Middell, Samuel Montero-Hernandez, Murat Can Mutlu, Sergio L. Novi, Natacha Paquette, Ishara Paranawithana, Yisrael Parmet, Jonathan E. Peelle, Ke Peng, Tommy Peng, João Pereira, Paola Pinti, Luca Pollonini, Ali Rahimpour Jounghani, Vanessa Reindl, Wiebke Ringels, Betti Schopp, Alina Schulte, Martin Schulte-Rüther, Ari Segel, Tirdad Seifi Ala, Maureen J. Shader, Hadas Shavit, Arefeh Sherafati, Mojtaba Soltanlou, Bettina Sorger, Emma Speh, Kevin D. Stubbs, Katharina Stute, Eileen F. Sullivan, Sungho Tak, Zeus Tipado, Julie Tremblay, Homa Vahidi, Maaike Van Eeckhoutte, Phetsamone Vannasing, Gregoire Vergotte, Marion A. Vincent, Eileen Weiss, Dalin Yang, Gülnaz Yükselen, Dariusz Zapała, Vit Zemanek

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Statistical Analysis of Physical Characteristics Calculated by NEMO Model After Data Assimilation by Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov

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Improving SVM performance through data reduction and misclassification analysis with linear programming by Carlos Aníbal Suárez, Mauricio Castro, Mariuxi Leon, Carlos Martin-Barreiro, Michael Liut

“…Abstract In the dual optimization problem behind Support Vector Machine (SVM), each data point corresponds to a decision variable. Therefore, removing data points is equivalent to reducing the dimensionality of the dual problem, leading to a more efficient optimization process. …”
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Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data by Gang Li, Hyeon-Jin Kim, Sriram Pendyala, Ran Zhang, Jean-Philippe Vert, Christine M. Disteche, Xinxian Deng, Douglas M. Fowler, William Stafford Noble

“…To address this problem, we propose Sceptic, a support vector machine model for supervised pseudotime analysis. We demonstrate that Sceptic achieves significantly improved prediction power relative to state-of-the-art methods, and that Sceptic can be applied to a variety of single-cell data types, including single-nucleus image data.…”
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