AI and Data Analytics in the Dairy Farms: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…In the treatment of variability, the models reviewed are mostly deterministic (77%), and the stochastic models (5%) are considered in a small number of cases. Based on our analysis, we conclude that future research on decision-making tools will benefit from the advantages of artificial neural networks in data analytics combined with optimization–simulation methods.…”
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Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation by N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov

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Implementation of the north central Timor regent instruction number: DPMD 413.4/208/vi/2019 as a stunting management effort in the border area of the Republic of Indonesia - democr... by Aplonia Pala, Fidelis Atanus, Sulistio D Binsasi

“…This research method uses qualitative methods and activities in data analysis, namely reduction, presentation, and drawing conclusions. …”
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The impact of soil data on SWAT modeling: Effects, requirements, and future directions by Yassine Bouslihim, Mohamed Ouarani, Soufiane Taia, El Mahdi El Khalki, Abdessamad Hadri, Mohamed Hakim Kharrou, Abdelghani Chehbouni

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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. …”
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Genome-Wide Analysis of PAPS1-Dependent Polyadenylation Identifies Novel Roles for Functionally Specialized Poly(A) Polymerases in Arabidopsis thaliana. by Christian Kappel, Gerda Trost, Hjördis Czesnick, Anna Ramming, Benjamin Kolbe, Son Lang Vi, Cláudia Bispo, Jörg D Becker, Cornelia de Moor, Michael Lenhard

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Enhancing corn yield prediction: Optimizing data quality or model complexity? by Yuting Zhou, Shengfang Ma, Huihui Zhang, Sathyanarayanan Aakur

“…In this study, we aim to provide a comprehensive analysis of the effectiveness of traditional ensemble learning methods (Random Forest and Gradient Boosting) and deep learning models (ResNet 18, ResNet34, and ViT) in predicting corn yield across deficit and fully irrigated fields using UAV-based RGB and multispectral imagery. …”
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Corticosteroid treatment prediction using chest X-ray and clinical data by Anzhelika Mezina, Samuel Genzor, Radim Burget, Vojtech Myska, Jan Mizera, Aleksandr Ometov

“…Results: The experiments have proved that combining clinical data with CXR images achieves 8% higher accuracy than independent analysis of CXR images. …”
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Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study by Camille R. Reaux, Shelby A. Meche, Jordan M. Grider, Soundharya Dhanabal, Tarikul I. Milon, Feng Chen, Wu Xu

“…To assess students’ performance, we have developed an assessment rubric that includes (i) demonstration of supercomputer skills in job script preparation, submission and monitoring, (ii) skills in preparation of datasets, (iii) data analytical skills, (iv) project report, (v) presentation, and (vi) integration of the TSR-based method with other computational methods (e.g., molecular 3D structural visualization and protein sequence analysis). …”
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PREVENTION OF MEDIATION IN BRIBERY by ZAMARAEV V.I.

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NECROTIZING GINGIVITIS: CASE REPORTS I (REVIEW OF THE LITERATURE) by V.I. Shynkevych

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The influence of green marketing orientation on customer behavior - A study in the real estate sector in Vietnam by Nguyễn Thị Phương Thảo, Nguyễn Đoàn Bảo Uyên, Bạch Văn Lượng, Nguyễn Văn Anh

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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models by Chenling Xu, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I Jordan, Nir Yosef

“…In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA‐seq data, while accounting for uncertainty caused by biological and measurement noise. …”
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A Distillation Approach to Transformer-Based Medical Image Classification with Limited Data by Aynur Sevinc, Murat Ucan, Buket Kaya

“…<b>Methods</b>: This study investigates the impact of distillation techniques on the classification performance of transformer-based deep learning architectures trained on limited data. We use transformer-based models ViTx32 and ViTx16 without distillation and DeiT and BeiT with distillation. …”
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Clinical features of post-COVID-19 period. Results of the international register “Dynamic analysis of comorbidities in SARS-CoV-2 survivors (AKTIV SARS-CoV-2)”. Data from 6-month f... by G. P. Arutyunov, E. I. Tarlovskaya, A. G. Arutyunov, Yu. N. Belenkov, A. O. Konradi, Yu. M. Lopatin, A. P. Rebrov, S. N. Tereshchenko, A. I. Chesnikova, H. G. Hayrapetyan, A. P. Babin, I. G. Bakulin, N. V. Bakulina, L. A. Balykova, A. S. Blagonravova, M. V. Boldina, A. R. Vaisberg, A. S. Galyavich, V. V. Gomonova, N. Yu. Grigorieva, I. V. Gubareva, I. V. Demko, A. V. Evzerikhina, A. V. Zharkov, U. K. Kamilova, Z. F. Kim, T. Yu. Kuznetsova, N. V. Lareva, E. V. Makarova, S. V. Malchikova, S. V. Nedogoda, M. M. Petrova, I. G. Pochinka, K. V. Protasov, D. N. Protsenko, D. Yu. Ruzanau, S. A. Sayganov, A. Sh. Sarybaev, N. M. Selezneva, A. B. Sugraliev, I. V. Fomin, O. V. Khlynova, O. Yu. Chizhova, I. I. Shaposhnik, D. A. Shchukarev, A. K. Abdrahmanova, S. A. Avetisian, H. G. Avoyan, K. K. Azarian, G. T. Aimakhanova, D. A. Ayipova, A. Ch. Akunov, M. K. Alieva, A. V. Aparkina, O. R. Aruslanova, E. Yu. Ashina, O. Yu. Badina, O. Yu. Barisheva, T. I. Batluk, A. S. Batchayeva, R. A. Bashkinov, A. M. Bitieva, I. U. Bikhteyev, N. A. Borodulina, M. V. Bragin, A. M. Budu, G. A. Bykova, K. R. Vagapova, D. D. Varlamova, N. N. Vezikova, E. A. Verbitskaya, O. E. Vilkova, E. A. Vinnikova, V. V. Vustina, E. A. Gаlova, V. V. Genkel, E. I. Gorshenina, E. V. Grigorieva, E. Yu. Gubareva, G. M. Dabylova, I. A. Demchenko, O. Yu. Dolgikh, M. Y. Duyshobayev, D. S. Evdokimov, K. E. Egorova, A. N. Ermilova, A. E. Zheldybayeva, N. V. Zarechnova, Yu. D. Zimina, S. Yu. Ivanova, E. Yu. Ivanchenko, M. V. Ilina, M. V. Kazakovtseva, E. V. Kazymova, Yu. S. Kalinina, N. A. Kamardina, A. M. Karachenova, I. A. Karetnikov, N. A. Karoli, M. K. Karsiev, D. S. Кaskaeva, K. F. Kasymova, Zh. B. Kerimbekova, A. Sh. Kerimova, E. S. Kim, N. V. Kiseleva, D. A. Klimenko, A. V. Klimova, O. V. Kovalishena, E. V. Kolmakova, T. P. Kolchinskaya, M. I. Kolyadich, O. V. Kondriakova, M. P. Konoval, D. Yu. Konstantinov, E. A. Konstantinova, V. A. Kordukova, E. V. Koroleva, A. Yu. Kraposhina, T. V. Kriukova, A. S. Kuznetsova, T. Yu. Kuzmina, K. V. Kuzmichev, Ch. K. Kulchoroeva, T. V. Kuprina, I. M. Kouranova, L. V. Kurenkova, N. Yu. Kurchugina, N. A. Kushubakova, V. I. Levankova, N. A. Lyubavina, N. A. Magdeyeva, K. V. Mazalov, V. I. Majseenko, A. S. Makarova, A. M. Maripov, A. A. Marusina, E. S. Melnikov, N. B. Moiseenko, F. N. Muradova, R. G. Muradyan, Sh. N. Musaelian, A. O. Myshak, E. S. Nekaeva, N. M. Nikitina, B. B. Ogurlieva, A. A. Odegova, Yu. M. Omarova, N. A. Omurzakova, Sh. O. Ospanova, E. V. Pahomova, L. D. Petrov, S. S. Plastinina, V. A. Pogrebetskaya, D. V. Polyakov, D. S. Polyakov, E. V. Ponomarenko, L. L. Popova, N. A. Prokofeva, I. A. Pudova, N. A. Rakov, A. N. Rakhimov, N. A. Rozanova, S. Serikbolkyzy, A. A. Simonov, V. V. Skachkova, D. V. Soloveva, I. A. Soloveva, I. M. Sukhomlinova, A. G. Sushilova, D. R. Tagayeva, Yu. V. Titojkina, E. P. Tikhonova, D. S. Tokmin, A. A. Tolmacheva, M. S. Torgunakova, K. V. Trenogina, N. A. Trostianetckaia, D. A. Trofimov, M. A. Trubnikova, A. A. Tulichev, A. T. Tursunova, N. D. Ulanova, O. V. Fatenkov, O. V. Fedorishina, T. S. Fil, I. Yu. Fomina, I. S. Fominova, I. A. Frolova, S. M. Tsvinger, V. V. Tsoma, M. B. Cholponbaeva, T. I. Chudinovskikh, O. A. Shevchenko, T. V. Sheshina, E. A. Shishkina, K. Yu. Shishkov, S. Yu. Sherbakov, E. A. Yausheva

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Thrombolytic Therapy in Normotensive Patients with Pulmonary Embolism (Data from the Retrospective Study) by N. A. Сherepanova, I. S. Mullova, A. R. Kiselev, T. V. Pavlova, S. M. Khokhlunov, D. V. Duplyakov

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A Multi-Modal Approach Using a Hybrid Vision Transformer and Temporal Fusion Transformer Model for Stock Price Movement Classification by Ibanga Kpereobong Friday, Sarada Prasanna Pati, Debahuti Mishra

“…Our approach adequately captures potential price reversals or continuations by utilizing two modalities (candlestick chart patterns and historical price data). Specifically, the proposed framework converts the historical data into candlestick charts of <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula>-pixel images where both modalities are effectively integrated and processed. …”
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A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges by Thi-Thu-Trang Do, Quyet-Thang Huynh, Kyungbaek Kim, Van-Quyet Nguyen

“…Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. …”
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Evaluating the concordance between Vesical Imaging Reporting and Data System scores and bladder tumor histopathology by Hasan Gungor, Ahmet Camtosun, Ibrahim Topcu, Leyla Karaca

“…All participants underwent mpMRI, and Vesical Imaging Reporting and Data System (VI-RADS) criteria were applied to assess the recorded images. …”
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Estimating carbon stock using vegetation indices and empirical data in the upper awash river basin by Fekadu Legesse, Sileshi Degefa, Teshome Soromessa

“…The goal of this study was to determine carbon stocks using the linear correlation between field inventory and remote sensing data in the basin. The regression model was created using each plot studied from field inventory and the corresponding vegetation indices (VI) values obtained from ArcGIS software in the basin’s study area. …”
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