PREPOSITION IN THE SOCIO-CULTURAL SPACE OF LANGUAGE by Z. Kasyanova, C. Martirosyan

“…Some universal parameters, suficient for the classiication of prepositional language systems on their basis, are highlighted. A comparative analysis of contexts against the background of similar features of the analyzed prepositional systems of different languages allowed us to detect both interlanguage analogies and differences. …”
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Whole Genome Analysis of <i>Proteus mirabilis</i> in a Poultry Breeder Farm Reveals the Dissemination of <i>bla</i>_NDM and <i>bla</i>_CTX-M Mediated by Diverse... by Haibin Hu, Ke Wu, Tiejun Zhang, Yuhuan Mou, Luya Liu, Xiaoqin Wang, Wei Xu, Wenping Chen, Xiaojiao Chen, Hongning Wang, Changwei Lei

“…However, research on antibiotic resistance and genomic features of <i>P. mirabilis</i> in China’s poultry industry is limited. …”
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Newly diagnosed diseases and the frequency of their occurrence in patients after a new coronavirus infection. Results of an International Register "Dynamics Analysis of Comorbiditi... by G. P. Arutyunov, E. I. Tarlovskaya, A. G. Arutyunov, D. S. Polyakov, 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, M. I. Butomo, A. R. Vaisberg, A. S. Galyavich, V. V. Gomonova, N. Yu. Grigoryeva, I. V. Gubareva, I. V. Demko, A. V. Evzerikhina, A. V. Zharkov, A. A. Zateishchikova, U. K. Kamilova, Z. F. Kim, T. Yu. Kuznetsova, A. N. Kulikov, 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. Ruzanov, S. A. Saiganov, A. Sh. Sarybaev, N. M. Selezneva, A. B. Sugraliev, I. V. Fomin, O. V. Khlynova, O. Yu. Chizhova, I. I. Shaposhnik, D. A. Schukarev, A. K. Abdrakhmanova, S. A. Avetisyan, H. G. Avoyan, K. K. Azaryan, G. T. Aimakhanova, D. A. Ayypova, A. Ch. Akunov, M. K. Alieva, A. R. Almukhambedova, O. Yu. Aparkina, O. R. Aruslanova, E. Yu. Ashina, O. Yu. Badina, O. Yu. Barysheva, T. I. Batluk, A. S. Batchaeva, R. A. Bashkinov, A. M. Bitieva, I. U. Bikhteev, N. A. Borodulina, M. V. Bragin, V. A. Brazhnik, 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. Elena A. Galova, V. V. Genkel, D. B. Giller, E. D. Gordeychuk, E. I. Gorshenina, E. V. Grigoryeva, E. Yu. Gubareva, G. M. Dabylova, A. I. Demchenko, O. Yu. Dolgikh, M. Y. Duishobaev, D. S. Evdokimov, K. E. Egorova48, A. E. Zheldybaeva, N. V. Zarechnova, Yu. D. Zimina, S. Yu. Ivanova, E. Yu. Ivanchenko, M. V. Ilina, M. V. Kazakovtseva, E. V. Kazymova, Yu. S. Yuliya S. Kalinina, N. A. Kamardina, A. M. Karachenova, I. A. Karetnikov, N. A. Karoli, M. Kh. Karsiev, D. S. Kaskaeva, K. F. Kasymova, Zh. B. Kerimbekova, E. S. Kim, N. V. Kiseleva, D. A. Klimenko, O. V. Kovalishena, S. V. Kozlov, E. V. Kolmakova, T. P. Kolchinskaya, M. I. Kolyadich, O. V. Kondryakova, M. P. Konoval, D. Yu. Konstantinov, E. A. Konstantinova, V. A. Kordyukova, E. V. Koroleva, A. Yu. Kraposhina, T. V. Kryukova, A. S. Kuznetsova, T. Yu. Kuzmina, K. V. Kuzmichev, C. K. Kulchoroeva, T. V. Kuprina, I. M. Kuranova, L. V. Kurenkova, N. Yu. Kurchugina, N. A. Kushubakova, V. I. Levankova, A. A. Ledyaeva, T. V. Lisun, V. E. Lisyanskaya, N. A. Lyubavina, N. A. Magdeeva, K. V. Mazalov, V. I. Mayseenko, A. S. Makarova, A. M. Maripov, N. V. Markov, A. A. Marusina, E. S. Melnikov, A. I. Metlinskaya, N. B. Moiseenko, F. N. Muradova, R. G. Muradyan, Sh. N. Musaelyan, E. S. Nekaeva, N. M. Nikitina, S. E. Nifontov, E. Yu. Obolentseva, A. A. Obukhova, B. B. Ogurlieva, A. A. Odegova, Yu. V. Yuliya V. Omarova, N. A. Omurzakova, Sh. O. Ospanova, V. A/ Pavlova, E. V. Pakhomova, L. D. Petrov, S. S. Plastinina, D. A. Platonov, V. A. Pogrebetskaya, D. V. Polyakov, E. V. Ponomarenko, L. L. Popova, A. A. Potanin, N. A. Prokofieva, Yu. D. Rabik, N. A. Rakov, A. N. Rakhimov, N. A. Rozanova, S. Serikbolkyzy, Ya. A. Sidorkina, A. A. Simonov, V. V. Skachkova, R. D. Skvortsova, D. S. Skuridin, D. V. Solovieva, I. A. Solovieva, I. M. Sukhomlinova, A. G. Sushilova, D. R. Tagaeva, E. P. Tikhonova, D. S. Tokmin, A. A. Tolmacheva, M. S. Torgunakova, K. V. Trenogina, N. A. Trostyanetskaya, 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, I. V. Shavrin, O. A. Shevchenko, D. R. Shikhaliev, E. A. Shishkina, K. Yu. Shishkov, S. Yu Shcherbakov, G. V. Shcherbakova, E. A. Yausheva

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Application of the Gyrolab microfluidic platform to measure picomolar affinity of a PD-L1-binding Adnectin™ radioligand for positron emission tomography by Zhou Dai, Juhi Juneja, Lumelle Schneeweis, Daniel Cohen, Frank Marsilio, Paul Morin, Ruchira DasGupta

“…This work describes application of the Gyrolab solution affinity module and the new multi-curve analysis feature to determine affinity of the PD-L1 Adnectin™ positron emission tomography radioligand, which was measured as 20 pM for human PD-L1. …”
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Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework by Shafiq Ul Rehman, Uddalak Mitra

“…This dataset consists of clinical features and is designed to detect Alzheimer’s disease through handwriting analysis. …”
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Predicting Remaining Useful Life of Induction Motor Bearings from Motor Current Signatures Using Machine Learning by Nurul Zahirah Zulkifli, Bhukya Ramadevi, Kishore Bingi, Rosdiazli Ibrahim, Madiah Omar

“…<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>I</mi><mrow><mi>s</mi><mi>d</mi></mrow></msub><mo>−</mo><msub><mi>I</mi><mrow><mi>s</mi><mi>q</mi></mrow></msub></mrow></semantics></math></inline-formula> analysis identifies deviations in motor behavior, whereas FFT detects harmonics that indicate potential faults. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

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AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data by Mohd Zubair Akhtar, Maximilian Schmid, Gordon Elger

“…Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. …”
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Microsatélites amplificados al azar (RAM) en estudios de diversidad genética vegetal Random amplified microsatellites (RAM´s) in plant genetic diversity studies by Jaime Eduardo Muñoz Flórez, Ana Cruz Morillo Coronado, Yacenia Morillo Coronado

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Molecular classification of head and neck squamous cell carcinoma based on hypoxia-related genes and clinical significance of STC2 by ZHU Jianing, WANG Tiantian, ZHANG Rui, SONG Hongquan

“…HNSCC was classified into three subclasses (C1, C2, and C3), and the molecular characteristics and prognostic differences of the subclasses were assessed by comparing the tumor mutation load, functional enrichment analysis, drug sensitivity, and clinical features among the subclasses. …”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. …”
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SITA: Predicting site-specific immunogenicity for therapeutic antibodies by Yewei Cun, Hao Ding, Tiantian Mao, Yuan Wang, Caicui Wang, Jiajun Li, Zihao Li, Mengdie Hu, Zhiwei Cao, Tianyi Qiu

“…Furthermore, the SITA score can significantly distinguish immunogenicity levels of whole human antibodies, therapeutic antibodies and non-human-derived antibodies. More importantly, analysis of an additional 25 therapeutic antibodies revealed that over 70% of them were detected with decreased immunogenicity after modification compared to their parent variants. …”
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Implementation of Explainable Ai in Deep Learning Methods for Multiclass Classification of Plant Diseases in Mango Leaves by Menaka Radhakrishnan

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Increasing the Classification Achievement of Steel Surface Defects by Applying a Specific Deep Strategy and a New Image Processing Approach by Fatih Demir, Koray Sener Parlak

“…The Iterative Neighborhood Component Analysis (INCA) technique was chosen for distinguishing features to lower the computational cost. …”
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Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation by Sercan Yalcin, Zuhal Cetin Yalcin, Muhammed Yildirim, Bilal Alatas

“…Additionally, the MTA-CNN’s ability to accurately localize key facial landmarks provides valuable information for further analysis, including identifying abnormal structures or changes in anatomical features associated with ALL. …”
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Hybrid FFT-ARMA-Burg Modeling and LSTM-Enhanced BBO Optimization for Fault Diagnosis in Induction Motors by Khadidja Boudraa, Mohammed Assam Ouali, Mohamed Ladjal

“… Induction motors (IM) are crucial in industrial systems, and fault diagnosis reliably and effectively is of importance for maintaining efficiency. The detection of a broken rotor bar(RBB) is one of the most challenging tasks in condition monitoring due to the complexity of fault features in motor current signals. …”
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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). …”
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An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. …”
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Longitudinal Changes in Pitch-Related Acoustic Characteristics of the Voice Throughout the Menstrual Cycle: Observational Study by Jaycee Kaufman, Jouhyun Jeon, Jessica Oreskovic, Anirudh Thommandram, Yan Fossat

“…Ovulation was estimated using luteinizing hormone urine tests taken every morning. The analysis included comparisons of these features between the follicular and luteal phases and the application of changepoint detection algorithms to assess changes and pinpoint the day in which the shifts in vocal pitch occur. …”
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Association of exhaled breath volatile organic compounds with surgical traumatic stress by Qifeng Wang, Yuyi Zhao, Shikuo Li, Xuehan Li, Haiyan Wang, Yunxia Zuo

“…Differential mass spectral features between these time points were screened. Traumatic stress-related biomarker detection and mixed-effects model analysis were performed to define correlations and significance between parameters. …”
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