Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning‐enabled molecular diagnostics by Ariane Khaledi, Aaron Weimann, Monika Schniederjans, Ehsaneddin Asgari, Tzu‐Hao Kuo, Antonio Oliver, Gabriel Cabot, Axel Kola, Petra Gastmeier, Michael Hogardt, Daniel Jonas, Mohammad RK Mofrad, Andreas Bremges, Alice C McHardy, Susanne Häussler

“…Using these data types alone or in combination resulted in high (0.8–0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. …”
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Machine Learning Analysis of Umbilic Defect Annihilation in Nematic Liquid Crystals in the Presence of Nanoparticles by Ingo Dierking, Adam Moyle, Gabriele Maria Cepparulo, Katherine Skingle, Laura Hernández, Juhan Raidal

“…It is observed that the defect annihilation scaling exponents deviate from the theoretically predicted value of α = 1/2 when nanoparticles of varying size and concentration are introduced to the system. …”
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“CVCACS” MODEL FOR PREDICTION OF CARDIOVASCULAR COMPLICATIONS IN HOSPITALIZED PATIENTS WITH ACUTE CORONARY SYNDROME by E. T. Manyukova, M. A. Shalenkova, Z. D. Mikhailova

“…In order to facilitate the prediction values of CVC during ACS hospital period we have proposed a “CVCACS” model that employed the parameters of patient’s age, IL-10 level in the saliva, IL-6, and hs-CRP amounts in blood. …”
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A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults by Bridianne O’Dea, Philip J. Batterham, Taylor A. Braund, Cassandra Chakouch, Mark E. Larsen, Michael Berk, Michelle Torok, Helen Christensen, Nick Glozier

“…In all machine learning models, predicted values were weakly associated with actual values. …”
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Observation of ψ(3686) → Ξ − K S 0 Ω ¯ + $$ {\Xi}^{-}{K}_S^0{\overline{\varOmega}}^{+} $$ + c.c. by The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. K. Chen, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, W. T. Liu, X. Liu, X. Liu, X. Y. Liu, Y. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, F. M. Melendi, Y. H. Meng, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, T. Z. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, L. F. Tang, M. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, J. J. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. Wu, X. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, H. Y. Yan, L. Yan, W. B. Yan, W. C. Yan, W. P. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. H. Yang, Y. Q. Yang, Y. X. Yang, Y. Z. Yang, M. Ye, M. H. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, Y. J. Zeng, X. Y. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou, J. Zu

“…The ratio between B ψ 3686 → Ξ − K S 0 Ω ¯ + + c . c $$ {\mathcal{B}}_{\psi (3686)\to {\varXi}^{-}{K}_S^0{\overline{\varOmega}}^{+}+c.c} $$ and B ψ 3686 → Ω − K + Ξ ¯ 0 + c . c $$ {\mathcal{B}}_{\psi (3686)\to {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0+c.c} $$ is determined to be 1.05 ± 0.23 ± 0.14, which deviates from the isospin symmetry conservation predicted value of 0.5 by 2.1σ.…”
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Improve the Intelligent Convenience of Multivariate Optimization of Concrete Mix Ratio and the Development of Corresponding Applications by Zhanfei Yang, Bin Chen, Jianfen Zhou, Saihua Huang

“…The prediction and actual error of 7-day intensity and 28-day intensity are −14.4% to −6.1% and −17.6% to 0.6%, respectively, while the predicted value and actual slump error are 15%. The results show that the error between the measured value and the design value is basically within 15%, which meets the design requirements. …”
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Prediction of Influence of Environmental Factors on the Toxicity of Pentachlorophenol on <i>E. coli</i>-Based Bioassays by Sulivan Jouanneau, Gerald Thouand

“…This model was validated using a validation dataset and demonstrated a strong correlation between the experimental and predicted values (r² ≈ 0.9). Thus, this approach enables the effective prediction of PCP’s effects by accounting for environmental variations. …”
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Are NLR Rate and MPV Values Useful in Predicting Malignancy in Follicular Neoplasia, Atypia of Undetermined Significance and Suspicious Cytology? by Murat Doğan, Aykut Soyder

“…The aim of this study was to investigate the predictive values of these two parameters in detecting thyroid malignancy.Materials and Methods:Patients who were reported to have atypia of undetermined significance, follicular neoplasia and suspected cytology as a result of thyroid fine needle aspiration biopsy (FNAB) in a tertiary health care facility between January 2010 and December 2017 and who had undergone total thyroidectomy or hemithyroidectomy due to this were evaluated. …”
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Methods of creating and using a digital twin of a mobile transport and transshipment rope complex by I. A. Lagerev, V. I. Tarichko, A. V. Panfilov

“…To do this, the actual value of the load suspension point coordinate obtained through the video stream processing method was compared to the predicted value calculated using a digital twin.Discussion and Conclusions. …”
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FORECASTING VALUES OF CHROMATICITY OF DRINKING AND SOURCE WATERS USING ARIMA MODEL AND NEURAL NETWORK by D. V. Makarov, E. A. Kantor, N. A. Krasulina, A. V. Greb, Z. Z. Berezhnova

“…It was revealed that ANN allows to obtain the predicted values of colour of water more accurate than ARIMA-model.Main conclusions. …”
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Nonlinear quenching of excitonic emission from nanoplatelet films at high excitation densities by Simon Jessen, Alessio Di Giacomo, Iwan Moreels, Brian Julsgaard, Rosana M. Turtos

“…Despite this, light yield estimations based on a simulated distribution of excitation densities predict values upwards of 2000 ph/MeV, while showing ample room for improvement and the future potential of surpassing the 10 ph/MeV/ps benchmark.…”
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Deep analysis of chemically treated Jute/Kenaf and glass fiber reinforced with SiO2 nanoparticles by utilizing RSM optimization by S. Jothi Arunachalam, R. Saravanan, T. Sathish

“…Experimental results closely matched predicted values, affirming the model's accuracy. The study found that silane concentration had a significant effect on the flexural and hardness properties. …”
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Géostratégie et considérations écologiques pour la gestion des feux de végétation en Afrique de l’Ouest by Bareremna Afelu, Kombate Bimare, Tionhonkélé Drissa Soro, Somiyabalo Pilabina, Komlan Edou, Marra Dourma, Kouami Kokou

“…Spatio-temporal distribution shows an upward trend in number of active fires from 7,843 in 2000 to 11,994 in 2023 with a predicted value of 18,518 in 2100. Based on the optimistic option of the RCP2.6 hypothesis which assumes that the various mitigation measures included in the national contributions is honored, the predictions show a reduction in the occurrence of fires with reference to the 2000 situation. …”
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An Observational Study on Pre-natal Diagnosis of Congenital Talipes Equinovarus by Gaurav Vatsa, Saurabh Suman, Siddharth Kumar Singh

“…Diagnostic accuracy metrics, including sensitivity, specificity, and predictive values, were calculated. Results: Among live births, pre-natal US identified CTEV cases, with final confirmation distinguishing structural from positional deformities. …”
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Ranitidine Loaded Biopolymer Floats: Designing, Characterization, and Evaluation by Abdul Karim, Muhammad Ashraf Shaheen, Tahir Mehmood, Abdul Rauf Raza, Musadiq Aziz, Badar Din

“…The resemblance of observed and predicted values indicated the validity of derived equations for evaluating the effect of independent variables while kinetic study demonstrated that the applied models are feasible for evaluating and developing float for RNT.…”
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ALGORITHM FOR ASSESSING TIME AND COST RISKS AT ENTERPRISES OF THE MILITARY-INDUSTRIAL COMPLEX by N.D. Pechalin, A.G. Finogeev

“…The risk analysis algorithm solves the problems of assessing the time and cost parameters of the project task for compliance with the predicted values at an early stage of the production cycle. …”

Investigating the correlation between ultrasonic pulse velocity and compressive strength in polyurethane foam concrete by R. Roobankumar, M. SenthilPandian

“…The empirical relationships between compressive strength and UPV were found to be exponential, with high correlation values ranging from 0.9012 to 0.9998. The predicted values and the experimentally measured results were compared in order to confirm the accuracy of the empirical equations for compressive strength prediction.…”
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POWER DOPPLER IMAGING IN PATIENTS WITH SUSPECTED PROSTATE CANCER by T. V. Shatylko, L. N. Sedova, A. Yu. Korolev

“…Sensitivity of power Doppler imaging was 82.4%, specificity was 70.5%, positive and negative predictive values were 66.7% and 72.3% correspondingly. …”
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Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah by Maryam Hafezparast, Sadaf Gord, Rasool Ghobadian

“…The SCS method was implemented for the flood on 03/04/2019 and it showed that the occurred flood is equivalent to a 25-year flood in this region. The predicted values estimated a lower discharge than the soil conservation service (SCS) method. …”
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A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX) by Jong-Hwan Jang, Yong-Yeon Jo, Sora Kang, Jeong Min Son, Hak Seung Lee, Joon-myoung Kwon, Min Sung Lee

“…In contrast, the proposed framework explores “what-if” scenarios, generating counterfactual ECGs that increase or decrease a model’s predictive values. This approach has the potential to increase clinicians’ trust specific changes—such as increased T wave amplitude or PR interval prolongation—influence the model’s decisions. …”
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