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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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. 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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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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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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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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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Study on cutting force in laser-assisted ultrasonic elliptical vibration machining (LUEVM) of high volume SiCp/Al composites by Peicheng Peng, Tian Tian, Heshuai Yu, Daohui Xiang, Ke Niu, Wei Gao, Yanqin Li, Zhaojie Yuan, Guofu Gao

“…The results show that the predicted value matches the experimental value well, and the maximum error is 18.2 %. …”
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A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting by Zongxi Qu, Kequan Zhang, Jianzhou Wang, Wenyu Zhang, Wennan Leng

“…In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. …”
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Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

“…The research findings indicate that the ML model provides satisfactory prediction values with an R2 value of 0.9043, root mean square error of 48.5147 and mean absolute percentage error of 0.0484. …”
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Methodology Based on BERT (Bidirectional Encoder Representations from Transformers) to Improve Solar Irradiance Prediction of Deep Learning Models Trained with Time Series of Spati... by Llinet Benavides-Cesar, Miguel-Ángel Manso-Callejo, Calimanut-Ionut Cira

“…In this study, we propose a novel end-to-end methodology for solar irradiance forecasting that starts with the search for the data and all of the preprocessing operations involved in obtaining a quality dataset, continuing by imputing missing data with the BERT (Bidirectional Encoder Representations from Transformers) model, and ending with obtaining and evaluating the predicted values. This novel methodology is based on three phases; namely, Phase_1, related to the acquisition and preparation of the data, Phase_2, related to the proposed imputation with a BERT model, and Phase_3, related to the training and prediction with new models based on deep learning. …”
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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. …”
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