Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing by Zhiyuan Ren, Shijie Zhou, Dong Liu, Qihe Liu

“…The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72</mn><mo>%</mo></mrow></semantics></math></inline-formula> cost reduction compared to FEM in high-dimensional spaces (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.01</mn><mo>,</mo><mi>n</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula> benchmarks). …”
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Study of the interval width of features for improving forecasting efficiency of the transport flow intensity by I. N. Pugachev, N. G. Sheshera, D. Yе. Grigorov

“…For a comprehensive characterization, additional analysis was necessary to conduct of the remaining independent features. New algorithmic structures were created using the Python programming language, in which the established feature interval ranges were sequentially compared in such a way as to process all possible combinations. …”
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Accuracy and consequences of using trial-of-antibiotics for TB diagnosis (ACT-TB study): protocol for a randomised controlled clinical trial by Neil French, Peter MacPherson, Titus Henry Divala, Katherine L Fielding, Marriott Nliwasa, Chikondi Charity Kandulu, Lingstone Chiume, Sanderson Chilanga, Masiye John Ndaferankhande

“…Standard diagnostic algorithms include ‘trial-of-antibiotics’—empirical antibiotic treatment given to mycobacteriology-negative individuals to treat infectious causes of symptoms other than tuberculosis, as a ‘rule-out’ diagnostic test for tuberculosis. …”
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Fatty acid metabolism prognostic signature predicts tumor immune microenvironment and immunotherapy, and identifies tumorigenic role of MOGAT2 in lung adenocarcinoma by Denggang Fu, Biyu Zhang, Wenyan Fan, Fanfan Zeng, Fanfan Zeng, Jueping Feng, Xin Wang

“…Tumor immune microenvironment (TIME) was analyzed using ESTIMATE and multiple deconvolution algorithms. Functional assays, including CCK8, cell cycle, apoptosis, transwell, and wound healing assays, were performed on MOGAT2-silenced H1299 cells using CRISPR/Cas9 technology.ResultsLow-risk group patients exhibited decreased OS. …”
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Drug-induced second tumors: a disproportionality analysis of the FAERS database by Shupeng Chen, Yuzhe Zhang, Xiaojian Li, Nana Tang, Yingjian Zeng

“…A total of 76 drugs were identified through four disproportionality algorithms (ROR, PRR, MGPS, BCPNN), with a minimum time to drug-induced tumor occurrence of 5 years, exhibiting an early failure-type curve. …”
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Screening of potential markers for vitiligo based on bioinformatics and LASSO regression and prediction of Chinese medicine by Wei Liang, Minni Huang, Yue Sun, Shuyu Guan

“…Nineteen core genes were screened out from the five algorithms of Cytohubba, and LASSO regression analysis further determined four key genes (IL7R, GZMH, CD3G, and UBD). …”
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Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech by Julianna Olah, Win Lee Edwin Wong, Atta-ul Raheem Rana Chaudhry, Omar Mena, Sunny X. Tang

“…Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. …”
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Bioinformatics-based analysis of autophagy-related genes and prediction of potential Chinese medicines in diabetic kidney disease by Yufeng Xing, Zining Peng, Chaoyang Ye

“…Subsequently, the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were adopted to select autophagy-related genes. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.…”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

“…In the neural network models, option prices were predicted using Python and its machine learning algorithms. Finally, the predicted prices from the models were compared with the market prices of the same options. …”
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Método integral configurable y flexible de ensayo de materiales consumibles de soldadura por arco eléctrico. // Integral, flexible and shaped method for electric arc welding consum... by A. García Rodríguez

“…It is also possible to obtain electric<br />arc parameters data bases useful for search, development and valuation of methods and algorithms for real time monitoring of welding<br />quality during fixed technologic process of electric arc welding.…”
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Epidemiological association of the COVID-19 pandemic on Mycoplasma pneumoniae infections in children in Tianjin, China: a single-centre retrospective study (2017–2024) by Wei Wang, Lu Wang, Run Guo, Yun Bo, Yingxue Zou, Hualei Cui

“…These temporal and demographic patterns emphasise the necessity of incorporating epidemiological surveillance into diagnostic algorithms for childhood respiratory infections, particularly in post-pandemic settings. …”
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Fast Global and Local Semi-Supervised Learning via Matrix Factorization by Yuanhua Du, Wenjun Luo, Zezhong Wu, Nan Zhou

“…This allows it to be optimized quickly using state-of-the-art unconstrained optimization algorithms. The computational complexity of the proposed method is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mi>m</mi><mi>d</mi><mo>)</mo></mrow></semantics></math></inline-formula>, which is much lower than that of the original symmetric matrix factorization, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><msup><mi>n</mi><mn>2</mn></msup><mi>d</mi><mo>)</mo></mrow></semantics></math></inline-formula>, and even lower than that of other anchor-based methods, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mi>m</mi><mi>d</mi><mo>+</mo><msup><mi>m</mi><mn>2</mn></msup><mi>n</mi><mo>+</mo><msup><mi>m</mi><mn>3</mn></msup><mo>)</mo></mrow></semantics></math></inline-formula>, where <i>n</i> represents the number of samples, <i>d</i> represents the number of features, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>≪</mo><mi>n</mi></mrow></semantics></math></inline-formula> represents the number of anchors. …”
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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews by He-Li Xu, Ting-Ting Gong, Xin-Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hong-Zan Sun, Xiao-Han Li, Yu-Hong Zhao, Song Gao, Qi-Jun Wu

“…ResultsIn a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). …”
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The Prognostic Significance of Sleep and Circadian Rhythm for Myocardial Infarction Outcomes: Case-Control Study by Wei-Chih Chin, Pao-Hsien Chu, Lung-Sheng Wu, Kuang-Tso Lee, Chen Lin, Chien-Te Ho, Wei-Sheng Yang, I-Hang Chung, Yu-Shu Huang

“…The use of actigraphy with different algorithms to evaluate sleep and circadian rhythm after acute MI has the potential for predicting outcomes and preventing future disease progression. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. …”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…The use of deep learning and neural network algorithms enhances the accuracy of these predictions by capturing complex data trends over time. …”
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Linking Immunological Parameters and Recovery of Patient’s Motor and Cognitive Functions In The Acute Period of Ischemic Stroke by A. M. Tynterova, N. N. Shusharina, A. M. Golubev, E. M. Moiseeva, L. S. Litvinova

“…Lab tests included assessment of plasma concentrations of CXC and CC subfamilies of cytokines, interleukins and TNF-a on Day 2 of hospital stay. Machine learning algorithms, the Python programming language, the Pandas and SciPy libraries, and discriminant analysis were used for statistical processing.Results. …”
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Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases by Benjamin Böttcher, Marly van Assen, Roberto Fari, Philipp L. von Knebel Doeberitz, Eun Young Kim, Eugene A. Berkowitz, Felix G. Meinel, Carlo N. De Cecco

“…A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment. CBIR algorithms benefit from the input of multiple regions of interest per ILD case. …”
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Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review by Mehrdad Rahsepar Meadi, Tomas Sillekens, Suzanne Metselaar, Anton van Balkom, Justin Bernstein, Neeltje Batelaan

“…The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of “black box” algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers’ jobs (n=16, 15.8%). …”
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