Research progress on shale gas productivity evaluation: concepts, methods and future directions by ZHU Suyang, PENG Zhen, DI Yunting, PENG Xiaolong, LIU Dongchen, GUAN Wenjie

“…In addition, further work is needed to incorporate mechanism-informed constraints into machine learning algorithms, enhance model transparency through causal inference, and improve interpretability. …”
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Determinação do coeficiente convectivo de transferência de calor de figos submetidos ao resfriamento rápido Determination of the convective heat transfer coefficient of fig fruits... by Mariangela Amendola, Saul Dussán-Sarria, Anderson A. Rabello

“…Algorithms employing the finite differences and finite elements methods were implemented for the one-dimensional and three-dimensional models. …”
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Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors by Yu-Chen Liu, Ye-Hai Liu, Hai-Feng Pan, Wei Wang

“…Second, we trained ensemble machine learning (ML) algorithms that incorporated these factors, with Shapley Additive exPlanations (SHAP) value analysis quantifying predictor importance. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Diabetes, atrial fibrillation, smoking habits, hypertension, age, and viral load demonstrated a high likelihood of association with stroke in PLWH and should be prioritized when constructing clinical prediction algorithms for HIV-related stroke. Conclusions The most important factors were hypertension and chronic kidney disease, followed by smoking, dyslipidemia, diabetes, HCV, HBV, CD4 count, use of ART, TB, and substance use (cocaine). …”
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Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis by Dan Liu, Tingting Li, Li Du, Jianqiao Zheng, Yujie Huang, Weiyi Zhang

“…Nevertheless, the diagnostic performance of AI algorithms for difficult airway assessment remains unclear due to the small sample sizes, insufficient image acquisition standards and poor predictive accuracies. …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Results 157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. …”
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Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses by Huanyi Zhao, Zheng Jin, Junlong Li, Junfeng Fang, Wei Wu, J. F. Fang

“…Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. …”
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Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune by Zheng Zhu, Bizhi Tu, Cheng Peng, Xun Xu, Peizhi Lu, Rende Ning

“…The Venn diagram, random forest, SVM-RFE algorithm, and multivariate analysis were employed to determine SID-related hub genes in OA synovium. …”
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A deep learning software tool for automated sleep staging in rats via single channel EEG by Andrew Smith, Snezana Milosavljevic, Courtney J. Wright, Charlie A. Grant, Ana Pocivavsek, Homayoun Valafar

“…The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. …”
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Block Markov superposition transmission of LDPC codes by Qianfan Wang, Kongjing Guo, Xiao Ma

“…For decoding, the sliding window decoding algorithm with sum-product or min-sum implementations can be employed, inheriting a relatively low-latency decoding. …”
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Exothermic thermosolutal convection in a nanofluid-filled square cavity with a rotating Z-Fin: ISPH and AI integration by Kuiyu Cheng, Abdelraheem M. Aly, Nghia Nguyen Ho, Sang-Wook Lee, Andaç Batur Çolak, Weaam Alhejaili

“…The ANN model, employing the Levenberg–Marquardt algorithm, achieved a coefficient of determination $ R = 0.99994 $ and a mean squared error $ \text{MSE} = 4.21\times {10}^{-6} $, demonstrating its reliability in predicting thermal performance. …”
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Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference Suppression by Yuanzhe Gong, Mohammadhossein Karimi, Tho Le-Ngoc

“…The model achieves a phase error of less than 0.021 radians and a magnitude error of 0.17 dB in the predicted feeding weights. The resulting near-field beam patterns using the LCMV-based vector and the DNN-predicted vector show good agreement.…”
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Large language models for accurate disease detection in electronic health records: the examples of crystal arthropathies by Kim Lauper, Denis Mongin, Nils Bürgisser, Samia Mehouachi, Clement P. Buclin, Delphine S. Courvoisier, Etienne Chalot

“…The framework was further validated on 600 paragraphs assessing ‘Calcium Pyrophosphate Deposition Disease (CPPD)’.Results The LLM-based algorithm outperformed the regex method, achieving a 92.7% (88.7%–95.4%) positive predictive value, a 96.6% (94.6%–97.8%) negative predictive value and an accuracy of 95.4% (93.6%–96.7%) for gout. …”
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VAS value set still has its own application value compare with TTO value set of EQ-5D-3L in Chinese population by Lin Zhuo, Siyuan Gao, Rui Jin, Lang Zhuo, Xiuying Wang

“…Conclusions We recommend VAS value set, Model 2, as the reference of scoring algorithm when using EQ-5D-3L in large scale survey of Chinese population.…”
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What are the implications of using individual and combined sources of routinely collected data to identify and characterise incident site-specific cancers? a concordance and valida... by Rachael Williams, Helen Strongman, Krishnan Bhaskaran

“…We calculated positive predictive values and sensitivities of each definition, compared with a gold standard algorithm that used information from all linked data sets to identify first cancers. …”
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Hybrid intelligence framework for optimizing shear capacity of lightweight FRP-reinforced concrete beams by Iman Faridmehr, Moncef L. Nehdi, Mohammad Ali Sahraei, Kiyanets Aleksandr Valerievich, Chiara Bedon

“…Multi-objective optimization was deployed to integrate Genetic Algorithms (GA) and fmincon to optimize beam parameters for maximizing the shear capacity, Vc. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…The study considered 17 factors in four primary categories: topographic, climatic, soil, and land use/land cover (LULC). The Boruta algorithm assessed the importance of these variables. …”
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Uncertainty analysis of clinically relevant distances derived using photogrammetric intersection for the assessment of motor-speech-control in children by L. Boyle, P. Helmholz, R. Ward, R. Palmer, D. Lichti

“…In contrast, photogrammetry can predict facial inter-landmark distances and their uncertainties through intersection and variance propagation. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. …”
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Deep learning based screening model for hip diseases on plain radiographs. by Jung-Wee Park, Seung Min Ryu, Hong-Seok Kim, Young-Kyun Lee, Jeong Joon Yoo

“…Four different models were designed-raw image for both training and test set, preprocessed image for training but raw image for the test set, preprocessed images for both sets, and change of backbone algorithm from DenseNet to EfficientNet. The deep learning models were compared in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the receiver operating characteristic curve (AUROC).…”
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