Application of the generalized point source method for solving boundary value problems of mathematical physics by Sergey Yu. Knyazev, Elena E. Shcherbakova

“…From this equation, the transition to an inhomogeneous integral equation with the kernel expressed by the known fundamental solution is carried out. …”
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Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations by Anthony Shanks, Ben Steckler, Sarah Smith, Debra Rusk, Emily Walvoord, Erin Dafoe, Paul Wallach

“…PURPOSE With the transition of USMLE Step 1 to Pass/Fail, Step 2 CK carries added weight in the residency selection process. …”
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Multi-heat keypoint incorporation in deep learning model to tropical cyclone centering and intensity classifying from geostationary satellite images by Thanh-Ha Do, Son-The Phan, Duc-Tien Du, Dinh-Quan Dang, Khanh-Hung Mai, Lars R. Hole

“…Compared to a typical detecting object problem, the main issues come from the complexity of TC cloud patterns, which are nonlinear with actual TC grades or discrimination between TC grades (transition between TD to Tropical Storm (TS), TS to STS, and upgrading and progress of TCs). …”
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Technological Advancements and Economic Growth as Key Drivers of Renewable Energy Production in Saudi Arabia: An ARDL and VECM Analysis by Faten Derouez

“…These insights provide valuable implications for policymakers and stakeholders aiming to accelerate the transition to renewable energy in Saudi Arabia.…”
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Food Security–Renewable Energy Nexus: Innovations and Shocks in Saudi Arabia by Nourah A. Althani, Raga M. Elzaki, Fahad Alzahrani

“…We use the time series annual data covering the period (2000–2022) analyzed by applying the Vector Autoregressive (VAR) model system and its environment, Granger causality, the forecast-error variance decompositions (FEVD), and the impulse response functions (IRFs). …”
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A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems by Ryan Varga, Steven J. Cavazos, Elizabeth S. Sooby, Markus H. A. Piro, Bernard W. N. Fitzpatrick

“…The following phase transition temperatures are reported for Ce-C with an error of ±32.7 °C: 20 mol%C—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>878.6</mn></mrow></semantics></math></inline-formula> °C; 32 mol%C—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>714.7</mn></mrow></semantics></math></inline-formula> °C and 857 °C; 67 mol%C—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1138.7</mn></mrow></semantics></math></inline-formula> °C.…”
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Machine learning-based pseudo-continuous pedotransfer function for predicting soil freezing characteristic curve by Sangyeong Park, Yongjoon Choe, Hangseok Choi, Khanh Pham

“…Significant reductions in root mean square error and mean absolute error of 42% and 55%, respectively, demonstrated the superiority of the XGB-PTF. …”
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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies by Yingchun He, Yi-haw Jan, Fan Yang, Yunru Ma, Chun Pei

“…Spearman’s rank correlation analysis, intraclass correlation coefficient (ICC), error analysis, and t-test were conducted to compare the consistency of data obtained from the two movement capture systems, including the peak frequency of acceleration, transition time point between movement statuses, and the complexity index average (CIA) of the movement status based on multiscale entropy analysis.The KR algorithm showed excellent consistency (ICC1,3=0.988) between the two methods when estimating the peak acceleration frequency. …”
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Improved flow prediction of non-ideal sonic nozzles by CFD-based surrogate modelling by Sebastian Weiss, Bodo Mickan, Kilian Oberleithner, Markus Bär, Sonja Schmelter

“…The flow predictions of the surrogate model for the aforementioned parameterised contours are in much better agreement with experimental data for the respective test nozzle contour, exhibiting an average root mean square error of 0.17% over all test nozzles compared to the associated standard, with an average root mean square error of 0.35%. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…The fingerprints are readily interpretable and have demonstrated promising efficacy in addressing influences related to the photophysics of organic light‐emitting materials, achieving high accuracy and precision in the regression of emission transition energy (mean absolute error (MAE) ∼ 0.13eV) and the classification of optical features and excited state dynamics mechanisms (F1score ∼ 0.94). …”
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DMP-PUNet: A novel network for two-dimensional InSAR phase unwrapping under severe noise and complex fringes conditions by Yu Chen, Shuai Wang, Yandong Gao, Yanjian Sun, Jinqi Zhao, Kun Tan, Peijun Du

“…For simulated data, DMP-PUNet achieved an overall average mean absolute error (MAE) in residuals of 0.221 rad, improving accuracy by 54.75 % with an average processing time of 0.81 s. …”
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Use of artificial neural network for optimization of irreversibility analysis in radiative Cross nanofluid flow past an inclined surface with convective boundary conditions by Ali Farhan, Zaib Aurang, Zafar Syed Sohaib, Khan Umair, Ahmed Muhammad Faizan, Elattar Samia, Khashi’ie Najiyah Safwa

“…To evaluate the correctness of the proposed model, the results of training, testing, and validation are analyzed using the performance charts, error histograms, transition state analysis, comparisons between bvp4c and ANN, and regression plots. …”
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Explosion characteristics and overpressure prediction of hydrogen-doped natural gas under ambient turbulence conditions by Ranran Li, Zhongmo Xu, Mingzhi Li, Shuhong Li, Zhenyi Liu, Zihao Xiu, Qiqi Liu

“…The proposed model achieves a root mean square error of 0.140 kPa under various wind speed conditions, demonstrating good predictive accuracy.…”
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Development and Validation of Bioanalytical LC–MS/MS Method for Pharmacokinetic Assessment of Amoxicillin and Clavulanate in Human Plasma by Sangyoung Lee, Da Hyun Kim, Sabin Shin, Jee Sun Min, Duk Yeon Kim, Seong Jun Jo, Ui Min Jerng, Soo Kyung Bae

“…The negative electrospray ionization modes were utilized to monitor the transitions of <i>m</i>/<i>z</i> 363.9→223.1 (amoxicillin), <i>m</i>/<i>z</i> 198.0→135.8 (clavulanate), and <i>m</i>/<i>z</i> 285.0→185.8 (4-hydroxytolbutamide). …”
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Measurement of the dynamic axial load-share ratio in vivo could indicate sufficient callus healing in external fixators by Xuefei Fu, Sida Liu, Na Wang, Yi Ji, Lin Lu, Tao Chen, Mingyong Gu, Zhiwei Chai, Defu Yu, Yancheng Liu, Jun Miao

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Degradation and Migration Characteristics and Water‐Inrush Mechanism of Overburden Roof Under High‐Intensity Mining by Min Cao, Shangxian Yin, Xu Wang, Shuqian Li, Qixing Li, Yulong Di

“…The relative and absolute errors of the prediction results from both models were compared. …”
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Automatic pelvic fracture segmentation: a deep learning approach and benchmark dataset by Yanzhen Liu, Sutuke Yibulayimu, Gang Zhu, Chao Shi, Chendi Liang, Chunpeng Zhao, Xinbao Wu, Yudi Sang, Yu Wang, Yu Wang

“…The traditional manual slice-by-slice segmentation by surgeons is time-consuming, experience-dependent, and error-prone. The complex anatomy of the pelvic bone, the diversity of fracture types, and the variability in fracture surface appearances pose significant challenges to automated solutions.MethodsWe propose an automatic pelvic fracture segmentation method based on deep learning, which effectively isolates hipbone and sacrum fragments from fractured pelvic CT. …”
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Study on the evolution of regional future drought-flood abrupt alternation events by Yanqi Wei, Haichao Li, Yuliang Zhou, Nii Amarquaye Commey, Juliang Jin, Ping Zhou

“…Study region: Bengbu City, situated on the transition zone between southern and northern China. …”
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Cyber epidemic spread forecasting based on the entropy-extremal dynamic interpretation of the SIR model by Viacheslav Kovtun, Krzysztof Grochla, Mohammed Al-Maitah, Saad Aldosary, Tetiana Gryshchuk

“…This article proposes a transition to a stochastic interpretation of the controlled parameters of the SIR model and the introduction of additional stochastic parameters that represent the variability of operational data measurements. …”
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AI-driven design: powered by large language model and intelligent computation by Guodong Sa, Zhinan Li, Zhenyu Liu, Jianrong Tan

“…Conventional experience-driven design approaches exhibit fundamental limitations when confronting high-dimensional parameter spaces, complex multidisciplinary coupling effects, and dynamic performance prediction requirements, rendering trial-and-error iterative optimization processes inefficient and incapable of achieving optimal solutions. …”
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