Strengthening of Damaged Masonry Walls Using Engineered Cementitious Composites: Experimental and Numerical Analysis by S. M. Renuka, I. P. Mervin Sanjith

“…The suggested models were shown to be capable of predicting the common behaviour of masonry units.…”
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Experimental methodology for the quantification of crack tip plastic zone and shape from the analysis of displacement fields by J.M. Vasco-Olmo, F.A. Díaz, M.N. James, C.J. Christopher, E.A. Patterson

“…This analysis indicated that the CJP model provided the most accurate prediction of the plastic zone and shape.…”
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A material design guideline for self-assembled vertically aligned nanocomposite thin films by Jiawei Song, Haiyan Wang

“…These complexities pose challenges in the designs, materials selection, and prediction of the resulted VAN morphologies and properties. …”
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Transcranial Alternating Current and Random Noise Stimulation: Possible Mechanisms by Andrea Antal, Christoph S. Herrmann

“…As a result, fine-grained models of the human skull and brain allow the prediction of the exact pattern of current flow during tDCS and tACS. …”
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Development of a Regional F‐Region Critical Frequency Model for Southern Africa by S. Brijraj, P. J. Cilliers, M. Kosch

“…The model improves on the results of the Ionospheric Communications Enhanced Profile Analysis and Circuit Prediction Program in terms of the root mean square error skill score metric. …”
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Machine Learning-Based Probabilistic Seismic Demand Model of Continuous Girder Bridges by Wenshan Li, Yong Huang, Zikai Xie

“…The results indicated that random forest (RF) is the most suitable model to predict the longitudinal and transverse curvature at the bottom of the four piers from the coefficients of determination. …”
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Influence of Hole Arrangement on the Section of Cavity Formed by Cutting Blast by Xianglong Li, Qiang Li, Jianguo Wang, Zichen Wang, Hao Wang, Zihao Tao

“…The difference in contour area, contour width, and contour and contour height between the measured value and the simulation result is 5.3%, 3.3%, and 3.4%, respectively, indicating that the combination of theoretical calculation and numerical simulation is suitable for prediction of cavity section after blast in tunnel excavation.…”
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Salient object detection with non-local feature enhancement and edge reconstruction by Tao Xu, Jingyao Jiang, Lei Cai, Haojie Chai, Hanjun Ma

“…However, the existing methods struggle to capture long-range dependencies and edge information in complex images, which hinders precise prediction of salient objects. To this end, we propose a salient object detection method with non-local feature enhancement and edge reconstruction. …”
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Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method by Jing Gan, Qing Su, Linheng Li, Yanni Ju, Linchao Li

“…This study introduces an innovative spatial correlation matrix, termed the road matrix, which incorporates shared road lengths between grids to enhance accident prediction accuracy. The model examines the relationship between traffic accidents and various predictor variables, including land use, road networks, and public transportation facilities. …”
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Quantitative analysis and resolution of pharmaceuticals in the environment using multivariate curve resolution-alternating least squares (MCR-ALS) by Mostafa Ahmed, Shaaban Heba

“…Both models showed comparable and satisfactory results with the relative error of prediction of real water samples in the range of 1.70–9.75 % and 1.64–9.43 % for MCR-ALS and PLSR, resp. …”
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In-situ EBSD observation of structure evolution in tensile deformation of NiCrMoV high-strength steel at room temperature by Guangyuan Yang, Xianglong Meng, Zhiyong Gao

“…The research on the deformation mechanisms of the NiCrMoV high-strength steel can help gain work hardening parameters applicable to this material, providing theoretical guidance for predicting the forming of parts with complex shapes.…”
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Machine learning-powered, high-affinity modification strategies for aptamers by Gubu Amu, Xin Yang, Hang Luo, Sifan Yu, Huarui Zhang, Yuan Tian, Yuanyuan Yu, Shijian Ding, Yufei Pan, Zefeng Chen, Yixin He, Yuan Ma, Baoting Zhang, Ge Zhang

“…After four rounds of interactive training (a total of 422 modified aptamer-target affinity datasets with diverse modification types and sites), an antifcial intelligence model with high predictive accuracy with a correlation coefficient of 0.82 between the predicted and actual binding affinities was obtained. …”
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Design Synthesis and Biological Evaluation of Novel N-Nitro Acid Amide Derivatives as Lead Compounds of Herbicide by Xiaojuan Qi, Wenjie Tang, Shan Gao, Min Gao, Changshui Chen, Qingye Zhang

“…Meanwhile, the possible reasons for the lower activity of compounds were analyzed by molecular docking prediction.…”
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In Vitro Evaluation of the Cytotoxic Potential of Environmental Contaminant Mixtures Present in Water for Human Use by Israa J. Hakeem, Ngiste Amare

“…However, once the individual compounds’ respective mechanisms of action have been established, the toxicity caused by the mixtures can be qualitatively predicted.…”
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Risk assessment and prevention in airport security assurance by integrating LSTM algorithm. by Yao Hu, Liguang Qiao, Feng Gu

“…The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. The predicted data was highly consistent with the actual data. …”
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Analysis of Long-term Water Quality up to the Standard in Jinchuan River Basin Based on Hydrodynamic and Water Quality Model by CHEN Liming, YU Xin, JIN Zhe, CHEN Chen, CHEN Liangang, WANG Gaoxu

“…The water quality of the Jinchuan River Basin cannot reach the standard steadily all year round.Thus,after analyzing the pollution characteristics of the basin,this paper proposes three schemes for water quality that can reach the standard in the long term,considering the improvement in the treatment,water supplement,and sewage interception abilities in sewage treatment plans of this basin.Meanwhile,prediction and analysis are also conducted on the water quality improvement effects of each plan.The results are as follows.In current pollution source conditions,regarding the two schemes where sewage treatment efficiency is increased by 10% and the basin water supplement rises by 50% on this basis,the peak concentration of ammonia nitrogen decreases,whereas the concentration is still higher than the Class V water standard.In the third scheme where the inflow concentration can achieve the assessment objectives of a south sewage treatment plant,the pollution load of pollutants into the river is reduced by about 44.4%.Therefore,the ammonia nitrogen concentration in the river network is significantly decreased,and the ammonia nitrogen concentration in the Baotaqiao section of downstream is reduced to 1.85 mg/L.Its water quality can meet the long-term water quality up to the standard in the whole process.The results can provide an effective scientific basis for water pollution control and prevention in the Jinchuan River Basin.…”
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Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast by Yueya Wang, Haobo Li, Xiaoming Shi, Jimmy C. H. Fung

“…This study investigates the influence of cumulus parameterizations and turbulence models, including the Reconstruction and Nonlinear Anisotropy (RNA) turbulence scheme, on precipitation prediction in multiple typhoon cases. Incorporating the cumulus and RNA schemes increases domain‐averaged precipitation, improves recall scores, and lowers relative error across various precipitation thresholds, which is substantial in three out of four studied typhoon cases. …”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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Parameter uncertainties for imperfect surrogate models in the low-noise regime by Thomas D Swinburne, Danny Perez

“…As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. …”
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Laboratory Investigation of Fiber Bragg Grating Strain Sensors for Semirigid Base Asphalt Pavements by Jiancun Fu, Aiqin Shen, Huaizhi Zhang, Tuanwei Sun

“…The SCs remained stable when the moduli of the embedded materials were high enough; otherwise, the SCs varied. Two SC prediction models that used the modulus of the embedded material as the only independent variable were developed to deal with the problem of instability. …”
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