Browse Articles
Discover research articles across all indexed journals
Longitudinal patterns of attention-deficit/hyperactivity disorder children in Shanghai, China
Comparative effects of sensory motor and virtual reality interventions to improve gait, balance and quality of life MS patients
Phase transition and bandgap modulation in TiO2 nanostructures for enhanced visible-light activity and environmental applications
These are the top companies and countries for industry research
Long term efficacy of recession and resection for abducens nerve palsy with targeting overcorrection of 5 prism diopters
Reprogramming site-specific retrotransposon activity to new DNA sites
Publisher Correction: Immunohistochemical detection of 5-hydroxymethylcytosine as a prognostic biomarker in non-WNT/Non-SHH medulloblastomas
The expanding repertoire of ESCRT functions in cell biology and disease
Attention-based hybrid deep learning model with CSFOA optimization and G-TverskyUNet3+ for Arabic sign language recognition
Three science-led start-ups making their mark
AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete
Abstract Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R2 > 0.90) outperformed ANN, RF, and GB models. Water, superplasticizer, curing temperature, and fiber content emerged as the most significant controlling parameters affecting early-age CS. The analysis of the interaction among the significant input variables and early-age CS suggests recommended inclusion levels for optimal performance. Specifically, it is recommended that the water content be maintained between 145 and 155 kg/m2, the superplasticizer content between 30 and 40 kg/m2, and the fiber content exceed 200 kg/m2. These recommendations are aimed at achieving desirable early-age CS characteristics. The overall findings reveal that the AI models can effectively improve the monitoring of early-age CS of UHPFRC.
Enhancing sulfasalazine solubility in supercritical carbon dioxide with ethanol cosolvent: a comprehensive study
Y-chromosome loss in cancer and immune cells might worsen treatment outcomes
Here’s why China’s science and innovation model is thriving
What incentives do companies need to publish research?
Can industry fill the gap left by US research funding cuts?
Uncovering minimal pathways in melanoma initiation
Abstract Melanomas are genetically heterogeneous, displaying mitogen-activated protein kinase mutations and homozygous loss of tumor suppressor genes. Mouse models combining such mutations produce fast-growing tumors. In contrast, rare, slow-growing tumors arise in mice combining Braf activation with heterozygous loss of Pten . Here we show that similar tumors can arise in albino mice bearing only a Braf mutation. Incidence kinetics suggest a stochastic event underlies tumorigenesis in tumors that arise with only a Braf mutation, yet de novo mutations or structural variants that could explain the incidence of most tumors could not be found. Single-cell transcriptomics of tumors identify a cell type resembling “neural crest-like” cells in human and mouse melanomas. These exist in normal mouse skin, expand upon Braf activation, and persist through serial transplantation; analyses of gene expression suggest they serve as precursors of malignant cells. This state may serve as an intermediate on a slow path to malignancy that may provide a diagnostically and therapeutically important source of cellular heterogeneity.