Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods.

Due to its exceptional qualities, ultra-high-performance concrete (UHPC) has recently become one of the hottest research areas, although the material's significant carbon emissions go against the current development trend. In order to lower the carbon emissions of UHPC, this study suggests a machine learning-based strategy for optimizing the mix proportion of UHPC. To accomplish this, an artificial neural network (ANN) is initially applied to develop a prediction model for the compressive strength and slump flow of UHPC. Then, a genetic algorithm (GA) is employed to reduce the carbon emissions of UHPC while taking into account the strength, slump flow, component content, component proportion, and absolute volume of UHPC as constraint conditions. The outcome is then supported by the results of the experiments. In comparison to the experimental results, the research findings show that the ANN model has excellent prediction accuracy with an error of less than 10%. The carbon emissions of UHPC are decreased to 688 kg/m3 after GA optimization, and the effect of optimization is substantial. The machine learning (ML) model can provide theoretical support for the optimization of various aspects of UHPC.

Leaf litter chemistry and its effects on soil microorganisms in different ages of Zanthoxylum planispinum var. Dintanensis.

BACKGROUND: Leaf litter is the products of metabolism during the growth and development of plantation, and it is also an important component of nutrient cycling in plantation ecosystems. However, leaf litter chemistry and its effects on soil microorganisms in different ages, as well as the interactions between chemical components in leaf litter have been rarely reported. Based on this, this paper took Zanthoxylum planispinum var. dintanensis (hereafter Z. planispinum) plantations of 5-7, 10-12, 20-22, and 28-32 years old as the objects. By using one-way ANOVA, Pearson correlation analysis and redundancy analysis, we investigated leaf litter chemistry and its effects on soil microorganisms in different ages, and to reveal internal correlation of various chemical components in leaf litter, which can provide a scientific basis for the regulation of soil microbial activity in plantations. RESULTS: The variation of organic carbon with plantation age was more stable compared to total nitrogen and phosphorus of leaf litter. Nitrogen resorption was stronger than phosphorus resorption efficiency in Z. planispinum, and resorption efficiencies of leaf nitrogen and phosphorus for different ages were lower than the global average. Total nitrogen was highly significantly positively correlated with lignin, and total potassium was significantly positively correlated with tannin, suggesting the increase of inorganic substances in leaf litter would promote the accumulation of secondary metabolites. The leaf litter chemical traits explained up to 72% of soil microorganisms, where lignin was positively correlated with fungi and negatively correlated with bacteria, indicating that fungi are able to decompose lower quality litter and can break down complex and stable organic compounds more rapidly than bacteria. The nutrient elements carbon and nitrogen in the leaf litter and their interrelationship also have a great impact on soil microorganisms, because carbon is not only the element that provides energy, but also the element with the largest content in the microbiota. CONCLUSIONS: The sustained increase in inorganic nutrients of leaf litter did not favor the decomposition of secondary metabolites, but rather inhibited the degradation of leaf litter. The significant positive effect of the leaf litter chemistry on soil microorganisms indicates the important role of leaf litter in promoting nutrient cycling in Z. planispinum plantations.

Decreased inter-brain synchronization in the right middle frontal cortex in alcohol use disorder during social interaction: An fNIRS hyperscanning study.

BACKGROUND: Alcohol use disorder (AUD) is a widespread mental disorder and has thrust a heavy burden on the health system all over the world. Social cognition and function are reported to be impaired in AUD, but its neural mechanism is rarely investigated. The current study attempts to fill this gap. METHODS: 28 subjects with AUD and 36 healthy controls (HC) were recruited in this study and were paired into 14 AUD dyads and 18 HC dyads. The drinking problems, depression, anxiety, and impulsivity of subjects were measured. Each dyad completed cooperation and competition tasks with simultaneous frontal functional near-infrared spectroscopy (fNIRS) hyperscanning recording. The inter-brain synchronization (IBS) in the frontal cortex was calculated for each dyad and compared between AUD and HC. The significantly altered IBS in AUD was correlated with clinical measures to explore possible influencing factors. RESULTS: The IBS in the right middle frontal cortex was significantly decreased in AUD under both cooperation (t = -2.257, P = 0.028) and competition (t = -2.488, P = 0.016) task. The IBS during the cooperation task in the right middle frontal cortex in AUD was negatively correlated with non-planning impulsivity (r = -0.673, P = 0.006). LIMITATIONS: This study used cross-sectional data, which limited the causal inference. The synchronization between other brain regions besides the frontal cortex should be further explored in patients with AUD. CONCLUSION: The current study could provide new insights into the neural mechanism of social dysfunction in AUD and facilitate clinical intervention in future practice.