Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines

The development of solid waste resources as constituent materials for wet shotcrete has significant economic and environmental advantages. In this study, the concept of using tailings as aggregate and fly ash and slag powder as auxiliary cementitious material is proposed and experiments are carried out by response surface methodology(RSM). Multivariate nonlinear response models are constructed to investigate the effect of factors on the uniaxial compressive strength(UCS) of tailings wet shotcrete(TWSC). The UCS of TWSC is predicted and optimized by constructing Gaussian process regression(GPR) and genetic algorithm(GA). The UCS of TWSC is gradually enhanced with the increase of slag powder dosage and fineness modulus, and it is enhanced first and then decreased with the increase of fly ash dosage. The microstructure of TWSC has the highest gray value and the highest UCS when the fly ash dosage is about 120 kg·m^(-3). The GPR–GA model constructed in this study achieves high accuracy prediction and optimization of the UCS of TWSC under multi-factor conditions.

Development of Wet Shotcrete with Solid Waste as Aggregate:Strength Optimization and Mix Proportion Design

The super-fine particle size of tailings is its drawback as a recycled resource,which is reflected in the low strength of the new construction and industrial materials formed when it is mixed with cement and other cementitious materials.Therefore,it is crucial to study the effect of tailings particle size and cementitious material on the strength of tailings wet shotcrete(TWSC)and to investigate the optimal mix proportion.In this paper,a multivariate nonlinear response model was constructed by conducting central composite experiments to investigate the effect of different factors on the strength of TWSC.The strength prediction and mix proportion optimization of TWSC are carried out by machine learning techniques.The results show that the response model has R^(2)>0.94 and P<0.01,which indicates that the model has high reliability.Moreover,the strength of TWSC increases with the increase of tailings fineness modulus and decrease of water-binder ratio,while it also increases and then decreases with the increase of replacement rate of slag powder to cement(SRC rate).The extreme learning machine(ELM)constructed in this paper predicts the strength of TWSC with an accuracy of more than 98%and achieves rapid prediction under multi-factor conditions.It is worth mentioning that the ELM combined with the genetic algorithm(ELM-GA)collaboratively solved to obtain the mix proportion for C15 and C20 strength grades of TWSC and the maximum error is verified by experiments to be less than 2%.

A Modeling Method for Predicting the Strength of Cemented Paste Backfill Based on a Combination of Aggregate Gradation Optimization and LSTM

Cemented paste backill(CPB)is a susta inable mining technology that is widely used in mines and helps to improve the mine environment.To investigate the relationship between aggregate grading and different affecting factors and the uniaxial compressive strength(UCS)of the cemented paste backill(CPB),Talbol gradation theory and neural networks is used to evaluate aggregate gradation to determine the optimum aggregate ratio.The mixed aggregate ratio with the least amount of cement(waste stone content river sand content=7:3)is obtained by using Talbol grading theory and pile compactness function and combined with experiments.In addition,the response surface method is used to design strength speaific ratio experiments.The UCS prediction model which ues the ISTM and considers the aggregates gradation have high accuracy.The root mean square error(RMSE)of the prediction results is 0.0914,the coefficient of determination(R^(2))is 0.9973 and the variance account for(VAF)is 99.73.Compared with back propagation neural network(BP-ANN),extreme lea ming machine(ELM)and madal basis function neural network(RBF ANN),LSTM can efectively characterize the nonlinear relationship between UCS and individual affecting factors and predict UCS with high accuracy.The sensitivity analysis of different affecting factors on UCS shows that all 4 factors have significant effect on UCS and sensitivity is in the following ranking:cement content(0.9264)>slurry concentration(0.9179)>aggregate gradation(waste rodk content)(0.9031)>curing time(09031).

Fly Ash and Slag as Partial Replacement of Cement for the Synthesis of Low Carbon Cementitious Materials

Tailings known as solid waste are generated by the mining industry.The development of tailings as wet shotcrete aggregates has significant economic and environmental benefits.The fine particle size of the tailings results in a large consumption of traditional cement as a cementitious material and insignificant improvement in strength.Therefore,a composite cementitious system of cement and solid waste resources(fly ash and slag powder)is explored for this study.In this paper,the response surface methodology(RSM)is used to optimize the experimental design and a multivariate nonlinear response model with cement,fly ash and slag powder contents as variables are constructed,which can investigate the effect of the composite cementitious system on the strength of tailing wet shotcrete(TWSC).In addition,the information entropy(IE)is introduced and combined with the RSM to evaluate the composite cementitious system.Finally,the desirability function(DF)combined with RSM is used to optimize the composite cementitious system.The results show that the response model constructed in this paper has R^(2)=0.96 and P-value<0.01(the test result of the model is P-value<0.01),which indicates that the model has high reliability.The higher the content of slag powder and cement in the composite cementitious system,the higher the strength and comprehensive score of the TWSC.There is a critical value of fly ash content,which makes the maximum cementation of the composite cementing system.The optimal mix proportion of the composite cementitious system is obtained based on RSM-DF,which leads to the strength of TWSC at different curing time to achieve the expected index.

An Exploration of Growth and Selenium Enrichment of Selenium-enriched Rice

[Objectives]To explore the effects of selenium fertilizer on rice growth and selenium enrichment.[Methods]Using high-quality rice varieties as experimental materials,exogenous selenium fertilizer was sprayed at two critical periods of tillering stage and full heading stage to explore the effects of different selenium treatments on rice agronomic traits,leaf SPAD value and yield.[Results]Spraying selenium fertilizer could promote the growth of rice,and the selenium enrichment effect in the aboveground parts of the plant was obvious.The selenium content of rice(milled rice)was 0.04-0.07 mg/kg.[Conclusions]This study lays a foundation for the promotion of suitable leaf fertilizers in Zhaoqing City,and is expected to promote the cultivation and promotion of selenium-enriched rice.

Accelerating the practical application of MOFs for hydrogen storage-from performance-driven to application-oriented

Metal–organic frameworks(MOFs)are highly promising porous materials known for their exceptional porosity,extensive surface area,and customizable pore structures,making them an ideal solution for hydrogen storage.However,most MOFs research remains confined to the laboratory,lacking practical applications.To address this,the author proposes a shift towards practical applications,the creation of a comprehensive MOFs database,alignment of synthesis with practical considerations,and diversification of MOFs applications.These steps are crucial for harnessing the full potential of MOFs in real-world energy challenges.

Testing the role of genetic variation of the MC4R gene in Chinese population in antipsychotic-induced metabolic disturbance

Antipsychotic-induced metabolic disturbance(AIMD) is a common adverse effect of antipsychotics with genetics partly underpinning variation in susceptibility among schizophrenia patients. Melanocortin4 receptor(MC4 R) gene, one of the candidate genes for AIMD, has been under-studied in the Chinese patients. We conducted a pharmacogenetic study in a large cohort of Chinese patients with schizophrenia. In this study, we investigated the genetic variation of MC4 R in Chinese population by genotyping two SNPs(rs489693 and rs17782313) in 1,991 Chinese patients and examined association of these variants with the metabolic effects that were often observed to be related to AIMD. Metabolic measures, including body mass index(BMI), waist circumference(WC), glucose, triglyceride, high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels were assessed at baseline and after 6-week antipsychotic treatment. We found that interaction of SNP×medication status(drug-na?ve/medicated) was significantly associated with BMI, WC, and HDL change %, respectively. Both SNPs were significantly associated with baseline BMI and WC in the medicated group. Moderate association of rs489693 with WC, Triglyceride, and HDL change % were observed in the whole sample. In the drug-na?ve group, we found recessive effects of rs489693 on BMI gain more than 7%, WC and Triglyceride change %, with AA incurring more metabolic adverse effects. In conclusion, the association between rs489693 and the metabolic measures is ubiquitous but moderate. Rs17782313 is less involved in AIMD. Two SNPs confer risk of AIMD to patients treated with different antipsychotics in a similar way.

Interaction Between Variations in Dopamine D2 and Serotonin 2A Receptor is Associated with Short-Term Response to Antipsychotics in Schizophrenia

Dear Editor,Schizophrenia is a chronic and debilitating brain disorder,which has a strong genetic component with heritability ranging from 66%to 85%[1,2].Currently,antipsychotic drugs remain the most effective treatment for the psychotic symptoms of schizophrenia[3].Because of the severe sideeffects of first-generation antipsychotics(FGAs),secondgeneration antipsychotics(SGAs)have become more widely used in the treatment of schizophrenia.