State-of-the-art applications of machine learning in the life cycle of solid waste management

Due to the superiority of machine learning(ML)data processing,it is widely used in research of solid waste(SW).This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW.Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress.Based on the articles considered,a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened.The three topics of ML,namely,SW categories,ML algorithms,and specific applications,as applied to the life cycle of SW were discussed.ML has been applied during the entire SW process,thereby affecting its life cycle.ML was used to predict the generation and characteristics of SW,optimize its collection and transportation,and model the processing of its energy utilization.Finally,the current challenges of applying ML to SW and future perspectives were discussed.The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW.ML plays an important role in the modernization and intellectualization of SW management.It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

Pyrolysis is considered an attractive option and a promising way to dispose waste plastics.The thermogravimetric experiments of high-density polyethylene(HDPE)were conducted from 105℃ to 900℃ at different heating rates(10℃/min,20℃/min,and 30℃/min)to investigate their thermal pyrolysis behavior.We investigated four methods including three model-free methods and one modelfitting method to estimate dynamic parameters.Additionally,an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss(wt.%)of HDPE,and optimized via assessing mean squared error and determination coefficient on the test set.The optimal MSE(2.6297×10^(−2))and R^(2) value(R^(2)>0.999)were obtained.Activation energy and preexponential factor obtained from four different models achieves the acceptable value between experimental and predicted results.The relative error of the model increased from 2.4%to 6.8%when the sampling frequency changed from 50 s to 60 s,but showed no significant difference when the sampling frequency was below 50 s.This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space.This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters.It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.

可生物降解塑料取代传统塑料--对碳排放的影响

In recent years,a great deal of attention has been focused on the environmental impact of plastics,includ-ing the carbon emissions related to plastics,which has promoted the application of biodegradable plas-tics.Countries worldwide have shown high interest in replacing traditional plastics with biodegradable plastics.However,no systematic comparison has been conducted on the carbon emissions of biodegrad-able versus traditional plastic products.This study evaluates the carbon emissions of traditional and biodegradable plastic products(BPPs)over four stages and briefly discusses environmental and economic perspectives.Four scenarios-namely,the traditional method,chemical recycling,industrial composting,and anaerobic digestion-are considered for the disposal of waste BPPs(WBPPs).The analysis takes China as a case study.The results show that the carbon emissions of 1000traditional plastic products(plastic bags,lunch boxes,cups,etc.)were52.09-150.36 carbon emissions equivalent of per kilogram(kg CO_(2)eq),with the stage of plastic production contributing 50.71%-50.77%.In comparison,1000 similar BPPs topped out at 21.06-56.86 kg CO_(2)eq,approximately 13.53%-62.19%lower than traditional plastic prod-ucts.The difference was mainly at the stages of plastic production and waste disposal,and the BPPs showed significant carbon reduction potential at the raw material acquisition stage.Waste disposal plays an important role in environmental impact,and composting and anaerobic digestion are considered to be preferable disposal methods for WBPPs.However,the high cost of biodegradable plastics is a challenge for their widespread use.This study has important reference significance for the sustainable development of the biodegradableplastics industry.

Emerging roles of circular RNAs in cancer:a narrative review

Circular RNAs (circRNAs) constitute a novel class of endogenous noncoding RNAs characterized by a covalently closed structure and involved in multiple biological processes. The main biological functions and properties of circRNAs can be defined by five features: a "sponging" effect on other RNA species, post-transcriptional regulation, rolling circle translation, generation of pseudogenes, and splicing interference. Although circRNAs were first detected decades ago, the role of circRNAs and the mechanisms underlying their actions remain incompletely characterized. Recently, circRNAs were reported to play indispensable roles in regulating metabolic and signal transduction events controlling the proliferation, migration, and survival of cells. Importantly, many studies demonstrated that dysregulated circRNA expression is associated with the development of multiple diseases, including cancer. In this review, we summarize current knowledge on the roles and mechanisms of circRNAs in cancer and discuss their functions as oncogenes or tumor suppressors in different tumor types.