Urban garbage disposal and management in China

This paper, probing into the present situation of urban domestic garbage by analyzing its growing trend, compositional change and regional difference, reveals the problems existing in its disposal and management in China. Meanwhile, a questionnaire was conducted in five big cities around China for surveying urban residents' attitudes towards garbage disposal and management policies and measures. Results showed the output of urban domestic garbage in Chinese cities is ever increasing, and the recoverable materials and energy in garbage composition are also increasing. The population growth, economic development, and increase of residents' expenditure level are the main factors influencing the growing output and changing composition of the garbage. Information acquired from the questionnaire showed that majority of the urban residents are in favor of the garbage reduction policies and managerial measures and are willing to collaborate with municipal government in battling against garbage. Based on the analysis and questionnaire, some policymaking oriented suggestions such as operating the garbage disposal from a social welfare service to a sector of profit gaining enterprises, transferring the garbage management from passive end control to active source control, promoting the classified garbage collection in cities around China, and charging garbage fees for its cleanup and disposal, have also been put forward in the paper.

Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.

Garbage sorting and disposal in Japan and its implications to China

Beginning from Shanghai,and then Beijing,China has started its garbage sorting enforcement.Before 2025,all cities above the prefecture level will set up and perfect their garbage sorting and disposal system.As Japan is the most successful example in garbage sorting and disposal in the world,where garbage sorting is different district from district,we put our focus on a district Nerima district,in Tokyo about its garbage classification and daily practice.It eventually aims to draw implications for China’s garbage sorting and disposal.As residents,we Chinese people can also learn from the Japanese daily practice in garbage sorting.Only after the government builds a reasonable gathering and disposing system,residents absolutely shoulder the responsibility of sorting garbage,can we succeed in garbage sorting enforcement and environment protection.

Impact of hydrothermal process on the nutrient ingredients of restaurant garbage

In order to recover the nutrient resource from restaurant garbage, a complete trial with 2 factors on 5 levels of experiments was carried out. The temperature and heating time are the main factors influencing on hydrothermal process （HP） by which improves the degradability and digestibility of the restaurant garbage favorably to make animal feeds or fertilizer. The results showed the variation of protein, saccharide, and oil in the garbage. It showed that protein dissolved and liquefied during hydrothermal process, which made organic nitrogen in solid phase transfer to liquid phase. After heating at 180℃ for 60min, organic nitrogen in liquid phase began to transform into ammonia. It also showed that hydrothermal process could promote the dextrinization, dissolution of the starch and its hydrolysis to reducing sugar, due to that starch in the restaurant garbage decreases and reducing sugar increases. When the temperature reached 140℃, the reducing sugar started to decrease due to chemical reactions. The cellulose was stable at 100-180℃. The floatable oil increased markedly in the hydrothermal process. The suitable condition for de-oil was observed at 160℃ heating for 80 min. Furthermore, the extraction of grease from the solid phase accords with first-order reaction dynamic model.

The Main Characteristics and Disposal of Municipal Garbage in Changchun City of China

Based on current situations of Chinese garbage management and disposal, this paper is centered on introduction of some research results of household garbage in Changchun, capital city of Jilin Province, northeast China. From the viewpoint of man the earth system, garbage is characterized by sociality, temporal and spatial differentiation and limitation and other features, which imply that the garbage research being involved with natural science and social science should not only focus on the natural features but also on the social features. Compared to some developed cities, the material composition of garbage in Changchun is typically characterized by high moisture content, low combustion value and minor recyclable materials. Concentration, cycle shaping and self similarity characterize the garbage distribution. The tracing investigation on 683 households ten times shows that three fourths of the total garbage yield in Changchun is derived from non domestic units and that the attention of garbage management, especially source reduction, should be focused on the non domestic units. The GM(1,1) prediction model has been set up and the prediction result shows that the amount of garbage yield of Changchun in 2004 will be up to 1,500,000 tons. Investigation of environmental consciousness to garbage problems have been carried out. Based on the above characteristics of garbage, the disposal countermeasures of Changchun garbage have been suggested.

YOLObin: Non-Decomposable Garbage Identification and Classification Based on YOLOv7

In order to safeguard the biological framework on which human creatures depend for presence and make society feasible improvement, it is becoming increasingly important to classify garbage. In any case, individuals are not recognizable with the classification strategy, so it is troublesome for individuals to accurately get the classification of each kind of rubbish. A proper waste management system is a primary task in building a smart and healthy city. In arrange to direct individuals to classify garbage accurately, this paper proposes a strategy of rubbish classification and acknowledgment based on YOLOv7 which is a cutting edge real time object detector. Performance of this model is compared along with two other object detectors where Mask-RCNN achieved f-measurement of 85%, YOLOv5 achieved f-measurement of 95.1% and YOLOv7 achieved f-measurement 95.9%. We have used non-decomposable multiclass garbage images which entails messy backgrounds with unwanted images as well. Four classes of non-decomposable garbage data namely chips packet, plastic bottle, polythene and image with multiclass garbage with 1000 images are prepared for our dataset. Our experimental models performed well in classifying garbage images with cluttered backgrounds. We compared our test results to previous studies in which the majority of the models were tested and trained using laboratory images. The test comes about illustrates that the classification framework features a sensible degree of accuracy and the segmentation recognition impact is way better within the case of point-by-point picture, which can proficiently and helpfully total the rubbish classification errand.

Research on the Applicability of Garbage Classification to Carbon Trading Market

Climate warming is one of the important environmental issues with global concern. The Bloomberg News has recorded temperature changes in the recent 135 years. As the hottest year, in 2014 the global surface temperature was as high as 1.39 degrees Fahrenheit, 0.68 degrees Celsius higher than the average in long-term. The severity of this issue has been proved with the refresh of the highest record^([1]) and the increasing temperature as well as people's personal experience. There is a demand of in-depth discussion about comprehensive and efficient reduction of carbon and greenhouse gas emission and the development of low carbon economy, with garbage classification as the most efficient breach but also most easily to be neglected by people. This article attempts to find a feasible method of carbon emission reduction from the perspective of garbage classification and resource recycling and make quantitative estimation of its value combined with local practice and data in Chengdu.

Antimicrobial Resistance Encountered in Garbage Collection Areas and Dumpsites in Nairobi, Kenya Using <i>Escherichia coli</i>and <i>Klebsiella</i>as Indicator Species

Dumpsites and garbage collection areas can act as reservoirs of highly resistant bacterial strains and facilitate the dissemination of Multidrug resistant strains to those living and work on or near the dumpsites and garbage collection areas. The objective of this study was to determine the potential of garbage collection areas and dumpsites in different parts of Nairobi as possible sources of resistant strains using E. coli and Klebsiella as indicator species. The study design was a cross-sectional survey. Sample collection was carried out at different days in seventeen different areas. A total of 126 samples were collected during the sampling period. The samples were then transported to the laboratory for analysis. The samples were cultured on MacConkey agar. Gram staining was done on discrete isolates based on colony characteristics. Biochemical tests were performed on colonies from primary cultures for final identification of the isolates. Antimicrobial disc susceptibility tests and pathogenicity tests were also carried out on the indicator isolates. A total of 121 E. coli and 165 Klebsiella were isolated from all the sampled sites. The highest bacterial burden was recorded from Muthurwa estate dumpsite, with a mean viable count of 8.2 × 1010 cfu/gm while the least was from Dandora dumpsite with a mean count of 1.1 × 1011 cfu/gm. Overall, gentamicin was the most effective antibacterial agent on Klebsiella and meropenem was the most effective on both E. coli and Klebsiella strains. The isolates showed high resistance to ampicillin, streptomycin, and trimethoprim-sulfamethoxazole. It is concluded that municipal waste dumpsites and garbage collection areas bear heavy burdens of potentially resistant bacteria which may constitute major public health hazards, not only to the immediate communities but also to the families of such site workers.

Practical Study of the Sports Garbage Pickup Tournament in Japan-Sports Events to Solve Social Problems

The Sports Garbage Pickup Tournament is a sports event that incorporates elements of competition into cleaning activities,as opposed to traditional cleaning activities.As of the end of December 2016,a total of 552 Sports Garbage Pickup Tournaments have been held,mobilizing a cumulative total number of 62,989 people.As each tournament brings a revenue of over 300,000 yen,the total sales so far would be more than 165,000,000 yen.This game was recently reported on the International Olympic Committee channel.The garbage issue,a social problem for local communities,should be solved by residents,visitors,and workers themselves through sports,cooperating with local governments.This is an eco-friendly sport aimed at“solving local social problems through sports.”Sports Garbage Pickup Tournaments can be held anywhere,inside the city or in a natural environment.In addition,the competition allows participants to visually understand the environmental capacity of garbage produced in the community,as the points are weighted according to the types of garbage.The Sports Garbage Pickup Tournament is an educational program for sustainable development with emphasis on experience,pursuit,and practice,and also an action-based program aimed at promoting spontaneous action.This is an optimal competition for participants to acquire skills through mutual communication.As children,adults,and people with disabilities can play together,the competition will promote communication across the generations.

Countermeasures on improving the charge rules and long-acting operation mechanism for the treating residential domestic sewage and household garbage in rural Yunnan

Improving the rural living environment in rural China is one of the key tasks for the country to accomplish its goal of building a moderately prosperous society by 2020. Yunnan has highly focused on the task of the treatment of residential domestic sewage and household garbage in rural area. Many efforts and resources have been put into this field in Yunnan since 2016. Progress has been made to increase the coverage rate of the sewage and household garbage treatment facilities. Seventy-five percent of total administrative villages have built up garbage transportation system and treatment facilities. Sixty-three percent of towns have collected and treated the residential domestic sewage by constructing various scale sewage treatment stations. However, the lack of the long-acting operation mechanism and the imperfection of the charge rules for the sewage and garbage treatment facilities have become problems that would hinder the achieving of the environmental goals in Yunnan. The reasons were elaborated on the basis of the local actual situations. Therefore, it is significant to improve the charge rules and frame the long-acting operation mechanism by strengthening the governance capacity, frame an overall mechanism and encourage the mass to be involved in the improvements of the living environment in rural Yunnan.

Application of Optimized BP Neural Network in Addressing for Garbage Power Plant

Neural network has the abilities of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. This paper presents a solution for overcoming these shortages from two aspects. One is to adopt principle component analysis to select study samples and make some of them contain sample characteristics as many as possible, the other is to train the network using Levenberg-Marquardt backward propagation algorithm. This new method was proved to be valid and practicable in site selection of practical garbage power generation plants.

Garbage乐队小传

说Garbage是一支乐队,倒不如说他们是一支制作人的团队更为合适.除了Shirly Manson这位魅力无比的女声主唱,三位男性成员Butch Vig、Duke Erikson和Billy Bush均是久已成名的音乐制作人.

Research on Business Model Optimization of Artificial Intelligence Garbage Classification

In recent years,garbage classification and environmental protection are gradually becoming an important step in the construction of ecological civilization in China.However,the popularity and commercial value of the application of artificial intelligence trash cans in Beijing are not high at present.This article analyzes these problems one by one and propose solutions,hoping that the commercial value of artificial intelligence trash cans can be optimized and improved and to make the city greener.This paper uses the questionnaire method and the literature method to research and analyze the optimization of the business model of artificial intelligence in garbage classification.

Garbage Collection Scheduling of Aperiodic Tasks

In the previous work of garbage collection （GC） models, scheduling analysis was given based on an assumption that there were no aperiodic mutator tasks. However, it is not true in practical real-time systems. The GC algorithm which can schedule aperiodic tasks is proposed, and the variance of live memory is analyzed. In this algorithm, active tasks are deferred to be processed by GC until the states of tasks become inactive, and the saved sporadic server time can be used to schedule aperiodic tasks. Scheduling the sample task sets demonstrates that this algorithm in this paper can schedule aperiodic tasks and decrease GC work. Thus, the GC algorithm proposed is more flexible and portable.

From Garbage to Green Park

LI Dong could not hide her surprise when she returned to her hometown from California. Having been away for four years, she was amazed by the transformation that Dongheyan Village, located near the Yongding River in southwest Beijing＇s Fengtai District, had undergone.

Beijing Declares War on Garbage

ＢｅｉｊｉｎｇＤｅｃｌａｒｅｓＷａｒｏｎＧａｒｂａｇｅ￥ＬＵＯＱＩＡＮ…ａｎｄａｆｏｒｍｉｄａｌｅｊｏｂｔｏｄｏ．ｂｙｓｔａｆｆｒｅｐｏｒｔｅｒＬＵＯＱＩＡＮＡＴＡＦＲＯＭａｎａｅｒｉａｌｒｅｍｏｔｅｓｅｎｓｉｎｇｔｅｓｔｃａｒｒｉｅｄｓｈｏｗｅｄｔ...

Keeping Foreign Garbage Out of China

ＫｅｅｐｉｎｇＦｏｒｅｉｇｎＧａｒｂａｇｅＯｕｔｏｆＣｈｉｎａＢｙｓｔａｆｆｒｅｐｏｒｔｅｒＸＩＡＥＲＴｈｅＳｔａｔｅＢｕｒｅａｕｏｆＥｎｖｉｒｏｎｍｅｎｔａｌＰｒｏｔｅｃｔｉｏｎａｎｄａｆｆｉｌｉａｔｅｄｄｅｐａｒｔｍｅｎｔｓｈｏｌｄａｎｏｎ－ｔｈ...

Garbage Collector Verification for Proof-Carrying Code

We present the verification of the machine-level implementation of a conservative variant of the standard mark-sweep garbage collector in a Hoare-style program logic. The specification of the collector is given on a machine-level memory model using separation logic, and is strong enough to preserve the safety property of any common mutator program. Our verification is fully implemented in the Coq proof assistant and can be packed immediately as foundational proof-carrying code package. Our work makes important attempt toward building fully certified production-quality garbage collectors.

New Benchmark for Household Garbage Image Recognition

Household garbage images are usually faced with complex backgrounds,variable illuminations,diverse angles,and changeable shapes,which bring a great difficulty in garbage image classification.Due to the ability to discover problem-specific features,deep learning and especially convolutional neural networks(CNNs) have been successfully and widely used for image representation learning.However,available and stable household garbage datasets are insufficient,which seriously limits the development of research and application.Besides,the state-of-the-art in the field of garbage image classification is not entirely clear.To solve this problem,in this study,we built a new open benchmark dataset for household garbage image classification by simulating different lightings,backgrounds,angles,and shapes.This dataset is named 30 classes of household garbage images(HGI-30),which contains 18 000 images of 30 household garbage classes.The publicly available HGI-30 dataset allows researchers to develop accurate and robust methods for household garbage recognition.We also conducted experiments and performance analyses of the state-of-the-art deep CNN methods on HGI-30,which serves as baseline results on this benchmark.