Sharing tableware reduces waste generation, emissions and water consumption in China’s takeaway packaging waste dilemma

China has a rapidly growing online food delivery and takeaway market, serving 406 million customers with 10.0 billion orders and generating 323kilotonnes of tableware and packaging waste in 2018. Here we use a top-down approach with city-level takeaway order data to explore the packaging waste and life-cycle environmental impacts of the takeaway industry in China. The ten most wasteful cities, with just 7% of the population, in terms of per capita waste generation, were responsible for 30% of the country's takeaway waste, 27-34% of the country's pollutant emissions and 30% of the country's water consumption. We defined one paper substitution and two sharing tableware scenarios to simulate the environmental mitigation potentials. The results of the scenario simulations show that sharing tableware could reduce waste generation by up to 92%, and environmental emissions and water consumption by more than two-thirds. Such a mechanism provides a potential solution to address the food packaging waste dilemma and a new strategy for promoting sustainable and zero-waste lifestyles. The online food delivery and takeaway market is growing in China, serving 406 million customers with 10.0 billion orders in 2018. Here, data from an online food delivery platform, life-cycle environmental impacts of packaging and tableware waste generated across 353 cities in China, and scenarios for paper alternatives and tableware sharing are presented

Methodology and applications of city level CO2 emission accounts in China

China is the world's largest energy consumer and CO2 emitter. Cities contribute 85% of the total CO2 emissions in China and thus are considered as the key areas for implementing policies designed for climate change adaption and CO2 emission mitigation. However, the emission inventory construction of Chinese cities has not been well researched, mainly owing to the lack of systematic statistics and poor data quality. Focusing on this research gap, we developed a set of methods for constructing CO2 emissions inventories for Chinese cities based on energy balance table. The newly constructed emission inventory is compiled in terms of the definition provided by the IPCC territorial emission accounting approach and covers 47 socioeconomic sectors, 17 fossil fuels and 9 primary industry products, which is corresponding with the national and provincial inventory. In the study, we applied the methods to compile CO2 emissions inventories for 24 common Chinese cities and examined uncertainties of the inventories. Understanding the emissions sources in Chinese cities is the basis for many climate policy and goal research in the future

Travel blogs on China as a destination image formation agent: A qualitative analysis using Leximancer

The Internet spreads tourism information around the world and specifically travel blogs function as an online version of word-of-mouth (eWOM). This research explored the role of blogs as a destination image formation agent for China's inbound tourism. Data were collected from 630 bloggers who wrote on two blog websites about their travels within China in 2011 and 2012. The bloggers on TravelBlog.org and TravelPod.com were mainly from English-speaking countries. Qualitative analysis using Leximancer software was applied and identified nine major textual themes and the relationships among these themes. In order of relative importance, the themes were place, Chinese, people, food, train, city, hotel, China, and students. The research indicated that international tourists tended to have positive images of China

System dynamic modeling on construction waste management in Shenzhen, China

This article examines the complexity of construction waste management in Shenzhen, Mainland China. In-depth analysis of waste generation, transportation, recycling, landfill and illegal dumping of various inherent management phases is explored. A system dynamics modeling using Stella model is developed. Effects of landfill charges and also penalties from illegal dumping are also simulated. The results show that the implementation of comprehensive policy on both landfill charges and illegal dumping can effectively control the illegal dumping behavior, and achieve comprehensive construction waste minimization. This article provides important recommendations for effective policy implementation and explores new perspectives for Shenzhen policy makers

A Review of Sensitivity Enhancement in Interferometer-Based Fiber Sensors

Optical fiber sensors based on an interferometer structure play a significant role in monitoring physical, chemical, and biological parameters in natural environments. However, sensors with high-sensitivity measurement still present their own challenges. This paper deduces and summarizes the methods of sensitivity enhancement in interferometer based fiber optical sensors, including the derivation of the sensing principles, key characteristics, and recently-reported applications.The modal coupling interferometer is taken as an example to derive the five terms related to the sensitivity: (1) the wavelength-dependent difference of phase between two modes/arms ∂ϕd/∂λ, (2) the sensor length Lw,A, (3) refractive index difference between two modes/arms Δneff,A, (4) sensing parameter dependent length change α, and (5) sensing parameter dependent refractive index change γ. The research papers in the literature that modulate these terms to enhance the sensing sensitivity are reviewed in the paper

Daunorubicin resensitizes Gram-negative superbugs to the last-line antibiotics and prevents the transmission of antibiotic resistance

Summary: Although meropenem, colistin, and tigecycline are recognized as the last-line antibiotics for multidrug-resistant Gram-negative bacteria (MDR-GN), the emergence of mobile resistance genes such as blaNDM, mcr, and tet(X) severely compromises their clinical effectiveness. Developing novel antibiotic adjuvants to restore the effectiveness of existing antibiotics provides a feasible approach to address this issue. Herein, we discover that a Food and Drug Administration (FDA)-approved drug daunorubicin (DNR) drastically potentiates the activity of last-resort antibiotics against MDR-GN pathogens and biofilm-producing bacteria. Furthermore, DNR effectively inhibits the evolution and spread of colistin and tigecycline resistance. Mechanistically, DNR and colistin combination exacerbates membrane disruption, induces DNA damage and the massive production of reactive oxygen species (ROS), ultimately leading to bacterial cell death. Importantly, DNR restores the effectiveness of colistin in Galleria mellonella and murine models of infection. Collectively, our findings provide a potential drug combination strategy for treating severe infections elicited by Gram-negative superbugs

An extended TODIM method for project manager’s competency evaluation

The project managers’ high efficiency and leadership ability is very important for the success of the whole project. Evaluating the competency of project manager accurately and selecting the suitable project manager from alternatives is a very core research issue that should be paid high attention to in the field of project management. When evaluating the competency of project manager, multiple criteria with interactive relationship should be considered, and the decision makers may have bounded rational behavior which may have a great impact on the result of evaluation, whereas the decision makes’ psychological behavior is rarely taken into account in the existing studies on the evaluation of project managers’ competency. TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) is a multi-criteria decision making method considering the decision makers’ behavior. In this paper, an extended TODIM method which combines λ -fuzzy measure with Choquet integral considering incomplete criteria information and decision makers’ bounded rational behaviors are presented to evaluate the competency of project manager. Furthermore, a numerical example is presented to illustrate the method proposed. We hope that this method can provide some valuable references for the evaluation of project manager’s competency

Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained

Automatic detection of actual water depth of urban floods from social media images

Urban flooding disasters are the most frequent natural disasters in cities, which broadly affects people's livelihood. Prompt emergency response is the key to alleviating such impact, however, that would rely on the timely mapping of flooding according to waterlogging severity. Compared with traditional approaches, crowdsourcing images from social media has emerged as a more efficient way to obtain this type of information. Therefore, this paper aims to explore the approach based on computer vision technology to automatically extract water depth from social media images, which can be utilized to construct flooding maps during urban flooding. Firstly, the images related to urban flooding were retrieved from social media and then filtered, leaving only images containing people. Secondly, a specific objective detection model based on YOLO was trained to detect the human body parts which are divided into crus, thigh, shoulder, and head from bottom to top. The experiment shows that the mAP of the trained human body parts detection model in the test dataset is 0.967. Afterward, an algorithm for extracting water depth was proposed based on the ratio of bounding boxes of detected body parts in images to the actual length of human body parts. Next, these models were verified by comparing them with manually estimated depth range and manually measured depth. The experiment shows that the accuracy of water depth range is 0.959 and the mean absolute error of the actual depth detection of water is 10.22 cm in the measurement dataset. Finally, the proposed models were applied to map the 2016 flooding that occurred in Wuhan as an illustrative case. This approach is helpful to broaden the source of flooding severity information and improve the efficiency of flood mapping in urban flood emergency management