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The idea of industry finding uses for non-product outputs (by-products and waste) is not a new one (Desrochers 2000). Local context and incentives changed in the 20th century, however, and the practice fell out of favor due to cheap and abundant energy, resources and disposal options, subsidies that discouraged recycling, and regulations preventing re-use (Desrochers 2004). As the sustainable development agenda gained ground, other concerns led firms and governments to reconsider their strategies: increasing waste disposal costs, concerns over environmental degradation accompanied by stricter environmental regulations, and a growing awareness of the potential profits from by-product and waste utilization. The practical example of resource cycling between industrial facilities in Kalundborg, Denmark, came to the attention of the academic community (Ehrenfeld and Gertler 1997), generating substantial interest in what has become known as 'industrial symbiosis' (IS). In the intervening fifteen years, examples in which two or more otherwise unrelated industries develop a mutually beneficial relationship making productive use of otherwise underutilized resources (including materials, water and energy)i continue to be a topic of study. The phenomenon, and the research on it, is emblematic of the broader field of industrial ecology.

With the development of the Internet of Battlefield Things (IoBT), soldiers have become key nodes of information collection and resource control on the battlefield. It has become a trend to develop wearable devices with diverse functions for the military. However, although densely deployed wearable sensors provide a platform for comprehensively monitoring the status of soldiers, wearable technology based on multi-source fusion lacks a generalized research system to highlight the advantages of heterogeneous sensor networks and information fusion. Therefore, this paper proposes a multi-level fusion framework (MLFF) based on Body Sensor Networks (BSNs) of soldiers, and describes a model of the deployment of heterogeneous sensor networks. The proposed framework covers multiple types of information at a single node, including behaviors, physiology, emotions, fatigue, environments, and locations, so as to enable Soldier-BSNs to obtain sufficient evidence, decision-making ability, and information resilience under resource constraints. In addition, we systematically discuss the problems and solutions of each unit according to the frame structure to identify research directions for the development of wearable devices for the military.

OBJECTIVES: In this study, we aim to evaluate the effect of urban and rural resident medical insurance scheme (URRMI) on the utilisation of medical services by urban and rural residents in the four pilot provinces. SETTING AND PARTICIPANTS: The sample used in this study is 13 305 individuals, including 2620 in the treatment group and 10 685 in the control group, from the 2011 and 2015 surveys of China Health and Retirement Longitudinal Study. OUTCOME MEASURES: Propensity score matching and difference-in-differences regression approach (PSM-DID) is used in the study. First, we match the baseline data by using kernel matching. Then, the average treatment effect of the four outcome variables are analysed by using the DID model. Finally, the robustness of the PSM-DID estimation is tested by simple model and radius matching. RESULTS: Kernel matching have improved the overall balance after matching. The URRMI policy has significantly reduced the need-but-not outpatient care and significantly increased outpatient care cost and inpatient care cost for rural residents, with DID value of −0.271, 0.090 and 0.256, respectively. After robustness test, the DID competing results of four outcome variables are consistent. CONCLUSIONS: URRMI has a limited effect on the utilisation of medical and health services by all residents, but the effect on rural residents is obvious. The government should establish a unified or income-matching payment standard to prevent, control the use of medical insurance funds and increase its efforts to implement URRMI integration in more regions to improve overall fundraising levels.

Air pollution seriously threatens human health, and its consequences are particularly prevalent among environmentally vulnerable or sensitive groups. However, whether the concerns among these groups are different and how they affect air pollution governance remain unclear. Here, we extract 3.8 million haze-related posts from China's Sina Weibo and analyze the concerns raised by these groups by constructing an air pollution notability index. The results show that protection is the key theme for women aged 20–35 years, while elderly individuals are easily influenced by haze-related product ads yet lack awareness of scientific-based protection. Concerns shared by young individuals are more effective in pressuring the government in cities that experience higher levels of pollution. Concerns shared by women are more effective in cities that experience lower levels of pollution. This study evidences the influence of the public concerns conveyed via social media on air pollution governance in China.

Xi Zhang,1– 3 Jing-Hua Li,4 Qi Zhang,1,3 Qin-Qin Li,1,3 Kang-Ping Zhang,1,3 Meng Tang,1,3 Yi-Zhong Ge,1,3 Wei Li,5 Hong-Xia Xu,6 Zeng-Qing Guo,7 Han-Ping Shi1,3 1Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of China; 2Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, 071000, People's Republic of China; 3Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People's Republic of China; 4Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 071000, People's Republic of China; 5Cancer Center of the First Hospital of Jilin University, Changchun, 130021, People's Republic of China; 6Department of Clinical Nutrition, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, 400042, People's Republic of China; 7Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University, Fujian, 350014, People's Republic of ChinaCorrespondence: Han-Ping ShiDepartment of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People's Republic of ChinaTel +86-10-6392 6985Fax +86-10-6392 6325Email shihp@ccmu.edu.cnBackground: Overweight and obese patients with cancer present with chronic inflammation, dysfunctional antitumor immunity and malnutrition risk. Prognostic nutritional index (PNI) is a promising indicator for predicting inflammatory, immunological and nutritional states; however, its prognostic value in overweight and obese patients with cancer has not been explored. Therefore, the aim of the current study was to explore the prognostic value of PNI levels in overweight and obese patients with cancer.Methods: The current large-scale retrospective cohort multicenter study included 3532 patients. Time-dependent receiver operating characteristic (ROC) curve analysis was used to determine the prediction accuracy of PNI levels for mortality of overweight and obese patients with cancer. Restricted cubic splines were used to model the association between PNI levels and mortality. Association between low PNI and overall survival rate was analyzed using the Kaplan–Meier method and Cox regression model.Results: Area under the curve (AUC) of the PNI for all-cause mortality was higher compared with that of the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) in overweight and obese patients with cancer. There was a significant inverse relationship between PNI levels and all-cause mortality (per SD increment-HR: 0.79; 95% CI: 0.74, 0.85; P< 0.001). Subgroup analysis showed that the risk for mortality significantly decreased with increase in PNI levels in patients at risk of malnutrition (per SD increment-HR: 0.67; 95% CI: 0.57, 0.78; P< 0.001) and elderly patients (per SD increment-HR: 0.74; 95% CI: 0.64, 0.84; P< 0.001). In addition, PNI levels showed an inverse association with mortality in patients without malnutrition risk (per SD increment-HR: 0.81; 95% CI: 0.75, 0.88; P< 0.001). Subgroup analysis based on tumor type showed that low PNI was an independent predictor of poor prognosis for patients with lung cancer, gastric cancer and hepatobiliary and pancreatic cancer.Conclusion: Low PNI levels are associated with an increased risk for all-cause mortality. PNI level is a potential effective inflammation-based prognostic tool for overweight and obese patients with cancer.Keywords: PNI, mortality, overweight or obese, prognosis, cancer patients