Rationale: Little evidence from large-scale cohort studies exists about the relationship of solid fuel use with hospitalization and mortality from major respiratory diseases. Objectives: To examine the associations of solid fuel use and risks of acute and chronic respiratory diseases. Methods: A cohort study of 277,838 Chinese never-smokers with no prior major chronic diseases at baseline. During 9 years of follow-up, 19,823 first hospitalization episodes or deaths from major respiratory diseases, including 10,553 chronic lower respiratory disease (CLRD), 4,398 chronic obstructive pulmonary disease (COPD), and 7,324 acute lower respiratory infection (ALRI), were recorded. Cox regression yielded adjusted hazard ratios (HRs) for disease risks associated with self-reported primary cooking fuel use. Measurements and Main Results: Overall, 91% of participants reported regular cooking, with 52% using solid fuels. Compared with clean fuel users, solid fuel users had an adjusted HR of 1.36 (95% confidence interval, 1.32–1.40) for major respiratory diseases, whereas those who switched from solid to clean fuels had a weaker HR (1.14, 1.10–1.17). The HRs were higher in wood (1.37, 1.33–1.41) than coal users (1.22, 1.15–1.29) and in those with prolonged use (≥40 yr, 1.54, 1.48–1.60; <20 yr, 1.32, 1.26–1.39), but lower among those who used ventilated than nonventilated cookstoves (1.22, 1.19–1.25 vs. 1.29, 1.24–1.35). For CLRD, COPD, and ALRI, the HRs associated with solid fuel use were 1.47 (1.41–1.52), 1.10 (1.03–1.18), and 1.16 (1.09–1.23), respectively. Conclusions: Among Chinese adults, solid fuel use for cooking was associated with higher risks of major respiratory disease admissions and death, and switching to clean fuels or use of ventilated cookstoves had lower risk than not switching.

Background Cooking practice has transitioned from use of solid fuels to use of clean fuels, with addition of better ventilation facilities. However, the change in mortality risk associated with such a transition remains unclear.Methods The China Kadoorie Biobank (CKB) Study enrolled participants (aged 30-79 years) from ten areas across China; we chose to study participants from five urban areas where transition from use of solid fuels to clean fuels for cooking was prevalent. Participants who reported regular cooking (weekly or more frequently) at baseline were categorised as persistent clean fuel users, previous solid fuel users, or persistent solid fuel users, according to selfreported fuel use histories. All-cause and cardiopulmonary mortality were identified through linkage to China's Disease Surveillance Point system and local mortality records.

The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.

Mobile devices are evolving into powerful systems due to recent advances in their communication, storage and computation technologies. They are poised to play a key role in providing a rich collaborative computing platform for various applications. This paper proposes "Transient Clouds" -a collaborative computing platform that allows nearby devices to form an ad-hoc network and provide various capabilities as cloud services. Transient Clouds utilize the collective capabilities of the devices present, along with their social and context awareness that cannot be provided efficiently by the traditional clouds. We present a modified algorithm of the Hungarian method for assigning tasks to devices in order to achieve various goals (e.g., load balancing, collocating executions, etc...). We evaluate the performance of our proposed algorithms through simulation and provide a real implementation on the Android platform using the Wi-Fi Direct framework. We envision Transient Clouds to be utilized in temporal scenarios in which the cloud is created on-the-fly by the devices present in an environment and would disappear as the devices leave the network.

Since sensor applications are implemented in embedded computer systems, cyber attacks that compromise regular computer systems via exploiting memory-related vulnerabilities present similar threats to sensor networks. However, the paper shows that memory fault attacks in sensors are not just the same as in regular computers due to sensor's hardware and software architecture. In contrast to worm attacks, malcodes carried by exploiting packets cannot be executed in a sensor. Therefore, the paper proposes a range of attack approaches to illustrate that a mal-packet, which only carries specially crafted data, can exploit memory-related vulnerabilities and utilize existing application codes in a sensor to propagate itself without disrupting sensor's functionality. The paper shows that such a mal-packet can have as few as 17 bytes. A prototype of a 27-byte mal-packet has been implemented and tested in Mica2 sensors. Simulation shows that the propagation pattern of such a mal-packet in a sensor network is very different from worm propagation. Mal-packets can either quickly take over the whole network or hard to propagate under different traffic situations.