Radiocarbon (14C) has become a unique and powerful tracer in source apportionment of atmospheric carbonaceous particles. In this study, the Asia Pacific Economic Cooperation summit (APEC) held in Beijing in 2014 was used as a demonstration to research the source apportionment of atmosphere PM2.5. We used a 200 kV single stage accelerator mass spectrometer recently completed at China Institute of Atomic Energy (CIAE). The PM2.5 samples related to above case were collected, and the characteristics of radiocarbon in organic carbon (OC) and elemental carbon (EC) in samples were analyzed using the AMS. The results show that the Before-APEC pollution emission mode is different from the During-APEC and After-APEC pollution emission modes. For Before-APEC, During-APEC and After-APEC, the average values of fossil carbon fraction of OC are 0.463, 0.431 and 0.615, respectively, and those of EC are 0.644, 0.561 and 0.687. The fossil source contributions of traffic activities using fossil fuels to OC and EC are 15.8 % and 21.9 %, respectively. The fossil source contributions of industrial activities to OC and EC are 38.0 % and 8.2 %, respectively. It is about 7–10 days that is needed to take to regenerate the PM2.5 pollution caused by human activities.

Avian pathogenic Escherichia coli (APEC) cause extra intestinal disease in chickens, turkeys, and other avian species via respiratory tract infection, giving rise to typical signs related to colibacillosis, such as: septicaemia, enteritis, granulomas, omphalitis, sinusitis, airsacculitis, arthritis/synovitis, peritonitis, pericarditis, perihepatitis, cellulitis, and swollen head syndrome. Disease caused by APEC strains results in significant financial losses to the poultry industry worldwide. This review provides essential information on the diagnosis of APEC. The information cited in this review will elucidate the various forms of APEC diagnosis while demonstrating the, lack of a precise diagnosis of this strain. The diagnoses mentioned herein reinforce the necessity to make progress toward a more accurate diagnosis of APEC to contribute to its reduction in poultry flocks.

The dialogue on data protection has so far been dominated by European and American voices. There are currently a few international conventions in place such as the Council of Europe's 1981 Convention for the Protection of Individuals with regard to the Automatic processing of personal data, the 1980 OECD Guidelines Governing the Protection of Privacy and Transborder Flows of Personal Data , which apply to 30 OECD countries, and the EU Directive 95/46/EC on the protection of individuals with regard to the processing of personal data, which binds EU member states but has had some impact on non-European countries due to the restriction on cross border flow of information.

This has changed with the emergence of the APEC Privacy Framework in 2004 which focuses on the importance of the free flow of information in the digital age. Does the APEC Privacy Framework have anything of value to add or does it dilute the standards already in place? This article will examine these questions and argue that perhaps the APEC Privacy Framework is the first step towards a truly global standard for data protection.

The skill of seasonal mean precipitation and temperature predictions by multi-model ensemble (MME) schemes has been examined using hindcast data from the APEC Climate Network (APCN). The hindcast data of the member models have been collected from several institutions in the APEC region. Skill metrics, namely, anomaly correlation coefficient (ACC), Hanssen-Kuipers skill (HKS), and economic value were used to assess the APCN MME predictions. The member models have a range of skills in predicting the seasonal mean conditions: however, the MME schemes consistently produce improved predictions. Though the weighted multi-model ensemble schemes have better skill scores in terms of ACC, the unbiased multi-model ensemble mean provides better and more useful categorical predictions as seen from the HKS score and the economic values. Prediction skills show distinct seasonality in different regions. Precipitation prediction skill is the lowest in the Asian monsoon region in June-July-August. The APCN MME predictions have positive economic values in most regions and examination of monthly values suggests that the MME predictions are reasonably good and useful even with three-month lead times. An uncalibrated multi-model probabilistic prediction scheme gives an indication of threshold probability for which the seasonal predictions are more beneficial to users. The extreme climatic conditions are predicted well by the MME schemes. However, user sectors for which the extreme climate predictions are beneficial are different to those of regular three-category predictions. These results can be used in planning and preparedness for the extremes of climate in the future, and when developing economic decision support models.