In the existence of appropriate amount of disodium ethylenediaminetetraacetate (EDTA), precipitation would not occur in seawater and other natural waters even if the sample solution was adjusted to strong basicity, and the NH3-OPA-sulfite reaction at the optimal pH range could be used to determine ammonium in natural waters. Based on this, a modified o-phthalaldehyde fluorometric analytical method has been established to determine ultratrace ammonium in natural waters. Experimental parameters, including reagent concentration, pH, reaction time, and effect of EDTA, were optimized throughout the experiments based on univariate experimental design. The results showed that the optimal pH range was between 10.80 and 11.70. EDTA did not obviously affect the fluorometric intensity. The linearity range of the proposed method was 0.032–0.500 µmol/L, 0.250–3.00 µmol/L, and 1.00–20.0 µmol/L at the excitation/emission slit of 3 nm/5 nm, 3 nm/3 nm, and 1.5 nm/1.5 nm, respectively. The method detection limit was 0.0099 µmol/L. Compared to the classical OPA method, the proposed method had the advantage of being more sensitive and could quantify ultratrace ammonium without enrichment.
Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.
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