Search

Duckweed, as raw material of biomass energy, was pretreated by using pyrolysis and then used for electricity generation in microbial fuel cell(MFC). Experimental result showed that the optimized conditions for the pretreatment were:reaction temperature of 160℃;reaction time of 80 min, oxalic acid dosage of 3%, in which the reducing sugar reached 0.272 g for 1 g duckweed, and digestibility of duckweed reached at 55%. The maximum power density was 521 mW·m-2, the minimum total internal resistance was 145.9 Ω, and maximum Coulombic efficiency(CE) was 12.1%, when the pyrolyzed solution was diluted 10 times. COD removal ratio was more than 91% with concentrations of the pyrolyzed solutions increasing, and final pH was kept at 7.7-8.0, although the maximum voltage output of MFC was not apparently changed. The results of three-dimensional fluorescence spectrum analysis showed that all organic compounds in the influent were effectively biodegraded. The relative abundance of Acetoanaerobium was 30.1%, 42.2% and 33.8% corresponding to the solution dilution of 20, 10 and 5 folds, respectively.The highest relative abundance of Proteiniclasticum was 19.5% under the 20 folds diluted the hydrolyzed solution, while, the highest relative abundance of Proteiniphilum reached 16.0% under the 5 folds diluted the solution.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

The 2, 4-dichlorophenoxyacetic acid (2, 4-D) online immunosensor was characterized from the point of application in this study. Our experimental results showed that Logistic curve could be used to describe the response of 2, 4-D online immunosensor to the 2, 4-D concentration. In a range of 10~200 μg/L of 2, 4-D, half logarithmic linear equation could be used to describe the response of 2, 4-D online immunosensor to the 2, 4-D concentration. The memory effect happened on the 2, 4-D online immunosensor. According to 48 series of data, the total error of determination in the 2, 4-D online immunosensor was lower than that in the labor measurement. The 2, 4-D online immunosensor could be used in the Xijiang River with high operational stability and signal reliability. Our results should provide a promising way to realize the online early warning management on toxic organic compounds in water environment.

Grain closely involves people's livelihood. Prompt decisions of higher efficiency, better quality and lower cost stand in the center of grain emergency measures. The dissertation introduces the Domain Ontology construction method based on Upper Ontology and constructs the Domain Knowledge Base of grain emergency through researching on the construction tools and methodology of Ontology, the description language, Upper Ontology and so on. The dissertation designs an intelligent casesretrieval algorithm on the basis of semantic similarity calculation. Finally, many efforts are made for the actualized of the intelligent emergency decision system of grain. The result shows that this system can find the solution of the similar problem quickly and serve for the accidental public emergency.

With the frequent natural disasters in recent years, rural emergency problem has been more and more attention and study. A new rural emergency public service system is proposed based on WebGIS under network environment. Using the J2EE open source framework, existing data of public emergency can be integrated in our rural emergency. Moreover, rural emergency management and emergency resources schedule can be realized. Firstly, design ideas of rural emergency public services system were discussed and proposed. Then, emergency system architecture was designed. Third, the main function of rural emergency system and technical characteristics were summarized. Finally, the system was implemented and tested. After system running test online, it is concluded that our emergency system in rural areas with high real value.

Based on economic control theory and data mining, grain market price impact analysis system was established to analyze the influence brought by macro-control measures. From two angles, this paper selects macroeconomic regulation indicators and grain control from production and circulation and consumption and stock fields as measures. Firstly, Comprehensive effect based on level indicators was analyzed by analytic hierarchy process (AHP). Secondly, fuzzy comprehensive evaluation method was used to estimate the probability of achieving the desired effects after the implementation of the price control program. This system develops these analysis toolboxes based on the powerful software Matlab and exploits visual interface based on ASP Technolgy.

Data mining on uncertainty is concerned with. Support vector machine is a powerful classification technique and has been successfully applied to many real-world problems. However, it is required that the values of input indices must be real numbers. A new fuzzy SVM technique is proposed which can deal with the training fuzzy data. A definition of fuzzy coefficient quadratic programming is given as a direct result of fuzzy SVM. Meanwhile, a useful method is presented to solve the programming. By data experiment, ordinary SVM can be viewed as a special case of fuzzy SVM.

A two-chambered microbial fuel cell (MFC) was designed to test the feasibility of organics degradation and electricity production, simultaneously, by using various organics including glucose, glucose-phenol mixture, and phenol as fuel. The experimental results showed that the shortest start-up time was achieved by using glucose and the longest was by using phenol as fuel. During the long-term experiments, the average operation period was 400h, 200h, and 100h for glucose, glucose-phenol mixture, and phenol, respectively. The maximal voltage outputs were obtained by 551 mV, 208 mV, and 121 mV (external resistance of 1000Ω), respectively for glucose, glucose-phenol mixture, and phenol. The results indicated that the MFC could make use of phenol for generating power with effective biodegradation. The MFC process may provide a new way to treat the refractory biodegraded organics with excess electrical energy production at the same time.

Ordinal regression is complementary to the standard machine learning tasks of classification and metric regression which goal is to predict variables of ordinal scale. However, every input must be exactly assigned to one of these classes without any uncertainty in standard ordinal regression models. Based on structural risk minimization (SRM) principle, a new support vector learning technique for ordinal regression is proposed, which is able to deal with training data with uncertainty. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, two algorithms have been derived. This technique extends the application horizon of ordinal regression greatly. Moreover, the problem about early warning of food security in China is solved by our algorithm.

Support vector techniques have been successfully applied to many real-world problems, but it is difficult to select the parameter C. The v-support vector classification (v-SVC) has the advantage of a parameter v on controlling the number of support vectors. However, it is required that every input must be exactly assigned to one of these two classes without any uncertainty. A new v-SVM technique is proposed which is able to deal with training data with uncertainty based on expert advices. Firstly, the meaning of the uncertainty is defined. Based on this meaning of uncertainty, the algorithm has been derived. This technique extends the application horizon of v-SVM greatly. As an application, the problem about early warning of grain production is solved by our algorithm.