Enhanced nitrogen removal via Yarrowia lipolytica-mediated nitrogen and related metabolism of Chlorella pyrenoidosa from wastewater

We investigated the optimum co-culture ratio with the highest biological nitrogen removal rate, revealing that chemical oxygen demand, total nitrogen (TN), and ammoniacal nitrogen (NH3-N) removal was increased in the Chlorella pyrenoidosa and Yarrowia lipolytica co-culture system at a 3:1 ratio. Compared with the control, TN and NH3-N content in the co-incubated system was decreased within 2–6 days. We investigated mRNA/microRNA (miRNA) expression in the C. pyrenoidosa and Y. lipolytica co-culture after 3 and 5 days, identifying 9885 and 3976 differentially expressed genes (DEGs), respectively. Sixty-five DEGs were associated with Y. lipolytica nitrogen, amino acid, photosynthetic, and carbon metabolism after 3 days. Eleven differentially expressed miRNAs were discovered after 3 days, of which two were differentially expressed and their target mRNA expressions negatively correlated with each other. One of these miRNAs regulates gene expression of cysteine dioxygenase, hypothetical protein, and histone-lysine N-methyltransferase SETD1, thereby reducing amino acid metabolic capacity; the other miRNA may promote upregulation of genes encoding the ATP-binding cassette, subfamily C (CFTR/MRP), member 10 (ABCC10), thereby promoting nitrogen and carbon transport in C. pyrenoidosa. These miRNAs may further contribute to the activation of target mRNAs. miRNA/mRNA expression profiles confirmed the synergistic effects of a co-culture system on pollutant disposal

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks.</jats:p

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks