Performance, comparison and utilization of reduced sulfur (-2) compounds (S2-, FeS and SCN-) in autotrophic denitrification process by thiosulfate-driven autotrophic denitrifier.

The coexistence of reduced sulfur (-2) compounds (S2-, FeS and SCN-) are found in some industrial wastewaters due to pre-treatment of Fe(II) salts. These compounds as electron donors have attracted increasing interest in autotrophic denitrification process. However, the difference of their functions still remain unknown, which limit efficient utilization in autotrophic denitrification process. The study aimed to investigate and compare utilization behavior of these reduced sulfur (-2) compounds in autotrophic denitrification process activated by thiosulfate-driven autotrophic denitrifiers (TAD). Results showed that the best denitrification performance was observed in SCN-; while the reduction of nitrate was significantly inhibited in S2- system and the efficient accumulation of nitrite was observed in FeS system with cycle experiments continuing. Additionally, intermediates containing sulfur were produced rarely in SCN- system. However, the utilization of SCN- was limited obviously in comparison with S2- in coexistence systems. Moreover, the presence of S2- increased the accumulation peak of nitrite in coexistence systems. The biological results indicated that the TAD utilized rapidly these sulfur (-2) compounds, in which genus of Thiobacillus, Magnetospirillum and Azoarcus might play main roles. Moreover, Cupriavidus might also participate in sulfur oxidation in SCN- system. In conclusion, these might be attributed to the characteristics of sulfur (-2) compounds including the toxicity, solubility and reaction process. These findings provide theoretical basis for regulation and utilization of these reduced sulfur (-2) compounds in autotrophic denitrification process.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox.

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (XAN/XTSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.

Implementation of a real-time fall detection system based on hybrid threshold analysis algorithm and machine learning algorithm.

With the increasing global aging population, the health of the elderly has become a global concern. Accidental falls, as one of the major causes of health and safety issues affecting the elderly, can cause serious hazards. In this paper, a fall detection system is proposed to be able to deliver timely information after a fall. The acceleration and angular velocity time series extracted from motion were used to describe human motion features. Hybrid threshold analysis algorithm and machine learning algorithm are used for classification between falls and activities of daily living (ADLs). The fall detection results showed 98.55% accuracy, 98.16% sensitivity, and 98.73% specificity. The result is higher than the single-threshold algorithm and slightly lower than the machine learning algorithm. In addition, the hybrid algorithm of fall detection in this paper is to put the threshold analysis algorithm in the edge device for calculation and put the machine learning algorithm in the cloud server for calculation. Since the single machine learning algorithm needs to transmit data to the cloud server all the time, the hybrid algorithm has lower power consumption than machine learning algorithms, and the average alarm time is shorter, making it more suitable for actual systems.