Publications by authors named "Chunyang Su"

Background: Venous thromboembolism (VTE) is a common complication after hip arthroplasty. Here, we investigated the clinical efficacy and safety of prophylactic aspirin vs. conventional therapy in hip arthroplasty for femoral neck fracture.

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This meta-analysis evaluated the effects of ultrasound-guided (USG) and landmark-guided (LMG) injections of corticosteroids into the shoulder joint cavity of patients with shoulder pain. The PubMed database was searched for articles (January 1, 2004-December 31, 2023) comparing USG and LMG injections of corticosteroids for the treatment of adult shoulder pain. Two authors independently performed data extraction and appraisal.

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Despite the increased research efforts, full-scale implementation of shortcut nitrogen removal strategies has been challenged by the lack of consistent nitrite-oxidizing bacteria out-selection. This paper proposes an alternative path using partial denitrification (PdN) selection coupled with anaerobic ammonium-oxidizing bacteria (AnAOB). A nitrate residual concentration (>2 mg N/L) was identified as the crucial factor for metabolic PdN selection using acetate as a carbon source, unlike the COD/N ratio which was often suggested.

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In this study, concurrent operation of anammox and partial denitrification within a nonacclimated mixed culture system was proposed. The impact of carbon sources (acetate, glycerol, methanol, and ethanol) and COD/NO3 -N ratio on partial denitrification selection under both short- and long-term operations was investigated. Results from short-term testing showed that all carbon sources supported partial denitrification.

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Treatment of sewage sludge with a thermal hydrolysis process (THP) followed by anaerobic digestion (AD) enables to boost biogas production and minimize residual sludge volumes. However, the reject water can cause inhibition to aerobic and anoxic ammonium-oxidizing bacteria (AerAOB & AnAOB), the two key microbial groups involved in the deammonification process. Firstly, a detailed investigation elucidated the impact of different organic fractions present in THP-AD return liquor on AerAOB and AnAOB activity.

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Substrate limitation occurs frequently in wastewater treatment and knowledge about microbial behavior at limiting conditions is essential for the use of biokinetic models in system design and optimization. Monod kinetics are well-accepted for modeling growth rates when a single substrate is limiting, but several models exist for treating two or more limiting substrates simultaneously. In this study three dual limitation models (multiplicative, minimum, and Bertolazzi) were compared based on experiments using nitrite-oxidizing bacteria (limited by dissolved oxygen and nitrite) and ANaerobic AMMonia-OXidizing bacteria or Aanammox (limited by ammonium and nitrite) within mixed liquor from deammonification pilots.

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This study monitored three different activated sludge systems from the Blue Plains Advanced Wastewater Plant for a 1 year period to explore the relationship between effluent quality and activated sludge settling and flocculation behavior. Hindered settling rates (ISV) and sludge volume index (SVI) measurements were collected weekly. Novel metrics based on the solids concentration at which the transition between settling regimens occurred were also collected weekly.

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Article Synopsis
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The thermal hydrolysis process (THP) has been proven to be an excellent pretreatment step for an anaerobic digester (AD), increasing biogas yield and decreasing sludge disposal. The goal of this work was to optimize deammonification for efficient nitrogen removal despite the inhibition effects caused by the organics present in the THP-AD sludge filtrate (digestate). Two sequencing batch reactors were studied treating conventional digestate and THP-AD digestate, respectively.

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When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights.

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