Catheters are increasingly being used to tackle problems in the cardiovascular system. However, positioning precision of the catheter tip is negatively affected by hysteresis. To ensure tissue damage due to imprecise positioning is avoided, hysteresis is to be understood and compensated for. This work investigates the feasibility to model hysteresis with a Long Short-Term Memory (LSTM) network. A bench-top setup containing a catheter distal segment was developed for model evaluation.The LSTM was first tested using four groups of test datasets containing diverse patterns. To compare with the LSTM, a Deadband Rate-Dependent Prandtl-Ishlinskii (DRDPI) model and a Support Vector Regression (SVR) model were established. The results demonstrated that the LSTM is capable of predicting the tip bending angle with sub-degree precision. The LSTM outperformed the DRDPI model and the SVR model by 60.1% and 36.0%, respectively, in arbitrarily varying signals. Next, the LSTM was further validated in a 3D reconstruction experiment using Forward-Looking Optical Coherence Tomography (FL-OCT). The results revealed that the LSTM was able to accurately reconstruct the environment with a reconstruction error below 0.25 mm. Overall, the proposed LSTM enabled precise free-space control of a robotic catheter in the presence of severe hysteresis. The LSTM predicted the catheter tip response precisely based on proximal input pressure, minimizing the need to install sensors at the catheter tip for localization.

The characteristics of turbulent boundary layer over streamwise aligned drag reducing riblet surface under zero-pressure gradient are investigated using particle image velocimetry. The formation and distribution of large-scale coherent structures and their effect on momentum partition are analyzed using two-point correlation and probability density function. Compared with smooth surface, the streamwise riblets reduce the friction velocity and Reynolds stress in the turbulent boundary layer, indicating the drag reduction effect. Strong correlation has been found between the occurrence of hairpin vortices and the momentum distribution. The number and streamwise length scale of hairpin vortices decrease over streamwise riblet surface. The correlation between number of uniform momentum zones and Reynolds number remains the same as smooth surface.

This study investigated the feasibility of a new biological nitrogen removal process that integrates sulfur-driven autotrophic denitratation (NO₃ ⁻→NO₂ ⁻) and anaerobic ammonium oxidation (Anammox) for simultaneous removal of nitrate and ammonium from industrial wastewater. The proposed sulfur(thiosulfate)-driven denitratation and Anammox process was developed in two phases: First, the thiosulfate-driven denitratation was established in the UASB inoculated with activated sludge and fed with ammonium, nitrate and thiosulfate for 52 days until the nitrite level in the effluent reached 32.1 mg N/L. Second, enriched Anammox biomass was introduced to the UASB to develop the integrated thiosulfate-driven denitratation and Anammox (TDDA) bioprocess (53–212 d). Results showed that nitrate and ammonium could be efficiently removed from synthetic wastewater by the integrated TDDA system at a total nitrogen (TN) removal efficiency of 82.5 ± 1.8% with an influent NH₄ ⁺-N of 101.2 ± 2.2 mgN/L, NO₃ ⁻-N of 101.1 ± 1.5 mgN/L and thiosulfate of 202.5 ± 3.2 mg S/L. It was estimated that Anammox and autotrophic denitritation (NO₂ ⁻→N₂) contributed to about 90% and 10% of the TN removal respectively at stable operation. The established TDDA system was further supported by high-throughput sequencing analysis that sulfur-oxidizing bacteria (e.g., Thiobacillus and Sulfurimonas) coexisted with Anammox bacteria (e.g., Ca. Kuenenia and Ca. Anammoxoglobus) in this syntrophic biocenosis. Additionally, batch experiments were conducted to reveal the kinetic rates and to reconcile the stoichiometry of the electron donor/acceptor couples of the TDDA process. The results unraveled the mechanisms in the new bioprocess: i) sulfite and elemental sulfur (S⁰) were initially generated from branched thiosulfate; ii) oxidation of sulfite and elemental sulfur coupled with fast and slow denitratation; iii) nitrite produced from denitratation together with ammonium were effectively converted to dinitrogen gas via Anammox.

Sulfide-oxidizing autotrophic denitrification (SOAD) implemented in a moving-bed biofilm reactor (MBBR) is a promising alternative to conventional heterotrophic denitrification in mainstream biological nitrogen removal. The sulfide-oxidation intermediate – elemental sulfur – is crucial for the kinetic and microbial properties of the sulfur-oxidizing bacterial communities, but its role is yet to be studied in depth. Hence, to investigate the performance and microbial communities of the aforementioned new biosystem, we operated for a long term a laboratory-scale (700 d) SOAD MBBR to treat synthetic saline domestic sewage, with an increase of the surface loading rate from 8 to 50 mg N/(m²·h) achieved by shortening the hydraulic retention time from 12 h to 2 h. The specific reaction rates of the reactor were eventually increased up to 0.37 kg N/(m³·d) and 0.73 kg S/(m³·d) for nitrate reduction and sulfide oxidation with no significant sulfur elemental accumulation. Two sulfur-oxidizing bacterial (SOB) clades, Sox-independent SOB (SOB_I) and Sox-dependent SOB (SOB_II), were responsible for indirect two-step sulfur oxidation (S²⁻→S⁰→SO₄ ²⁻) and direct one-step sulfur oxidation (S²⁻→SO₄ ²⁻), respectively. The SOB_II biomass-specific electron transfer capacity could be around 2.5 times greater than that of SOB_I (38 mmol e⁻/(gSOB_II·d) versus 15 mmol e⁻/(gSOB_I·d)), possibly resulting in the selection of SOB_II over SOB_I under stress conditions (such as a shorter HRT). Further studies on the methods and mechanism of selecting of SOB_II over SOB_I in biofilm reactors are recommended. Overall, the findings shed light on the design and operation of MBBR-based SOAD processes for mainstream biological denitrification.