Mitigation of Greenhouse Gas Emissions from Urban Environmental Infrastructures

The world’s population will increase to 9.4 billion people by 2050 and 70% of whom will be living in urban areas. Such urbanization with population growth and industrial development demands in turn create a need for the planning, design, and construction of environmental infrastuctures (e.g., water and wastewater treatment plants: WTPs and WWTPs). The environmental infrastructures are essential to provide cities and towns with water supply, waste disposal, and pollution control services. During the operation of WTPs and WWTPs, massive amount of energy, fuels, and chemicals are consumed. Therefore, they could be major contributors to urban greenhouse gas (GHG) emissions (i.e., 17% of GHGs are generated from water and sewer sector in urban area). To make cities resilient and sustainable, the emission of GHGs from WTPs and WWTPs should be estimated as accurately as possible and effective mangement plans should be set up as soon as possible. A comprehensive model was developed to quantitatively estimate on-site and off-site GHGs generated from WTPs and WWTPs. The model was applied to an advanced WTP (treating 200,000 m³/d of raw water with micro-filtration membrane) and a hybrid WWTP (treating 5,500 m³/d of municipal wastewater with five-stage Bardenpho processes). The overall on-site and off-site GHG emissions from the advanced WTP and hybrid WWTP were 0.193 and 2.337 kgCO2e/d*m3. The major source of GHG generation in the advanced WTP was off-site GHG emissions (98.6%: production of chemicals consumed for on-site use and electricity consumed for unit-process operation). On the other hand, on-site GHG emissions related to biochemical reactions (64%) was the main GHG source of the hybrid WWTP. Reducing electricity consumption in advanced WTPs could be the best option for generating less GHG emissions and acquiring better water quality. Various options (CO2 capture and conversion to other useful materials, recovery and reused of CH4, and operation of WWTPs at optimal conditions) could significanlty reduce the total amount of GHG emissions in hybrid WWTPs. The results could be applied to the development of green and sustainable technology, leading to a change in paradigm of urban environmental infrastructure

Federated Cross Learning for Medical Image Segmentation

Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with the data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple and yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a round-robin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code will be made publicly available after paper publication.Comment: 10 pages, 4 figure

Soft-tissue Driven Craniomaxillofacial Surgical Planning

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.Comment: Early accepted by MICCAI 202

Flammability Characteristics and Mechanical Properties of Casein Based Polymeric Composites

Even though casein has an intrinsic potential ability to act as a flame retardant (FR) additive, the research regarding the FR performance of casein filled polymeric composites has not been thoroughly conducted. In the present work, two commercial casein products, such as lactic casein 720 (LAC) and sodium casein 180 (SC), were chosen to investigate their effects on the performances of the polypropylene (PP) composites. The melt compounding and compression moulding processes were employed to fabricate these casein-based composites. Ammonium polyphosphate (APP) was also selected to explore its combined effects in conjunction with casein on the composite’s flammability. The cone calorimeter results showed that the addition of casein significantly reduced (66%) the peak heat release rate (PHRR) of the composite compared to that of neat PP. In particular, the combination of LAC and APP led to the formation of more compact and rigid char compared to that for SC based sample; hence, a further reduction (80%) in PHRR and self-extinguishment under a vertical burn test were accomplished. Moreover, the tensile modulus of the composite improved (23%) by the combined effects of LAC and APP. The overall research outcome has established the potential of casein as a natural protein FR reducing a polymer’s flammability

Fabrication of Electro-Optic Devices Using Liquid Crystals With a Single Glass Substrate

A recently developed phase separated composite film method has been employed to fabricate a liquid crystal(LC) based electro-optical device using a single glass substrate. The resultant device is made of adjacent parallel layers of LC and polymer created by phase separation. The LC layer is confined between a film of solidified polymer layer on one side and the glass substrate on the other. Electro-optical properties of these devices demonstrate their technological potential in light weight and hand-held electronic products.</p

Efficient reduction of graphene oxide using Tin-powder and its electrochemical performances for use as an energy storage electrode material

A green and facile approach for the reduction of graphene oxide (GO) to graphene has been reported using Tin (Sn) powder and dilute hydrochloric acid. Reduction has been performed by varying time from 0.5 to 3 h at room temperature (RT) and 50 °C to determine the best conditions for high quality crystalline graphene. The as-prepared Sn-reduced GO (SR-GO) has been characterized by Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, scanning electron microscopy and Transmission electron microscopy. The efficiency of the reduction increases with increasing reduction time at RT and at 50 °C as evidenced by the electrical conductivity study. However, the electrical conductivity of SR-GO obtained at RT is significantly greater than that of SR-GO obtained at 50 °C. This is attributed to the presence of unreacted Sn particles that increase the electrical conductivity of graphene sheets, as evidenced by XPS elemental analysis. The electrochemical performances of SR-GOs were analyzed by cyclic voltammetry, charge–discharge and electrochemical impedance spectroscopy analysis. A maximum specific capacitance of 152 F g−1 at a current density of 1.5 A g−1 was recorded for graphene prepared at 50 °C for 3 h. The retention in specific capacitance was 92% after 1500 charge–discharge cycles, indicating good electrochemical cyclic stability of SR-GO and its suitability as an energy storage electrode material

Effects of hybrid carbon fillers of polymer composite bipolar plates on the performance of direct methanol fuel cells

The effects of carbon filler type on the properties and performance of composite bipolar plates fabricated by compression molding of carbon fillers such as graphite, carbon black (CB), multi-walled carbon nanotube (MWNT), carbon fiber (CF) and powder type epoxy have been investigated. The electrical conductivity and flexural properties of the composites are increased by increasing the content of fibrous conducting fillers, e.g. MWNT and CF. On the contrary, incorporation of particulate fillers such as CB and graphite plays a significant role in enhancing the electrical conductivity but has a negative effect on the flexural properties of the composites. The current–voltage curve of the fuel cell indicates that the performance of the fuel cell is improved upon selection of an optimum amount of carbon filler in the composite bipolar plates

CO₂ Hydrate Nucleation Kinetics Enhanced by an Organo-Mineral Complex Formed at the Montmorillonite–Water Interface

In this study, we investigated experimentally and computationally the effect of organo-mineral complexes on the nucleation kinetics of CO₂ hydrate. These complexes formed via adsorption of zwitter-ionic glycine (Gly-zw) onto the surface of sodium montmorillonite (Na-MMT). The electrostatic attraction between the −NH₃⁺ group of Gly-zw, and the negatively charged Na-MMT surface, provides the thermodynamic driving force for the organo-mineral complexation. We suggest that the complexation of Gly-zw on the Na-MMT surface accelerates CO₂ hydrate nucleation kinetics by increasing the mineral–water interfacial area (thus increasing the number of effective hydrate-nucleation sites), and also by suppressing the thermal fluctuation of solvated Na⁺ (a well-known hydrate formation inhibitor) in the vicinity of the mineral surface by coordinating with the −COO^– groups of Gly-zw. We further confirmed that the local density of hydrate-forming molecules (i.e., reactants of CO₂ and water) at the mineral surface (regardless of the presence of Gly-zw) becomes greater than that of bulk phase. This is expected to promote the hydrate nucleation kinetics at the surface. Our study sheds new light on CO₂ hydrate nucleation kinetics in heterogeneous marine environments, and could provide knowledge fundamental to successful CO₂ sequestration under seabed sediments