Doctor of Philosophy

dissertationElectrorefining is widely utilized to refine nonferrous metals such as copper, zinc, and nickel as a final step to meet purity requirements. Thus, it is critical to control impurities and maintain high cathode purity in electrorefining. In copper electrorefining, slime particles are responsible for most cathode contamination. As a result, the adhesion, mobility, and transport of anode slime particles in flowing electrolyte are of significance and worth comprehensive studies. A 3-factor 2-level designed set of experiments was performed to determine the effects of inlet flow rate, temperature, and current density on impurity particle behavior in electrolyte and the associated distribution on the cathode in copper electrorefining. A model based in COMSOL Multiphysics® consisting of an electrorefining cell was utilized to simulate copper electrorefining. The model data for impurity particle distribution were compared with measured impurity particle contamination at the cathode surface, and the results show a very good correlation. Four series of copper electrorefining tests were performed using four different types of anodes. Test results show that the high impurity anodes and the scrap cycle anodes have more inclusions associated with the Pb-Bi-S compounds that show evidence of sintering at 50 ℃, whereas the low impurity anodes and the strip cycle anodes have more inclusions related with the Pb-Bi-S-As compounds that demonstrate evidence of sintering above 65 ℃. Arsenic content in copper anode and cell temperature are major factors affecting slime sintering and coalescence, which can improve anode slime adhesion and reduce the amount of suspended slimes. Copper electrorefining tests were conducted in a pilot scale cell made of transparent cell walls. Fluid flow velocities in the gaps between adjacent electrodes were measured. Modeling and simulation of copper electrorefining in this cell were performed. The flow velocity field results from modeling agree reasonably well with the measured electrolyte velocities. The effects of anode compositions, current density, cathode blank width, and flow rate on anode slime behavior and cathode copper purity were studied by performing copper electrorefining tests in the pilot scale cell under commercial tankhouse environment

Cadmium Depth Separation Method in Polymetallic Sulfate Solution: Flow-Electric Field Enhanced Cementation Combined with M5640 Extraction

An efficient and controllable process for separating copper and cadmium was required to be developed due to the high cost of the long separation process of copper cadmium slag generated from the zinc smelting process. Therefore, a new process for the application and deep separation of copper and cadmium was developed by combining the Circulating Flow Electric (CFE) cadmium cement method and the 2-hydroxy-5-nonyl formaldehyde oxime (M5640) copper extract method. The process firstly removed copper ions utilizing M5640 and obtained a primary purification solution, followed by CFE method to extract cadmium in depth. The effects of extractant volume fraction, pH, Oil phase/Aqueous phase (O/A) ratio and reaction time on the removal of copper ions were investigated. The results showed that the removal of copper was above 97%, while the removal of zinc and cadmium was below 1.6%, respectively, proved that the selectivity of M5640 for copper was significantly higher than that for metals such as cadmium and zinc. The characterization results indicate that the oxygen on the hydroxyl group and the nitrogen on the oxime group co-ligated with the copper ions and subsequently formed chelated extracts. That was the mechanism of the copper ion purification by M5640. Furthermore, the extraction of high purity cadmium was carried out in the extraction residual liquid. A novel method of cadmium removal enhanced by coupling an electric field with a circulating flow field was developed and applied to the cement cadmium from sulfate solutions. The optimal process conditions of the method were explored, which were further fitted into statistical equations and optimized by response surface analysis. Since the fitted theoretical results were close to the experimental results, the optimization was considered as effective. The optimized experimental parameters were 6.23 mL/s of flow rate, 48.14 mA/cm2 of current density, 2.25 of pH, and 0.93 of anode/cathode area ratio, respectively. Next, the extraction electrical efficiency, purity and its weight distribution in the cell of cadmium sponge under different flow fields were calculated and measured. The results were analyzed to prove the existence of an optimal interval for the distribution of cadmium under high-speed flow field

Traffic Sign Recognition using Optimized Federated Learning in Internet of Vehicles

Traffic Sign Recognition (TSR) is vital for vehicle safety and navigation, especially in the era of autonomous cars. Internet of Vehicles (IoV) provide a promising infrastructure for vehicular networks due to their agility and interoperability. However, privacy concerns and network restrictions hinder the collection of massive data from distributed automotive sensors in IoV. To address these challenges, this paper proposes the application of Federated Learning (FL) and model sparsification to optimize TSR in autonomous vehicles. FL enables decentralized learning while preserving data privacy, and model sparsification significantly reduces communication costs. Furthermore, we incorporate the Adam optimizer for local training, ensuring efficient model optimization on each vehicle. Experimental results demonstrate the effectiveness of our approach, with improved TSR performance while mitigating privacy risks and enhancing communication efficiency. This research contributes to the advancement of TSR in IoV by introducing FL, model sparsification, and the use of the Adam optimizer for local training, facilitating efficient and privacy-preserving vehicular network learning

Towards Intelligent Attack Detection Using DNA Computing

In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.</p

Delay-masquerading Technique Upheld StrongBox:A Reinforced Side-Channel Protection

In recent years, Graphical Processing Unit (GPU) is not only becoming a piece of hardware that accelerates graphics but also playing a key role in accelerating the fields of machine learning and artificial intelligence. The GPU’s heightened importance has led to increasing concern about the confidentiality of a GPU’s computing data as well as its internal communications. Although the GPU Trusted Execution Environment (TEE) has been implemented as a solution toward this issue, side-channel attacks in GPUs still remain as an open problem. In this work, we introduce Delay-masquerading Technique Upheld StrongBox (DTUBox) to strengthen the resilience of existing GPU TEE over StrongBox against side-channel attacks by injecting obfuscated noise with our developed algorithm, making the correlations difficult to reference between a task and workload. In our evaluation, we demonstrate that with only around 5% performance overhead, our approach could effectively lower the correlation rate to 38% between the original behavior sequences and the obfuscated sequences

Comparing carbon sequestration and stand structure of monoculture and mixed mangrove plantations of Sonneratia caseolaris and S apetala in Southern China

NSFC [30700092]; Key Project from the State Oceanic Administration [200905009-4]Mangroves are one of the most carbon-rich tropical ecosystems. Two fast-growing mangrove species of the genus Sonneratia, the native S. caseolaris and non-native S. apetala, have been widely used for mangrove reforestation in China; however their ability to sequester carbon is still unclear. The present study aimed to estimate the growth, carbon accumulation in biomass and carbon sequestration in sediments of these two species, in both mixed (50% S. caseolaris: 50% S. apetala) and monoculture plantations, in the intertidal zones of Shenzhen Bay, Guangdong Province, China. Twenty-five months of observation showed strong competition between the two species in the mixed plantation, with the native S. caseolaris outcompeting the non-native S. apetala due to a faster growth rate. Although S. caseolaris in the mixed plantation had lower carbon storage in biomass than in monoculture, carbon accumulation in sediment was higher in the mixed plantation. Thus, the relative advantage of the mixed plantation was in sequestering more carbon in sediment, as opposed to high carbon accumulation to biomass. These results indicated that the mixed plantation could be a good option for mangrove restoration and carbon sequestration of sediment. However, the two and a half years of this study may not indicate long-term trends, so more research on long-term species performance is essential for successful mangrove reforestation. (C) 2012 Elsevier B.V. All rights reserved

Circular RNA AKT3 upregulates PIK3R1 to enhance cisplatin resistance in gastric cancer via miR-198 suppression

Abstract Background Cisplatin (CDDP) treatment is one of the most predominant chemotherapeutic strategies for patients with gastric cancer (GC). A better understanding of the mechanisms of CDDP resistance can greatly improve therapeutic efficacy in patients with GC. Circular RNAs (circRNAs) are a class of noncoding RNAs whose functions are related to the pathogenesis of cancer, but, in CDDP resistance of GC remains unknown. Methods circAKT3 (hsa_circ_0000199, a circRNA originating from exons 8, 9, 10, and 11 of the AKT3 gene) was identified by RNA sequencing and verified by quantitative reverse transcription PCR. The role of circAKT3 in CDDP resistance in GC was assessed both in vitro and in vivo. Luciferase reporter assay, biotin-coupled RNA pull-down and fluorescence in situ hybridization (FISH) were conducted to evaluate the interaction between circAKT3 and miR-198. Functional experiments were measured by western blotting, a cytotoxicity assay, clonogenic assay and flow cytometry. Results The expression of circAKT3 was higher in CDDP-resistant GC tissues and cells than in CDDP-sensitive samples. The upregulation of circAKT3 in GC patients receiving CDDP therapy was significantly associated with aggressive characteristics and was an independent risk factor for disease-free survival (DFS). Our data indicated that circAKT3 promotes DNA damage repair and inhibits the apoptosis of GC cells in vivo and in vitro. Mechanistically, we verified that circAKT3 could promote PIK3R1 expression by sponging miR-198. Conclusions circAKT3 plays an important role in the resistance of GC to CDDP. Thus, our results highlight the potential of circAKT3 as a therapeutic target for GC patients receiving CDDP therapy