Multi-level filter based on H shaped channels

Micro and nano filtration is a crucial fundamental process in biological, biomedical and chemical engineering fields. This project aims to establish a multilevel filter for separation and purification complex bio-components mixtures by size in both micro and nano scales; generalize the design methodology of this multi level filter; optimise traditional nanofilter and eventually develop a multi level filter for rapid micro volume sample filtration. During the study and improvement of traditional filter, a new type of multi level filter for sequential separation based on H shaped channels were put forward in this thesis. The methodology of designing this type multi level filter was built and verified with simulations. A general path for multi level filter chip design was established and demonstrates with examples and experiments. A new fabrication process for multi depths channel on the same surface was put forward during the examples design. The fabrication experiments demonstrated the feasibility of this fabrication process and the recipes of these three fabrication processes were given by the experiments. Simulations on both 2D and 3D filters were described and discussed. The angle effect of angular H shaped channels was studied with COMSOL Multiphysics® Modeling Software

Biodiversity and temporal patterns of macrozoobenthos in a coal mining subsidence area in North China

Coal resources play a strategic role in the long-term development of China. Large-scale mining has a considerable impact on the landscape, and it is a long-term heritage of industrialization unique to the Anthropocene. We investigated the macrozoobenthos and water in nine mining subsidence wetlands at different developmental stages (3–20 years) in North China. A total of 68 species were found, and the macrozoobenthos community in the newly formed wetlands showed high diversity. We believe that this high diversity is not random; rather, the high diversity was because of the special origin and development of the wetland. We used three time slices from the timeline of the development of the newly formed wetlands and compared them. It was found that the macrozoobenthos community was significantly affected by the change in the subsidence history. We emphasize that coal mining subsidence should not be merely identified as secondary man-made disasters, as they are often secondary habitats with high conservation value, and their conservation potential lies in the fact that these secondary habitats can replace rapidly decreasing natural wetlands

Nitrogen-doped hierarchical porous carbons derived from biomass for oxygen reduction reaction

Nowadays biomass has become important sources for the synthesis of different carbon nanomaterials due to their low cost, easy accessibility, large quantity, and rapid regeneration properties. Although researchers have made great effort to convert different biomass into carbons for oxygen reduction reaction (ORR), few of these materials demonstrated good electrocatalytical performance in acidic medium. In this work, fresh daikon was selected as the precursor to synthesize three dimensional (3D) nitrogen doped carbons with hierarchical porous architecture by simple annealing treatment and NH3 activation. The daikon-derived material Daikon-NH3-900 exhibits excellent electrocatalytical performance towards oxygen reduction reaction in both alkaline and acidic medium. Besides, it also shows good durability, CO and methanol tolerance in different electrolytes. Daikon-NH3-900 was further applied as the cathode catalyst for proton exchange membrane (PEM) fuel cell and shows promising performance with a peak power density up to 245 W/g

Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.Comment: Accepted for publication in IEEE INFOCOM 202

LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits a best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low latency. In our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in a best latency of 4 steps and an expected worst latency of $12(t+1)$ steps, where $t$ represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices

Integrated chemical characterization, metabolite profiling, and pharmacokinetics analysis of Zhijun Tangshen Decoction by UPLC-Q/TOF-MS

Diabetic nephropathy (DN) is the main cause of end-stage renal disease worldwide and a major public issue affecting the health of people. Therefore, it is essential to explore effective drugs for the treatment of DN. In this study, the traditional Chinese medicine (TCM) formula, Zhijun Tangshen Decoction (ZJTSD), a prescription modified from the classical formula Didang Decoction, has been used in the clinical treatment of DN. However, the chemical basis underlying the therapeutic effects of ZJTSD in treating DN remains unknown. In this study, compounds of ZJTSD and serum after oral administration in rats were identified and analyzed using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS). Meanwhile, a semi-quantitative approach was used to analyze the dynamic changes in the compounds of ZJTSD in vivo. UPLC-Q/TOF-MS analysis identified 190 compounds from ZJTSD, including flavonoids, anthraquinones, terpenoids, phenylpropanoids, alkaloids, and other categories. A total of 156 xenobiotics and metabolites, i.e., 51 prototype compounds and 105 metabolites, were identified from the compounds absorbed into the blood of rats treated with ZJTSD. The results further showed that 23 substances with high relative content, long retention time, and favorable pharmacokinetic characteristics in vivo deserved further investigations and validations of bioactivities. In conclusion, this study revealed the chemical basis underlying the complexity of ZJTSD and investigated the metabolite profiling and pharmacokinetics of ZJTSD-related xenobiotics in rats, thus providing a foundation for further investigation into the pharmacodynamic substance basis and metabolic regulations of ZJTSD