Using Artificial Intelligence to Predict the Discharge Performance of Cathode Materials for Lithium-ion Batteries Applications

A comprehensive understanding of the composition-structure-property relationships for doped cathode materials used in lithium-ion batteries remains lacking which delays the progress of developing new cathode materials. This thesis proposes that machine learning (ML) techniques can be used to predict the discharge capacities of the cathode materials whilst revealing these underlying relationships. To achieve this, the data for three different doped cathodes are curated from the publications, namely, the doped spinel cathode, LiMxMn2−xO4, the M-doped nickel- cobalt-manganese layered cathode, LiNixCoyMnzM1−x−y−zO2, and the carbon -coated and doped olivine cathode, C/LiM1M2PO4 (M1, M2 denote different metal ions). Several linear and non-linear ML models are trained with the data and compared for the power of predicting initial and higher cycle discharge capacity. Gradient boosting models have shown the best prediction power for predicting the initial and 20th cycle end discharge capacity of 102 doped spinel cathode and the initial and 50th cycle discharge capacity of 168 doped nickel-cobalt-manganese layered cathodes. For the doped spinel cathode, higher discharge capacities at both cycles can be achieved through increasing the material formula mass, reducing the crystal lattice constant and using dopants with smaller electronegativity. For the doped layered cathodes, it is revealed that the higher lithium content, lower formula molar mass, small doping content and doped with low electronegativity dopant are more likely to possess greater capacities at both cycles. Bayesian ridge regression and gradient boosting model are shown to have the highest prediction power over the initial and the 20th cycle discharge capacity of carbon-coated and doped olivine cathode. In addition, the olivine systems with lower dopant content, higher base-metal content and smaller unit cells are shown to be more likely to possess higher capacities at both cycles. Finally, future research directions are presented including the suggestion of involving other new input variables and using principal component analysis and feature selection algorithms to use to improve the model performance

An Engine and Shock Tube Study in Combustion Soot Measurement

Soot phenomena in combustion have been an interesting topic in combustion research for a long time. Study of soot includes identifying reaction pathways leading to soot and developing innovative techniques and apparatus to measure soot. In this research, two separate experiments are conducted to explore possible new ways to study soot formation mechanisms in combustion. The first experiment employs a miniature engine to prove the feasibility of coupling high pressure reactors with synchrotron sourced photoionization mass spectrometry and probing species concentration in the combustion chamber and the exhaust. The second experiment demonstrates the feasibility of measuring extremely low level scattering signals by employing a high repetition rate shock tube and sensitive optical components to quantitatively measure the number density and size distribution of soot generated in the shock tube. Technical challenges and solutions of conducting the two experiments will be presented in details. Results of the two experiments will be discussed and potential improvements in the future will be given regarding the results of the currents experiments.Master of Science in EngineeringMechanical Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136066/1/An Engine and Shock Tube Study in Combustion Soot Measurement.pdfDescription of An Engine and Shock Tube Study in Combustion Soot Measurement.pdf : Master of Science in Engineering Thesi

Health Information Seeking Behaviour during the Pandemic: Exploring the experiences of Chinese International Students

Many Chinese students choose Australia to study abroad. Studying abroad is a great opportunity, but it can also bring challenges for international students. In particular, during the Covid-19 pandemic, Chinese international students in Australian universities have new challenges in managing health information and misinformation across different platforms. There is currently a lack of research on Chinese international students’ health information-seeking strategies. A deeper understanding of Chinese international students’ social, cultural, and welfare support through online health information-seeking behaviour can be important. This can help support providers, educational institutions and governments to formulate better public health strategies for supporting international students. It will also help students better understand effective health information-seeking strategies. This study first synthesized relevant articles on information-seeking behaviour by international students. Then it identifies the gaps in studying Chinese international students’ health information-seeking behaviour. Finally, recommendations are made to bridge the identified gaps

A Physical Theory of the Competition that Allows HIV to Escape from the Immune System

Competition within the immune system may degrade immune control of viral infections. We formalize the evolution that occurs in both HIV-1 and the immune system quasispecies. Inclusion of competition in the immune system leads to a novel balance between the immune response and HIV-1, in which the eventual outcome is HIV-1 escape rather than control. The analytical model reproduces the three stages of HIV-1 infection. We propose a vaccine regimen that may be able to reduce competition between T cells, potentially eliminating the third stage of HIV-1.Comment: 5 pages, 2 figures, to appear in Phys. Rev. Let

Generation of Chinese classical poetry based on pre-trained model

In order to test whether artificial intelligence can create qualified classical poetry like humans, the author proposes a study of Chinese classical poetry generation based on a pre-trained model. This paper mainly tries to use BART and other pre training models, proposes FS2TEXT and RR2TEXT to generate metrical poetry text and even specific style poetry text, and solves the problem that the user's writing intention gradually reduces the relevance of the generated poetry text. In order to test the model's results, the authors selected ancient poets, by combining it with BART's poetic model work, developed a set of AI poetry Turing problems, it was reviewed by a group of poets and poetry writing researchers. There were more than 600 participants, and the final results showed that, high-level poetry lovers can't distinguish between AI activity and human activity, this indicates that the author's working methods are not significantly different from human activities. The model of poetry generation studied by the author generalizes works that cannot be distinguished from those of advanced scholars. The number of modern Chinese poets has reached 5 million. However, many modern Chinese poets lack language ability and skills as a result of their childhood learning. However, many modern poets have no creative inspiration, and the author's model can help them. They can look at this model when they choose words and phrases and they can write works based on the poems they already have, and they can write their own poems. The importance of poetry lies in the author's thoughts and reflections. It doesn't matter how good AI poetry is. The only thing that matters is for people to see and inspire them.Comment: 8 pages,2 figure

YOGA’S THERAPEUTIC EFFECT ON PERINATAL DEPRESSION: A SYSTEMATIC REVIEW AND META-ANALYSIS

Introduction: In recent years, the incidence of perinatal depression in female population is very high. Perinatal depression has adverse effects on the physical and mental health of mothers and children. However, according to current researches, Yoga has been considered as an effective exercise that can help pregnant women to regulate their emotions. Thus, this review reports the effectiveness of yoga on perinatal depression. Methods: We reviewed all of the relevant RCT (Randomized Control Trial, RCT) studies published until June 2021 from the major open-access databases. Results: 12 RCTs were selected and included in this study, and the total number of people included in the analysis in the combined study was 594. The level of depression and anxiety of participants was evaluated using detailed and recognized scale. Compared with the control group, the yoga intervention group indicates a statistically significant decrease in depression levels (SMD (Standardised Mean Difference, SMD), -2.31; 95% CI, -3.67 to -0.96; P=0.139) and anxiety (SMD, -4.75; 95% CI, -8.3 to -1.19; P=0.002). In addition, we also conducted a subgroup analysis according to the type of population. The subgroup analysis successfully reduced the level of heterogeneity and the results indicated that the difference in population types in the combined analysis leads to the higher heterogeneity. The SMD value for healthy women is -2.3 (95% CI, -4.83 to 0.23) and for depressed women is -9.02 (95% CI, -11.42 to -6.62). Finally, the meta-analysis results of the self-control group prove that yoga can reduce the depression scores (SMD, 5.23; 95% CI, 1.90 to 8.56; P=0.049) compared with baseline. Conclusions: Yoga can effectively relieve symptoms of depression and anxiety in the perinatal period, which can be used as an auxiliary treatment option clinically