MODELLING SUBGRADE FLUIDISATION UNDER RAIL TRACKS BASED ON LBM-DEM COUPLING

Railways play an essential role in transportation and economy in Australia; however, due to the increasing demand for rail transport in recent years, railway infrastructure inevitably faces extensive degradation. One of the severe issues causing the degradation of rail tracks is mud pumping, where the fines intrude into the ballast layer to form slurry state under wet condition. Mud pumping is a complex process involving different mechanisms, including subgrade fluidisation, internal erosion, filtration and upward migrations of fines. This thesis focuses on the fluidisation of subgrade soil under increasing excess pore water pressure, which results in fines penetrating overlying ballast. Traditional methods such as experimental and analytical approaches can capture the macro-behaviours of soil such as soil settlement and hydraulic conductivity under increasing excess pore pressure; however, they have many limitations when microscopic and localised behaviour must be addressed. Therefore, this study proposed a numerical method that couples the Lattice Boltzmann Method (LBM) with the Discrete Element Method (DEM) to capture soil behaviours under increasing hydraulic gradient at both macro and micro scales. While particle behaviour is modelled using the DEM, the fluid properties can be depicted in greater detail based on the LBM. The numerical results are validated with experiments on a selected subgrade soil. The results show that the numerical method can reasonably predict the hydraulic and soil fluidisation aspects concerning the experimental data. Microscopic properties such as the localised fluid velocity through the porous spaces of the soil are also captured well by the proposed fluid-particle coupling approach. Also, the gas fluidisation is carried out in this study using LBM-DEM coupling to further validate the numerical method. The results are compared with the conventional CFD (Computational Fluid Dynamics) - DEM coupling and show a good agreement between LBM-DEM and CFD-DEM coupling

Difficulties of Vietnamese Students in Learning Academic Writing

Writing is consistently regarded as one of the most challenging language-learning abilities. This paper investigates the challenges Vietnamese students face in learning academic writing in English. Ninety-five students from grades 6 to 12 who have been studying English for several years were surveyed using a questionnaire. The questionnaire comprised 14 Likert-scale statements and one multiple-choice question to determine their interests, habits, and perspectives toward learning English. Most participants expressed a keen interest in learning languages but felt that their writing skills in English were not up to par, attributing this to inadequate time spent practicing. As a result, it is suggested that secondary school students in Vietnam increase their writing practice time in English and alter their learning routines and habits accordingly

THE INFLUENCE OF TRP53 IN THE DOSE RESPONSE OF RADIATIONINDUCED APOPTOSIS, DNA REPAIR AND GENOMIC STABILITY IN MURINE HAEMATOPOIETIC CELLS

Apoptotic and DNA damage endpoints are frequently used as surrogate markers of cancer risk, and have been well-studied in the Trp53+/- mouse model. We report the effect of differing Trp53 gene status on the dose response of ionizing radiation exposures (0.01- 2 Gy), with the unique perspective of determining if effects of gene status remain at extended time points. Here we report no difference in the dose response for radiationinduced DNA double-strand breaks in bone marrow and genomic instability (MN-RET levels) in peripheral blood, between wild-type (Trp53+/+) and heterozygous (Trp53+/-) mice. The dose response for Trp53+/+ mice showed higher initial levels of radiation-induced lymphocyte apoptosis relative to Trp53+/- between 0 and 1 Gy. Although this trend was observed up to 12 hours post-irradiation, both genotypes ultimately reached the same level of apoptosis at 14 hours, suggesting the importance of late-onset p53-independent apoptotic responses in this mouse model. Expected radiation-induced G1 cell cycle delay was observed in Trp53+/+ but not Trp53+/-. Although p53 has an important role in cancer risk, we have shown its influence on radiation dose response can be temporally variable. This research highlights the importance of caution when using haematopoietic endpoints as surrogates to extrapolate radiation-induced cancer risk estimation

Factors affecting the adoption of climate-smart aquaculture (CSAq) in the North Central Coast of Vietnam

Climate-smart aquaculture (CSAq) is considered an appropriate and effective adaptation approach for the coastal aquaculture sector under the climate change phenomenon. This study, applying probit model, aims to assess the influence of several factors on the farmers’ decision to apply CSAq practices in extensive coastal shrimp farming. Data were collected from interviews with 200 households who have both already applied and have yet to apply CSAq practices in five coastal districts of Thanh Hoa Province. The results showed six key factors that influenced the decision of the farmers to apply CSAq practices: availability of household labor; access to information on CSAq practices; market price of products applying CSAq practices; economic efficiency; ability to ensure food security; and improved pond environment when applying CSAq practices. These factors explained 69.41% of their decision to apply CSAq, among which economic efficiency had the greatest impact (30.2%). Market prices and access to information about CSAq are also important factors with respective levels of influence at 16.0% and 14.9%. The result implies that strengthening access to CSAq information and improving technical understanding of CSAq practices are important solutions to upscale CSAq in the North Central Coast of Vietnam

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising results for both academic and practical applications. In these works, the use of Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can aid malware creators in crafting metamorphic malware that evades antivirus software. In this study, we propose a mutation system to counteract ensemble learning-based detectors by combining GANs and an RL model, overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three key challenges in performing adversarial attacks on Windows Portable Executable malware, including format preservation, executability preservation, and maliciousness preservation. In the FeaGAN model, ensemble learning is utilized to enhance the malware detector's evasion ability, with the generated adversarial patterns. The experimental results demonstrate that 100\% of the selected mutant samples preserve the format of executable files, while certain successes in both executability preservation and maliciousness preservation are achieved, reaching a stable success rate

Chemical diversity of essential oils of rhizomes of six species of Zingiberaceae family

In this study, the essential oils from the rhizomes of six species belonging to the Zingiberaceae family, including Zingiber zerumbet, Curcuma pierreana, Globba macrocarpa, Alpinia conchigera, Stahlianthus campanulatus and Amomum sp., collected in Binh Chau-Phuoc Buu Nature Reserve were isolated using hydrodistillation, and their constituents were identified via Gas Chromatography-Mass Spectrometry. A total of 91 constituents have been identified from essential oils. These compounds were classified into 4 clusters by Agglomerative Hierarchical Clustering (AHC) and Principal Component Analysis (PCA) analysis. The principal constituents of the essential oils isolated from four species, C. pierreana, S. campanulatus, A. conchigera, and Z. zerumbet contained camphene (18.82%), α-copaene (11.75%), p-xylene (21.86%), and α-santalene (17.91%), which were significantly different from those in previous reports. Furthermore, this study revealed the chemical constituents of essential oils of G. macrocarpa and Amomum sp. for the first time. Accordingly, artemisia triene (22.21%), β-pinene (13.57%), 4,6,8-trimethylazulene (11.1%), 2-tert-butylquinoline (9.86%), β-patchoulene (7.06%), α-elemene (6.93%), and β-ocimene (6.0%) were the major compounds in essential oils of G. macrocarpa rhizomes whereas the oil of Amomum sp. was found to be rich in 2-carene (21.82%), fenchyl acetate (14.26%), 3-carene (8.28%), bornyl acetate (7.7%), and D-limonene (7.13%)

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

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