Low-mass Stellar Evolution Traced with Non-LTE Abundances

The detailed chemical composition of stellar atmospheres can reveal the structure and evolution of the stellar interiors, otherwise hidden from direct site, as well as the structure and evolution of our entire Galaxy. The advent of several large-scale stellar spectroscopic surveys promises breakthroughs in our understanding of the physical processes that shape stellar surface abundances. However, the full potential of these extremely large and precise surveys is not yet being reached, as standard elemental abundance determinations today are based on the simplifying and incorrect assumption that the stellar atmosphere is in local thermodynamic equilibrium (LTE). In this thesis I have employed non-LTE radiative transfer methods to tackle two outstanding astrophysical problems. The first problem is related to the chemical homogeneity in the open clusters, which for example is very important to understand how disrupted clusters have formed the Galactic disk and pinpoint the birth location of field stars. Abundance trends with stellar effective temperature have been found in all the analysed elements, indicating that the chemical abundance varies along with evolutionary phase past the turn-off. The overall agreement between our measured abundance patterns and the predictions by the stellar models with atomic diffusion and mixing, implies that the process of atomic diffusion poses a non-negligible effects during the main-sequence phase, which leads to the inhomogeneities in the abundances of open clusters. The second problem is related to lithium evolution in low-mass main-sequence stars. The primordial elemental abundances predicted by Standard Big Bang nucleosynthesis (SBBN) generally show good agreement with observations. However, a glaring exception is the cosmic abundance of lithium, which SBBN estimates to be three times higher than what is observed in the atmospheres of metal-poor stars in the Galactic halo (i.e. stars on the so-called Spite Plateau). This long-recognized discrepancy has become known as the Cosmological Lithium Problem. In this thesis, I present observational evidence, based on a state-of-the-art non-LTE spectroscopic analysis of more than 100,000 stars from the large-scale spectroscopic “Galactic Archaeology with HERMES"(GALAH) survey, that the surface lithium abundances of these Spite Plateau do not in fact reflect their initial (SBBN) lithium abundances; rather, they have been depleted by a factor of three. This further strengthens the case for an astrophysical solution to the cosmological problem, reconciling tension with predictions of the SBBN

Deep Contrastive One-Class Time Series Anomaly Detection

The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based methods, which reveal only a certain aspect of the whole normality, are incapable of tasks involved with a large number of anomalies. Specifically, Contrastive Learning (CL) methods distance negative pairs, many of which consist of both normal samples, thus reducing the AD performance. Existing multi-normality-assumption-based methods are usually two-staged, firstly pre-training through certain tasks whose target may differ from AD, limiting their performance. To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification. It treats the origin and reconstructed representations as the positive pair of negative-samples-free CL, namely "sequence contrast". Next, invariance terms and variance terms compose a contrastive one-class loss function in which the loss of the assumptions is optimized by invariance terms simultaneously and the ``hypersphere collapse'' is prevented by variance terms. In addition, extensive experiments on two real-world time-series datasets show the superior performance of the proposed method achieves state-of-the-art

HB-net: Holistic bursting cell cluster integrated network for occluded multi-objects recognition

Within the realm of image recognition, a specific category of multi-label classification (MLC) challenges arises when objects within the visual field may occlude one another, demanding simultaneous identification of both occluded and occluding objects. Traditional convolutional neural networks (CNNs) can tackle these challenges; however, those models tend to be bulky and can only attain modest levels of accuracy. Leveraging insights from cutting-edge neural science research, specifically the Holistic Bursting (HB) cell, this paper introduces a pioneering integrated network framework named HB-net. Built upon the foundation of HB cell clusters, HB-net is designed to address the intricate task of simultaneously recognizing multiple occluded objects within images. Various Bursting cell cluster structures are introduced, complemented by an evidence accumulation mechanism. Testing is conducted on multiple datasets comprising digits and letters. The results demonstrate that models incorporating the HB framework exhibit a significant $2.98\%$ enhancement in recognition accuracy compared to models without the HB framework ($1.0298$ times, $p=0.0499$). Although in high-noise settings, standard CNNs exhibit slightly greater robustness when compared to HB-net models, the models that combine the HB framework and EA mechanism achieve a comparable level of accuracy and resilience to ResNet50, despite having only three convolutional layers and approximately $1/30$ of the parameters. The findings of this study offer valuable insights for improving computer vision algorithms. The essential code is provided at https://github.com/d-lab438/hb-net.git

Coupled Cooling Method and Application of Latent Heat Thermal Energy Storage Combined with Pre-cooling of Envelope: Sensitivity Analysis and Optimization

This document is the Accepted Manuscript version of the following article: Xiangkui gao, Yanping Yuan, Hongwei Wu, and Xudong Zhao, ‘Coupled Cooling Method and Application of Latent Het Thermal Energy Storage Combined with Pre-cooling of Envelope: Sensitivity Analysis and Optimization’, Process Safety and Environmental Protection, first published online 9 March 2017. Under embargo. Embargo end date: 9 March 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The version of record is available online at doi: http://dx.doi.org/10.1016/j.psep.2017.03.005 © 2017 Elsevier Ltd. All rights reserved.Cooling system for mine refuge chamber provides comfortable environment for miners to avoid heat damage. The existing cooling systems have their own application scopes and limitations. The coupled cooling method of Latent Heat Thermal Energy Storage (LHTES) combined with Pre-cooling of Envelope (PE) is a new free cooling method which is suitable for high-temperature, passive, impact and other harsh environment. Then, to improve the thermal comfort and reduce energy consumption, the effect of the pre-cooling temperature, melting temperature of PCM, aspect ratio and amounts of PCM unit on the indoor temperature are investigated in a systematic manner. Furthermore, the system is optimized and the generalized results for the evaluation parameter are given. Analysis of the results may lead to following main conclusions: (i) the method really controls the indoor temperature and the saving amount of PCM is more than 50% compared to the traditional LHTES systems; (ii) the Temperature Control(TC) performance of PCM would drop significantly if it melts more than 80%; (iii) under current operating conditions, the optimal melting temperature is about 29 °C and the aspect ratio of PCM unit is 60:500; (iv) per 1 °C the pre-cooling temperature dropped, 19% the actual amount of PCM decreased for the case studied.Peer reviewedFinal Accepted Versio

Coupled Cooling Method and Application of Latent Heat Thermal Energy Storage Combined with Pre-cooling of Envelope: Optimization of Pre-cooling with Intermittent Mode

This document is the Accepted Manuscript version of the following article: Xiangkui Gao, Yanping Yuan, Hongwei Wu, Xiaoling Cao, and Xudong Zhao, ‘Coupled cooling method and application of latent heat thermal energy storage combined with pre-cooling of envelope: Optimization of pre-cooling with intermittent mode’, Sustainable Cities and Society, Vol. 38: 370-381, April 2018. Under embargo until 10 January 2019. The final, definitive version of this paper is available online via: https://doi.org/10.1016/j.scs.2018.01.014The coupled cooling method combining latent heat thermal energy storage and pre-cooling of the envelope (PE) is a new free-cooling method that is suitable for exposure to high temperatures and other types of harsh environments. PE plays the most critical role in the coupled cooling method. Long-term, continuous PE cannot only reduce energy storage capacity, but it also causes numerous energy waste. Thus, an intermittent operational mode is firstly proposed to improve the heat transfer performance and reduce energy consumption. A simplified numerical model of intermittent thermal storage is established, and the subsequent effects of intermittent ratio (IR) and intermittent period (IP) on cold storage performance have been systematically investigated. Furthermore, the operational period is divided into a cold storage period (CSP) and a cold preservation period (CPP), each with their own respective evaluation indices. Long-term intermittent PE is optimized, and an interchanging continuous/intermittent cold storage strategy is proposed. Under the current operating conditions, as compared with the conventional continuous mode, the duration of CSP is extended by 0–26%, yielding an annual cold storage energy consumption reduction of 68–78%. Thus, the current study demonstrates the significant potential of intermittent operational mode application in underground thermal energy storage systems.Peer reviewedFinal Accepted Versio