Reconstruction of Black Identity in All Aunt Hagar’s Children

All Aunt Hagar’s Children, the latest work of Edward P. Jones, profoundly reflects the ordinary lives of the African Americans in Washington D.C. during the 20th century. It comprises 14 short stories which are the minified versions of long novels with a legion of characters. This paper attempts to examine the self-identity crises of these characters in the perspective of the identity theory of Erik. H. Erikson and Anthony Giddens. All black characters in this novel collection encounter the predicament about their self-identity. They are discriminated and marginalized by the dominated white society in Washington in which they make great efforts to assimilate into only to find disappointment. They are faced with the racial identity crises when abandoning their traditional black values and refusing to track their own history. Furthermore, Black women are in the lower position with much more oppression, deprived of the rights from the patriarchy. Through detailed interpretation, this thesis reveals three essential resolutions to save these lost black from identity crisis.

Three-Dimensional Time Resolved Lagrangian Flow Field Reconstruction Based on Constrained Least Squares and Stable Radial Basis Function

The three-dimensional Time-Resolved Lagrangian Particle Tracking (3D TR-LPT) technique has recently advanced flow diagnostics by providing high spatiotemporal resolution measurements under the Lagrangian framework. To fully exploit its potential, accurate and robust data processing algorithms are needed. These algorithms are responsible for reconstructing particle trajectories, velocities, and differential quantities (e.g., pressure gradients, strain- and rotation-rate tensors, and coherent structures) from raw LPT data. In this paper, we propose a three-dimensional (3D) divergence-free Lagrangian reconstruction method, where three foundation algorithms -- Constrained Least Squares (CLS), stable Radial Basis Function (RBF-QR), and Partition-of-Unity Method (PUM) -- are integrated into one comprehensive reconstruction strategy. Our method, named CLS-RBF PUM, is able to (i) directly reconstruct flow fields at scattered data points, avoiding Lagrangian-to-Eulerian data conversions; (ii) assimilate the flow diagnostics in Lagrangian and Eulerian descriptions to achieve high-accuracy flow reconstruction; (iii) process large-scale LPT data sets with more than hundreds of thousand particles in two dimensions (2D) or 3D; (iv) enable spatiotemporal super-resolution while imposing physical constraints (e.g., divergence-free for incompressible flows) at arbitrary time and location. Validation based on synthetic and experimental LPT data confirmed that our method can consistently achieve the above advantages with accuracy and robustness.Comment: 30 pages, 11 figure

Optimal design of standalone hybrid renewable energy systems with biochar production in remote rural areas: A case study

For remote agriculture-based rural areas, utilizing the local renewable resources such as biomass, wind, and solar energy could be potentially more efficient than long-distance transmission of electricity. In this paper, a multi-objective optimization model for the design of standalone hybrid renewable energy systems (HRES) in remote rural areas is proposed. The objective is to maximize the profits and the carbon abatement capability of the system by optimal process selection and sizing of HRES components including solar, wind, and biomass generation systems. A case study for the design of an HRES on the Carabao Island in the Philippines is conducted. The result shows a 122 kW solar power plant, a 67 kW onshore wind farm and a 223 kW biomass pyrolysis system constitute the optimal configuration of the hybrid energy system, generating a daily profit of US$ 940. The greenhouse gas emission of the optimal system is -3,339 kg CO2 eq/day, indicating good carbon sequestration performance

Lagrangian Flow Field Reconstruction Based on Constrained Stable Radial Basis Function

Recent advances in three-dimensional (3D) high seeding density time-resolved Lagrangian particle tracking (LPT) techniques have made diagnosing fluid flows at high resolution in space and time under a Lagrangian framework feasible and practical. But challenges persist in developing LPT data processing methods. A promising processing method should accurately and robustly reconstruct, for example, particle trajectories, velocities, and differential quantities from noisy experimental data. Despite numerous algorithms available in the LPT community, they may suffer from some issues, such as unfavorable accuracy and robustness, lack of physical constraints, and unnecessary projection from Lagrangian data onto Eulerian meshes. These challenges may limit the application of the 3D high seeding density time-resolved LPT techniques. In this thesis, a novel 3D Lagrangian flow field reconstruction method is proposed to address these challenges. The proposed method is based on a stable radial basis function (RBF) and constrained least squares (CLS). The stable RBF serves as a model function to approximate trajectories and velocity fields. The CLS provides a framework to facilitate regression and enforce physical constraints, further enhancing the reconstruction performance. The stable RBF and CLS work together to reconstruct 3D Lagrangian flow fields with high accuracy and robustness. The proposed method offers several advantages over the algorithms currently used in the LPT community. First, it accurately reconstructs particle trajectories, velocities, and differential quantities in 3D, even from noisy experimental data, while satisfying physical constraints such as divergence-free for incompressible flows. Second, it does not require projecting Lagrangian data onto Eulerian meshes, allowing for direct flow field reconstruction at scattered data locations. Third, it effectively mitigates experimental noise in particle locations. Last, the proposed method enables smooth spatial and temporal super-resolution with ease. These advantages exhibit that the proposed method is promising for LPT data processing and further applications in data assimilation and machine learning. Systematic tests were conducted to validate and verify the proposed method. Two-dimensional and 3D validations were performed using synthetic data based on exact solutions of the Taylor-Green vortex and Arnold-Beltrami-Childress flow with added artificial noise. The validations show that the proposed method outperforms baseline algorithms (e.g., finite difference methods and polynomial fittings) under different flow conditions. The method was then verified using experimental data from a 3D low-speed pulsing jet, showing its reliable performance. Based on these validations and verification, it is demonstrated that the proposed method can process experimental LPT data and reconstruct Lagrangian flow fields with accuracy and robustness

Optimal design of negative emission hybrid renewable energy systems with biochar production

To tackle the increasing global energy demand the climate change problem, the integration of renewable energy and negative emission technologies is a promising solution. In this work, a novel concept called “negative emission hybrid renewable energy system” is proposed for the first time. It is a hybrid solar-wind-biomass renewable energy system with biochar production, which could potentially provide energy generation, carbon sequestration, and waste treatment services within one system. The optimization and the conflicting economic and environmental trade-off of such system has not yet been fully investigated in the literature. To fill the research gap, this paper aims to propose a stochastic multi-objective decision-support framework to identify optimal design of the energy mix and discuss the economic and environmental feasibilities of a negative emission hybrid renewable energy system. This approach maximizes energy output and minimizes greenhouse gas emissions by the optimal sizing of the solar, wind, combustion, gasification, pyrolysis, and energy storage components in the system. A case study on Carabao Island in the Philippines, which is representative of an island-mode energy system, is conducted based on the aim of achieving net-zero emission for the whole island. For the island with a population of 10,881 people and an area of 22.05 km2, the proposed optimal system have significant negative emission capability and promising profitability with a carbon sequestration potential of 2795 kg CO2-eq/day and a predicted daily profit of 455 US$/day

Sequence Stratigraphy of Fine-Grained “Shale” Deposits: Case Studies of Representative Shales in the USA and China

The fine-grained “shale” deposits host a vast amount of unconventional oil and gas resources. This chapter examines the variations in lithofacies, patterns of well logs, geochemistry, and mineralogy in order to construct a sequence stratigraphic framework of the representative marine Barnett, Woodford, Marcellus, Mowry, and Niobrara fine-grained “shales” (USA) and the marine Longmaxi shale and lacustrine Chang7 lacustrine shale (China). Practical methods are proposed in order to recognize the sequence boundaries, the flooding surfaces, the parasequences and parasequence sets, the system tracts, and variation patterns of facies and rock properties. The case studies for the sequence stratigraphy in the USA and China have revealed that the transgressive systems tract (TST) and the early highstand systems tract (EHST, if identifiable) of fine-grained “shales” have been deposited in anoxic settings. TST and EHST of the siliciclastic “shales” are characterized by high gamma ray, high TOC, and high quartz content, while TST and EHST of the carbonate-dominated fine-grained “shales” are characterized by low gamma ray, organic lean, and carbonate rich fine-grained deposits. The lithofacies, geochemistry, mineralogy, depositional evolution, and reservoir development have been predicted and correlated within a sequence stratigraphic framework for the suggested cases. The best reservoir with the best completion quality is developed in TST and HST in both siliciclastic-dominated and carbonate-dominated fine-grained “shales.

Integrative analysis and identification of key elements and pathways regulated by Traditional Chinese Medicine (Yiqi Sanjie formula) in colorectal cancer

Introduction: The clinical efficacy of Yiqi Sanjie (YQSJ) formula in the treatment of stage III colorectal cancer (CRC) has been demonstrated. However, the underlying antitumor mechanisms remain poorly understood.Materials and methods: The aim of the present study was to comprehensively characterize the molecular and microbiota changes in colon tissues and fecal samples from CRC mice and in CRC cell lines treated with YQSJ or its main active component, peiminine. Integrative tandem mass tag-based proteomics and ultra-performance liquid chromatography coupled with time-of-flight tandem mass spectrometry metabolomics were used to analyze azoxymethane/dextran sulfate sodium-induced CRC mouse colon tissues.Results: The results showed that 0.8% (57/7568) of all detected tissue proteins and 3.2% (37/1141) of all detected tissue metabolites were significantly changed by YQSJ treatment, with enrichment in ten and six pathways associated with colon proteins and metabolites, respectively. The enriched pathways were related to inflammation, sphingolipid metabolism, and cholesterol metabolism. Metabolomics analysis of fecal samples from YQSJ-treated mice identified 121 altered fecal metabolites and seven enriched pathways including protein digestion and absorption pathway. 16S rRNA sequencing analysis of fecal samples indicated that YQSJ restored the CRC mouse microbiota structure by increasing the levels of beneficial bacteria such as Ruminococcus_1 and Prevotellaceae_UCG_001. In HCT-116 cells treated with peiminine, data-independent acquisition-based proteomics analysis showed that 1073 of the 7152 identified proteins were significantly altered and involved in 33 pathways including DNA damage repair, ferroptosis, and TGF-β signaling.Conclusion: The present study identified key regulatory elements (proteins/metabolites/bacteria) and pathways involved in the antitumor mechanisms of YQSJ, suggesting new potential therapeutic targets in CRC

Impact of meteorological factors on the COVID-19 transmission: A multicity study in China

The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID- 19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below0 °C, 12 cities had average AHbelow4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition,we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission