1,906 research outputs found
CCDC 2061103: Experimental Crystal Structure Determination
No full textRelated Article: Chaoyi Yao, Hongyu Lin, Brian Daly, Yikai Xu, Warispreet Singh, H. Q. Nimal Gunaratne, Wesley R. Browne, Steven E. J. Bell, Peter Nockemann, Meilan Huang, Paul Kavanagh, A. Prasanna de Silva|2022|J.Am.Chem.Soc.|144|4977|doi:10.1021/jacs.1c1302
Education and earnings inequality in Mexico
No full textEducation attainment levels increased dramatically for Mexico's labor force in the 1980s and early 1990s. In parallel, the country experienced a pronounced increase in earnings inequality from 1984-94, reflected in a higher dispersion of wages and an absolute decline in the real incomes of less educated, poorer Mexicans. This increased wage dispersion presents policymakers with a tradeoff between efficiency considerations (favoring increased spending on higher education) and equity considerations (favoring a more equal distribution of per student spending) in the allocation of fiscal resources to education. The author concludes that the best way to deal with this equity-efficiency tradeoff is to encourage greater private participation in higher education. His main findings are that: a) The accumulation of human capital during 1984-94, as proxied by education attainment, was accompanied by a more equal distribution of education attainment levels over that period and, thus, exerted an equalizing effect on the distribution of incomes. The increased income inequalityobserved over that period appears to be caused by an increased rate of skill-based technological change, whose transmission to Mexico and other developing countries may have been facilitated by the increased openness of their economies. b) The greater dispersion of wager observed in Mexico during the past decade raised the rates of return on investing in higher education, reversing the traditional pattern where primary education exhibits the highest rates of return. c) The social rates of return across levels of schooling were more uniform in 1994 than in 1984, suggesting a more efficient assignment of education spending. At the same time, the distribution of spending on education became more egalitarian, as per student spending in higher education declined markedly compared with per student spending at the primary level. This surprising coincidence in the pattern of spending on education was only possible because Mexico started out with a very distorted resource allocation in education that was both highly inequitable and inefficient. As Mexico's policymakers are on the way to correcting these distortions, the opportunities for avoiding the equity-efficiency tradeoff within Mexico's centralized education framework will become progressively exhausted. d) There is little reason to expect the pace of technological change, which appears mainly responsible for raising wage dispersion and the relative returns on higher education, to abate. Efficiency considerations dictate that Mexico should respond by devoting more resources to higher education. However, the federal budget, which traditionally has financed the lion's share of higher education costs in Mexico, is unable to accommodate additional spending on higher education, while spending cuts elsewhere in the education sector are bound to raise serious equity questions. Thus, to avoid falling behind in terms of human capital accumulation, greater private sector participation is necessary, at least, in terms of cost recovery from the main beneficiaries of higher education.Decentralization,Teaching and Learning,Environmental Economics&Policies,Public Health Promotion,Curriculum&Instruction,Teaching and Learning,Environmental Economics&Policies,Health Monitoring&Evaluation,Gender and Education,Curriculum&Instruction
Women and literature in the early Qing fiction Lin Lan Xiang = "Lin Lan Xiang" zhong de nü xing yu wen xue
No full textThe early Qing fiction Lin Lan Xiang tells a story about the destinies of a number of women who live in a noble family. The fiction aims to portray several different kinds of women, among whom Yan Mengqing is the heroine. She is virtuous, artistic and beautiful, while her husband Geng Lang is very inferior to her. Lin Lan Xiang explores the women’s life, fate, talents and abilities. It also shows how the women meet and develop a love for each other through literature. It is the love between the women that brings them into the same family, and not their affection for their husband.
Even though the author appreciates the outstanding literary talent of the women, the fiction implies that their literary talent is defined as a good thing only under certain circumstances. If a woman behaves inappropriately, her talent might bring her to harm. If a stranger sees a woman’s poems or paintings, the social gender rules may be broke, and she may be molested. If her talent overrides her morality, she would not be considered as a competent wife and mother any more, and could become estranged from her husband. The description of Yan Mengqing’s illness and her death at an early age reflects the historical obsession with “sickly beauties” and “talented women dying young”, an obsession that derived from the negative attitude towards women’s talent in traditional society.
The relationship between women and literature is complicated. The author not only describes upper-class women writing and painting, but considers lower-class women, such as maids and elderly female servants. The impact of literature is different when it comes to these lower-class women, which is revealed in the stories of Hongyu and Lipo. These two are based on some real traditional Chinese opera women writers and Tanci women writers/performers.published_or_final_versionChineseMasterMaster of Philosoph
Intelligent diagnosis of rotating machinery faults - A review
No full textThe task of condition monitoring and fault diagnosis of rotating machinery faults is both significant and important but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert knowledge. Various diagnostics methods have been proposed for different types of rotating machinery. However, little research has been conducted on synthesizing and analysing these techniques, resulting in apprehension when technicians need to choose a technique suitable for application. This paper presents a review of a variety of diagnosis techniques that have had demonstrated success when applied to rotating machinery and highlights fault detection and identification techniques based mainly on artificial intelligence approaches. The literature is categorised in the following diagnostic groups: neural networks, fuzzy sets, expert systems, and hybrid AI techniques based fault diagnosis. The paper concludes with a brief description of a new approach to diagnosis using a Wavelet based Coactive Artificial Neuro-Fuzzy Inference System (CANFIS) which the authors plan to develop and implement for diagnosing machine faults
Fault diagnosis of rolling element bearings using basis pursuit
No full textThe task of condition monitoring and fault diagnosis of rolling element bearing is often cumbersome and labour intensive. Various techniques have been proposed for rolling bearing fault detection and diagnosis. The challenge however, is to efficiently and accurately extract features from signals acquired from these elements, particularly in the time–frequency domain. A new time–frequency technique, known as basis pursuit, was recently developed. This paper presents an application of this new basis pursuit method in the extraction of features from signals collected from faulty rolling bearings with inner race and outer race faults. Results obtained using this new technique were compared with discrete wavelet packet analysis (DWPA) and the matching pursuit technique. Basis pursuit represents features with very fine resolution and sparsity in the time–frequency domain thus rendering easier interpretation of the analysed results. The technique also improves the signal to noise ratio so that subsequent fault detection and identification can be conducted with confidence
Basis pursuit-based intelligent diagnosis of bearing faults
No full textPurpose – The purpose of this article is to present a new application of pursuit-based analysis for diagnosing rolling element bearing faults. \ud
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Design/methodology/approach – Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis. \ud
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Findings – Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature-based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature-based fault diagnosis is significantly more accurate than matching pursuit feature-based fault diagnosis in detecting these faults. \ud
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Practical implications – Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery. \ud
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Originality/value – This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant
Representation learning of natural language and its application to language understanding and generation
No full textHow to properly represent language is a crucial and fundamental problem in Natural Language Processing (NLP). Language representation learning aims to encode rich information such as the syntax and semantics of the language into dense vectors. It facilitates the modeling, manipulation and analysis of natural language in computational linguistics. Existing algorithms utilize corpus statistics such as word co-occurrences to learn general-purpose language representation. Recent advances in generic representation integrate intensive information such as contextualized features from unlabeled text corpora.
In this dissertation, we continue this line of research to incorporate rich knowledge into generic embeddings. We show that word representation could be enriched with various information including temporal and spatial variations as well as syntactic functionalities, and that text representation could be refined with topical knowledge. Moreover, we develop an insight into the geometry of pre-trained representation, and connect it to the semantic understanding such as identifying the idiomatic word usage.
Besides generic representation, task-dependent representation is also extensively studied in downstream applications, where the representation is trained to encode domain information from labeled datasets. This dissertation leverages the capability of neural network models to integrate the task-specific supervision into language representations. We introduce new deep learning models and algorithms to train representations with external knowledge in annotated data. It is shown that the learned representation can assist in various downstream tasks in language understanding such as text classification and language generation such as text style transfer.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01The student, Hongyu Gong, accepted the attached license on 2020-04-14 at 14:10.The student, Hongyu Gong, submitted this Dissertation for approval on 2020-04-14 at 14:33.This Dissertation was approved for publication on 2020-04-15 at 11:07.DSpace SAF Submission Ingestion Package generated from Vireo submission #14978 on 2020-08-25 at 17:27:37Made available in DSpace on 2020-08-26T23:54:33Z (GMT). No. of bitstreams: 3
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