Research on power transformer vibration and noise under DC bias condition

In the process of Ultra high voltage direct current (UHVDC) transmission, the direct current (DC) bias of power transformer is easily induced, which makes the transformer exciting current distorted, the ferromagnetic material saturated and the magnetic leakage increased, and then leads to the increase of core vibration and noise. Aiming at this problem, taking a 240 MVA, 330 kV three-phase five-column power transformer as an example, the coupling of the electromagnetic field, structural force field and acoustic field is studied, and the influence of DC bias on vibration and noise of power transformer core is analyzed in this paper. According to the magnetic density and electric density of transformer core under different magnetic bias degree, the structural force field is solved, and the displacement and surface acceleration of core are obtained, which can be as the excitation of sound field to determine the noise distribution of transformer. In order to avoid the natural frequencies which easily cause resonance, the modal analysis is needed to obtain the natural frequencies and modal modes of the core. The transformer noise under no-load and DC bias conditions of the prototype is tested experimentally and compared with the theoretical calculation, the results prove the accuracy of the simulation calculation method in this paper.

Death by Robots? Automation and Working-Age Mortality in the United States

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. population health trends, including rising mortality due to ‘deaths of despair’. Increasing automation—the use of industrial robots to perform tasks previously done by human workers—is one major structural force driving the decline of manufacturing jobs and wages. In this study we examine the impact of automation on age-sex specific mortality. Using exogenous variation in automation to support causal inference, we find that increases in automation over the period 1993–2007 led to substantive increases in all-cause mortality for both men and women aged 45-54. Disaggregating by cause, we find evidence automation is associated with increases in drug overdose deaths, suicide, homicide and cardiovascular mortality although patterns differ across age-sex groups. We go on to examine heterogeneity in effects by safety net program generosity, labor market policies, and the supply of prescription opioids.

邁向湧現美學的基本設計教學研究

<p>本研究主要在探討建築設計在數位工具與生物學觀點介入後，如何將形態生成語彙的，以自然的湧現特質呈現在設計中。設計的操作方法透過材料特性的探索、量體聚集和力學傳遞的差異與連續，企圖創造出不同於以往只有幾何的組織特性而能傳達自組織美學的意。此看法包含Las Spuybroek所說解釋的新激進唯物主義概念，可以體現從構築、跨越物質感知與物質本身產生共鳴的設計方法。目前在數位設計型態上十分常見。但是，如何避免只是複製形式而不了解邏輯的生成原則，將材料探索和其生成意義傳遞給學生，需要一套較完整的設計教學方法。本研究歷經三年的嘗試，已接近明確的方法論，其目的就是希望讓學生從近身事物之觀察為起點，分門別類理解各種材料與對應工具的技術，最後運用於生成形式獨特的空間內涵。即便學生沒有直接接觸或使用計算機工具或任何種類的演算和結構運算軟體。學生也能自然地體驗形式由下而上的生成方法，以及來自構築性物件中結構力量的流動與傳遞經驗。他們可以看到組織的流動性如何轉移到物體結構中。</p> <p>&nbsp;</p><p>The research primarily explores how, after the introduction of digital tools and biological viewpoints, architectural design may express the morphological generation language within as a natural emergence. Through the exploration of material properties, mass aggregation and the force distribution and continuity, the design method attempts to create an assemblage characteristic containing more than simply geometry as in the past, and the ability to communicate the aesthetic value of self-assemblage. This aspect was also explained by Las Spuybroek, on new radical materialist concept that embodies a design approach resonating with the tectonics, cross-material perception and the material itself. At the moment, this approach is prevalent in digital design. Nonetheless, for the purpose of conveying this tectonic significance to students by the material exploration, and avoiding a simple replication of form and uninformed of the principles that generate such logic, a more thorough method in design teaching is required. After three years of experimentation, the research has nearly arrived at a clear methodology, which aims to allow students to take the observation of the surrounding objects as the outset, categorizing and understanding the materials and set of techniques of respective tools, and finally realized by generating unique forms of spatial connotation. Even if the students have yet been in direct contact or use of the computational tools or any types of algorithms and structural calculations, they can naturally experience the bottom-up approach of the form generation and the flow of structural force from the tectonic, thus pass on the experience. They will observe how the organizational mobility is transferred to the structure.</p> <p>&nbsp;</p>

Social Exclusion and Resilience: Examining Social Network Stratification among People in Same-Sex and Different-Sex Relationships

Social networks of minoritized societal groups may be exposed to a unique structural force, namely that of social exclusion. Using a national sample of people in same-sex and different-sex relationships in the Netherlands (N = 1,329), this study examines sexual orientation as stratifying factor in social networks. Specifically, it is a comparison of their size and composition. Overall, the networks are similar but a few differences stand out. People in same-sex relationships have larger networks than people in different-sex relationships, which are made up of fewer ties with the family-of-origin and more friends. This lends support to the families-of-choice hypothesis and suggests that people employ resilience strategies, such as alternative community building, to counteract social exclusion from families-of-origin. The results further show that men in same-sex relationships have the fewest same-gender ties in their networks out of both men and women in any relationship type. Overall, the results show that sexual orientation is a dimension worthwhile studying as a stratifying factor of social networks both standing alone and at the intersection with gender.

Novel field-circuit assisted FEA of 110 kV power transformer for noise control and vibration reduction

The vibration and noise are serious problems for large oil-immersed power transformers, which directly affect the performance and stability of transformers. The no-load current, as the excitation source, is very important for accurate calculation of vibration and noise. This paper provides a novel approach based on the new field-circuit coupling model to calculate no-load current of large power transformers. For one 110 kV large oil-immersed power transformer, the multi-physics coupling problem including magnetic field, structural force field and acoustic field under alternating magnetic field is analyzed. Following the multi-physics coupling calculation, distributions of vibration and noise are obtained. To validate feasibility and applicability of the proposed method, the actual vibration and noise of transformer are measured experimentally. Finally, the simulation results are compared with experimental ones, which show better goodness of fit.

Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data

Background Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society—through in-person and online social interactions—referencing the novel coronavirus as the “Chinese virus” or “China virus” has the potential to create and perpetuate stigma. Objective The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases “Chinese virus” and “China virus” on Twitter after the March 16, 2020, US presidential reference of this term. Methods Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of “Chinese virus.” We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. Results A total of 16,535 “Chinese virus” or “China virus” tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing “Chinese virus” or “China virus” were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod “Chinese virus” tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod “Chinese virus” tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%). Conclusions The rise in tweets referencing “Chinese virus” or “China virus,” along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter.

Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data (Preprint)

BACKGROUND Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society—through in-person and online social interactions—referencing the novel coronavirus as the “Chinese virus” or “China virus” has the potential to create and perpetuate stigma. OBJECTIVE The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases “Chinese virus” and “China virus” on Twitter after the March 16, 2020, US presidential reference of this term. METHODS Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of “Chinese virus.” We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. RESULTS A total of 16,535 “Chinese virus” or “China virus” tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing “Chinese virus” or “China virus” were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod “Chinese virus” tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod “Chinese virus” tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%). CONCLUSIONS The rise in tweets referencing “Chinese virus” or “China virus,” along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter.

A structure–agency integrative framework for information access disparity

PurposeThis study aims to build an integrative framework for explaining society's information access disparity, which takes both structure and agency as well as their interactions into consideration.Design/methodology/approachIt adopts a qualitative survey design. It collects data on the development of 65 individuals' information access through interviews, and analyzes the data following grounded theory principles.FindingsA theoretical framework is established based on seven constructs and their relationships, all emerging from the empirical data. It rediscovers practice as the primary structural force shaping individuals' information access, hence society's information access disparity; it shows, meanwhile, that the effect of practice is mediated and/or interrupted by four agentic factors: affective responses to a practice, strategic move between practices, experiential returns of information, and quadrant state of mind.Research limitations/implicationsIt urges LIS researchers to go beyond the embedded information activities to examine both the embedded and embedding, beyond actions to examine both actions and experiences.Practical implicationsIt calls for information professionals to take a critical stance toward the practices they serve and partake in their reforms from an LIS perspective.Originality/valueThe framework provides an integrative and novel explanation for information access disparity; it adds a number of LIS-relevant concepts to the general practice theories, highlighting the significance of embedded information activities in any practice and their reverberations; it also appears able to connect a range of human-related LIS theories and pinpoint their gaps.