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This study investigated the effect of two-dimensional fabric weaves (plain, twill, and satin) on the vibrational characteristics (natural frequency and damping) of woven carbon/epoxy composites. Natural frequencies of woven composites were measured by experimental modal analysis for plain weave, twill weave, and satin weave composites. The experimentally measured natural frequency results were compared with the ones predicted by numerical free vibration analysis. Numerical analysis was performed using a multiscale modelling approach. First, the impregnated fiber tow properties were predicted using periodic boundary conditions assuming that fibers are perfectly bonded. Then, the homogenized impregnated fiber tow properties and matrix properties were used to predict the natural frequencies of woven composite beams. The experimental and numerical results revealed that satin weave composites possess higher natural frequencies than plain weave composites. Flexural loss factor of woven composites was measured experimentally by dynamic mechanical analysis and half-power bandwidth method. Results from both analyses showed that plain weave composites have higher flexural loss factor as compared to satin weave composites.

The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability. As a factory becomes highly automated, physical asset management comes to be a critical part of an operational life-cycle. Maintenance is one area where the collection of technologies may be applied to enhance operational reliability using a machine condition monitoring system. Data-driven models have been extensively applied to machine condition data to build a fault detection system. Most existing studies on fault detection were developed under a fixed set of operating conditions and tested with data obtained from that set of conditions. Therefore, variability in a model’s performance from data obtained from different operating settings is not well reported. There have been limited studies considering changing operational conditions in a data-driven model. For practical applications, a model must identify a targeted fault under variable operational conditions. With this in mind, the goal of this paper is to study invariance of model to changing speed via a deep learning method, which can detect a mechanical imbalance, i.e., targeted fault, under varying speed settings. To study the speed invariance, experimental data obtained from a motor test-bed are processed, and time-series data and time–frequency data are applied to long short-term memory and convolutional neural network, respectively, to evaluate their performance.

The American Community Survey (ACS) provides valuable, timely population estimates but with increased levels of sampling error. Although the margin of error is included with aggregate estimates, it has not been incorporated into segregation indexes. With the increasing levels of diversity in small and large places throughout the United States comes a need to track accurately and study changes in racial and ethnic segregation between censuses. The 2005-2009 ACS is used to calculate three dissimilarity indexes (D) for all core-based statistical areas (CBSAs) in the United States. We introduce a simulation method for computing segregation indexes and examine them with particular regard to the size of the CBSAs. Additionally, a subset of CBSAs is used to explore how ACS indexes differ from those computed using the 2000 and 2010 censuses. Findings suggest that the precision and accuracy of D from the ACS is influenced by a number of factors, including the number of tracts and minority population size. For smaller areas, point estimates systematically overstate actual levels of segregation, and large confidence intervals lead to limited statistical power.

As a result of international immigration, ethnic diversity has increased rapidly in many countries, not only in major cities, but also in smaller cities. This trend is not limited to the traditional immigrant receiving countries, such as the United States and Canada, but occurs also in many other countries where doors are gradually opening to immigration, especially in Asia. This combination of a growing immigrant population and ethnic diversity has fostered a more complex immigrant integration process. This book addresses the subject at the city ecological level, inter-group level, and individual level. It contributes to the understanding of immigrant adaptation in a multi-ethnic context, brings Asian perspectives into the discussion of immigration and race and ethnic relations, and will serve as a basis for future study of immigrant adaptation in a multi-ethnic context.

The rights that the Convention on the Rights of the Child (CRC) enumerates include the rights to (1) an adequate standard of living, (2) an education directed toward the development of the child's fullest potential, (3) the highest attainable standard of health, and (4) the child's own cultural identity and use of his or her own language. The CRC states that these rights shall be ensured regardless of various statuses of children, including race, ethnic origin, national origin, and language. This article presents a statistical baseline for assessing the diversity of children in the United States with regard to these statuses, presents results for statistical indicators of well-being for children distinguished by these statuses, and discusses public policies to reduce inequalities relevant to these rights.

This paper conceives of residential segregation as a multidimensional phenomenon varying along five distinct axes of measurement: evenness, exposure, concentration, centralization, and clustering. Twenty indices of segregation are surveyed and related conceptually to one of the five dimensions. Using data from a large set of U.S. metropolitan areas, the indices are intercorrelated and factor analyzed. Orthogonal and oblique rotations produce pattern matrices consistent with the postulated dimensional structure. Based on the factor analyses and other information, one index was chosen to represent each of the five dimensions, and these selections were confirmed with a principal components analysis. The paper recommends adopting these indices as standard indicators in future studies of segregation.

Residential segregation has traditionally been measured by using the index of dissimilarity and, more recently, the P * exposure index. These indices, however, measure only two of five potential dimensions of segregation and, by themselves, understate the degree of black segregation in U.S. society. Compared with Hispanics, not only are blacks more segregated on any single dimension of residential segregation, they are also likely to be segregated on all five dimensions simultaneously, which never occurs for Hispanics. Moreover, in a significant subset of large urban areas, blacks experience extreme segregation on all dimensions, a pattern we call hypersegregation. This finding is upheld and reinforced by a multivariate analysis. We conclude that blacks occupy a unique and distinctly disadvantaged position in the U.S. urban environment.

In this paper, we use racial data from Census 2000, available for the first time in 50 years, to examine the links among race, socioeconomic status, and residential location on the island of Puerto Rico. Puerto Ricans overwhelmingly chose white as their race, and they chose only one race, not a combination of races that would seem more in keeping with the ideology of mestizaje. Overall, segregation by race is modest compared with residential segregation in the United States. In keeping with the Puerto Rican claim that class is more important than race, we find that segregation by race is generally lower than segregation between the lowest and highest income categories in all metro areas, but that the results for education and occupational status differ by metropolitan area. In San Juan-Bayamón, the most diverse metropolitan area on the island, we find that as percent black increases, neighborhood socioeconomic status decreases, though the changes are not that stark, except in Loiza, a community of black Puerto Ricans and in some Dominican neighborhoods, though there are relatively few of these neighborhoods.

This paper examines trends in residential segregation for blacks, Hispanics, and Asians in 60 SMSAs between 1970 and 1980 using data taken from the 1970 Fourth Count Summary tapes and the 1980 Summary Tape File 4. Segregation was measured using dissimilarity and exposure indices. Black segregation from Anglos declined in some smaller SMSAs in the south and west, but in large urban areas in the northeastern and north central states there was little change; in these areas blacks remained spatially isolated and highly segregated. The level of black-Anglo segregation was not strongly related to socioeconomic status or level of suburbanization. Hispanic segregation was markedly below that of blacks, but increased substantially in some urban areas that experienced Hispanic immigration and population growth over the decade. The level of Hispanic segregation was highly related to indicators of socioeconomic status, acculturation, and suburbanization. Asian segregation was everywhere quite low. During the 1970s the spatial isolation of Asians increased slightly, while dissimilarity from Anglos decreased. Results were interpreted to suggest that Asian enclaves were beginning to form in many U.S. metropolitan areas around 1980.

This article examines trend in suburganization for blacks, Hispanics, and Asians from 1970 to 1980 in 59 U.S. metropolitan areas and consider the effect of suburbanization on segregation at the latter date. Suburbanization is measured as the proportion of each group residing outside the central city but within the SMSA, and segregation is measured with indices of dissimilarity and exposure. Despite recent increases, blacks remain less suburbanized than other minority groups. They are less segregated in suburbs than in central cities, but, even in suburbs, black segregation remains quite high. Hispanics and Asians are considerably more suburbanized than blacks. Their segregation in central cities is generally moderate, and in suburbs it varies from low to moderate. Multivariate models indicate the persistence of barriers to the spatial assimilation of blacks. Given the same objective characteristics and metropolitan context, blacks are much more segregated than Hispanics or Asians.