The accurate prediction of pavement network condition and performance is important for efficient management of the transportation infrastructure system. By reducing the error of the pavement deterioration prediction, agencies can save budgets significantly through timely intervention and accurate planning. The objective of this research study was to develop a methodology for calculating a pavement condition index (PCI) based on historical distress data collected in the databases from Long-Term Pavement Performance (LTPP) program and Minnesota Road Research (Mn/ROAD) project. Excel™ templates were developed and successfully used to import distress data from both databases and directly calculate PCIs for test sections. Pavement performance master curve construction and verification based on the PCIs were also developed as part of this research effort. The analysis and results of LTPP data for several case studies indicated that the study approach is rational and yielded good to excellent statistical measures of accuracy.
It is believed that the InfoPaveTM LTPP and Mn/ROAD database can benefit from the PCI templates developed in this study, by making them available for users to compute PCIs for specific road sections of interest. In addition, the PCI-based performance model development can be also incorporated in future versions of InfoPaveTM. This study explored and analyzed asphalt pavement sections. However, the process can be also extended to Portland cement concrete test sections. State agencies are encouraged to implement similar analysis and modeling approach for their specific road distress data to validate the findings.
ligence. The innovations in ANN has led to ground breaking technological advances
like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and
many more. These were inspired by evolution and working of our brains. Similar
to how our brain evolved using a combination of epigenetics and live stimulus,ANN
require training to learn patterns.The training usually requires a lot of computation
and memory accesses. To realize these systems in real embedded hardware many
Energy/Power/Performance issues needs to be solved. The purpose of this research
is to focus on methods to study data movement requirement for generic Neural Net-
work along with the energy associated with it and suggest some ways to improve the
design.Many methods have suggested ways to optimize using mix of computation and
data movement solutions without affecting task accuracy. But these methods lack a
computation model to calculate the energy and depend on mere back of the envelope calculation. We realized that there is a need for a generic quantitative analysis
for memory access energy which helps in better architectural exploration. We show
that the present architectural tools are either incompatible or too slow and we need
a better analytical method to estimate data movement energy. We also propose a
simplistic yet effective approach that is robust and expandable by users to support
various systems.
To test the above proposition, I conduct the empirical analysis in three steps. In the first step, I investigate foreign banks’ management model by surveying 13 major foreign banks locally incorporated in Mainland China. The results suggest that these 13 foreign banks can be categorized into three distinct groups based on their management model: intergrators, customer-followers, and parent-followers. The results also indicate that intergrators have the highest level of localization while parent-followers have the lowest level of localization.
In the second step, I conduct DEA (Data Envelope Analysis) and CAMEL (Capital Adequacy, Asset Quality, Management, Earnings, Liquidity Analysis) to assess the operating efficiency of these 13 foreign banks. The assessment is conducted in two ways: 1) the inter-group comparison between foreign banks and local Chinese banks; 2) the intra-group comparison between the three distinct groups of foreign banks identified in the first step. The results indicates that the principal factor driving the operating efficiency of both local Chinese banks and foreign banks is the comprehensive technical efficiency, which includes both the quality of management and the quality of technical elements. I also find the uptrend of technical efficiency of the integrators is more stable than that of the other two groups of foreign banks.
Finally, I integrate the results from step one and step two to assess the relevance between foreign banks’ localization level and operating efficiency. I find that foreign banks that score higher in localization tend to have a higher level of operating efficiency. Although this finding is not conclusive about the causal relationship between localization and operating efficiency, it nevertheless suggests that the management model of the higher performing integrators can serve as references for the other foreign banks attempting to enhance their localization and operating efficiency. I also discuss the future trends of development in the banking industry in China and what foreign banks can learn from local Chinese banks to improve their market positions.
Recent developments in computational software and public accessibility of gridded climatological data have enabled researchers to study Urban Heat Island (UHI) effects more systematically and at a higher spatial resolution. Previous studies have analyzed UHI and identified significant contributors at the regional level for cities, within the topology of urban canyons, and for different construction materials.
In UHIs, air is heated by the convective energy transfer from land surface materials and anthropogenic activities. Convection is dependent upon the temperature of the surface, temperature of the air, wind speed, and relative humidity. At the same time, air temperature is also influenced by greenhouse gases (GHG) in the atmosphere. Climatologists project a 1-5°C increase in near-surface air temperature over the next several decades, and 1-4°C specifically for Los Angeles and Maricopa during summertime due to GHG effects. With higher ambient air temperatures, we seek to understand how convection will change in cities and to what ends.
In this paper we develop a spatially explicit methodology for quantifying UHI by estimating the daily convection thermal energy transfer from land to air using publicly-available gridded climatological data, and we estimate how much additional energy will be retained due to lack of convective cooling in scenarios of higher ambient air temperature.
