Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Home Buyer Search Duration and the Internet

In this study we examine the impact of internet use on the duration of search in the housing market. We develop a model of partial equilibrium in the housing market which suggests an ambiguous effect on the search duration when internet resources are employed. In this model, the impact of using the internet can be viewed as increasing the search efficiency, or as altering the distribution of potential matches from which the home buyer can choose. We use data from the 2000 Home Buyer and Seller Survey collected by the National Association of Realtors. While theory suggests there might be an increase or a decrease in search times when using on-line resources in the search, in this data using an Instrumental Quantile Regression approach we find a tendency for internet use to increase the duration of home search relative to employing more conventional search method

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance

Internet use and the duration of buying and selling in the residential housing market, economic incentives and voting

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2002.Includes bibliographical references.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.In this study we examine the impact of internet use on the duration of non-sequential search in the housing market. We develop a model of partial equilibrium in the housing market which suggests an ambiguous effect on the search duration when internet resources are employed in the search. In this model, the impact of using the internet can be viewed as increasing the search efficiency, or as altering the distribution of potential matches from which the home buyer can choose. We use data from the 2000 Home Buyer and Seller Survey collected by the National Association of Realtors. While theory suggests there might be an increase or a decrease in search times when using on-line resources in the search, in this data we find a tendency for internet use to increase the duration of home search relative to employing more conventional search methods. We use a simultaneous equations approach for the analysis of the impact of internet listing on the duration until sale in the residential housing market. In this model, the time on the market and the selling price are jointly determined, once asking price and the method used for the listing of the property is chosen by the home seller or agent. We use data from the 2000 Home Buyer and Seller Survey collected by the National Association of Realtors. We find that using the internet to list a house increases its time on the market. The results presented here are consistent our with previous findings pertaining to the use of the internet and the duration of search until a buyer locates a home to purchase. These results, together with the findings of the present study show evidence for a model of the housing market where all buyers are sellers.(cont.) We investigate the differential propensity of voters in the US to participate in national only versus national and local elections. We use data from the 1987 US General Social Survey to asses the importance of demographic and local community attachment characteristics of voters for this differential voting decision. We find that local community attachment and civic duty play an important role for this voting decision while personal monetary gains and redistributions do not appear to factor into the decision. In particular, education, age of respondent and length lived in community act to lower the costs of voting locally, and influence the voters' decision to participate in local elections as well as in national ones. However, economic incentives such as real estate capital values, local taxes and Social Security allocations do not appear to drive the differential voting decision for participating in local and national elections versus participating in national level elections only.by Victoria Tanusheva D'Urso.Ph.D

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings

On the use of dynamic reliability for an accurate modelling of renewable power plants

Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM