Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

2018 ◽  
Vol 233 (4) ◽  
pp. 593-604
Author(s):  
Amin Moniri-Morad ◽  
Mohammad Pourgol-Mohammad ◽  
Hamid Aghababaei ◽  
Javad Sattarvand
Keyword(s):  
Complex Systems ◽  
Cox Regression ◽  
Reliability Evaluation ◽  
Distribution Functions ◽  
Heterogeneous Environment ◽  
Mining Equipment ◽  
Data Set ◽  
Explanatory Variables ◽  
The Impact

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

scholarly journals Evacuation Data from a Hospital Outpatient Drill The Case Study of North Shore Hospital

2020 ◽  
Vol 5 ◽  
Author(s):  
Anass Rahouti ◽  
Ruggiero Lovreglio ◽  
Phil Jackson ◽  
Sélim Datoussaïd
Keyword(s):  
Built Environment ◽  
Fire Safety ◽  
Movement Time ◽  
Data Set ◽  
Healthcare Facilities ◽  
Staff Members ◽  
North Shore ◽  
The North ◽  
The Impact

Assessing the fire safety of buildings is fundamental to reduce the impact of this threat on their occupants. Such an assessment can be done by combining existing models and existing knowledge on how occupants behave during fires. Although many studies have been carried out for several types of built environment, only few of those investigate healthcare facilities and hospitals. In this study, we present a new behavioural data-set for hospital evacuations. The data was collected from the North Shore Hospital in Auckland (NZ) during an unannounced drill carried out in May 2017. This drill was recorded using CCTV and those videos are analysed to generate new evacuation model inputs for hospital scenarios. We collected pre-movement times, exit choices and total evacuation times for each evacuee. Moreover, we estimated pre-movement time distributions for both staff members and patients. Finally, we qualitatively investigated the evacuee actions of patients and staff members to study their interaction during the drill. The results show that participants were often independent from staff actions with a majority able to make their own decision.

NF-κB in the Vascular Progression of Melanoma

2004 ◽  
Vol 22 (4) ◽  
pp. 617-623 ◽  
Author(s):  
Mohammed Kashani-Sabet ◽  
Ladan Shaikh ◽  
James R. Miller ◽  
Mehdi Nosrati ◽  
Carlos M.M. Ferreira ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Surrogate Marker ◽  
Tissue Array ◽  
Matched Pair ◽  
Vascular Involvement ◽  
Tumor Vascularity ◽  
Data Set ◽  
Cox Regression Analysis ◽  
The Impact

Purpose To examine a model of melanoma progression based on vascular factors and the role of NF-κB in the vascular progression of melanoma. Patients and Methods A data set of 526 patients from the University of California San Francisco Melanoma Center with 2 years of follow-up or first relapse was studied. The impact of the presence or absence of various prognostic factors on overall survival of melanoma patients was assessed using Cox regression and Kaplan-Meier analysis. A matched-pair analysis of NF-κB expression was performed in cases with vascular involvement and increased tumor vascularity versus matched controls lacking these factors. Results Cox regression analysis of factors evaluated by the American Joint Committee on Cancer Melanoma Staging Committee reproduced the powerful impact of tumor thickness and ulceration in this data set. With the inclusion of vascular factors such as tumor vascularity and vascular involvement, ulceration was no longer significant in predicting overall survival. By multivariate analysis, vascular involvement and tumor vascularity were the strongest predictors of melanoma outcome. Tumor vascularity seems to be a precursor of both vascular involvement and ulceration. A matched-pair tissue array analysis demonstrated the significant correlation between overexpression of NF-κB–p65 and the development of vascular factors. Conclusion Vascular factors play an important role in the progression of malignant melanoma. Ulceration may be a surrogate marker for the interactions between melanoma and the tumor vasculature. NF-κB seems to play an important role in the development of these factors.

scholarly journals The impact of capital structure and certain firm specific variables on the value of the firm: Empirical evidence from Kuwait

2015 ◽  
Vol 13 (1) ◽  
pp. 1191-1200
Author(s):  
Ahmad Mohammad Obeid Gharaibeh ◽  
Adel Mohammed Sarea
Keyword(s):  
Capital Structure ◽  
Stock Exchange ◽  
Influential Factor ◽  
Annual Reports ◽  
Growth Opportunities ◽  
Data Set ◽  
Explanatory Variables ◽  
Size Growth ◽  
One Year ◽  
The Impact

The main objective of this study is to empirically examine the impact of leverage and certain firm-characteristics that are believed to have significant effects on the decision to use debt and on the value of the firm. The sample is composed of 48 companies listed in the Kuwait Stock Exchange (KSE) representing four different sectors. The study uses actual and historical panel data set obtained from the published annual reports of individual firms in addition to the publications of KSE. The study was accomplished using 8 years of data with a total of 239 observations representing the study period 2006-2013. The study uses descriptive statistics, correlation, and multiple-regression analyses to examine the impact of explanatory variables on the value of the firm. The study findings lead to the conclusion that capital structure (leveraging) is the most influential factor on firm’s value. Business risk, previous year’s value (one-year lagged ROA), dividends payout ratio, size, growth opportunities and liquidity of the firm are found to have significant influence on the firm’s value in Model 1 (where ROA is used as a proxy for the value of the firm). In model 2 (i.e., where ROE is used as a proxy of the firm’s value), the findings reveal that capital structure (leveraging); firm’s size, growth opportunities and liquidity of the firm are significant influential of the firm’s value. The study is valuable to academicians, finance managers, policy makers and other stakeholders as it fills the gap of literature by providing up-to-date evidence of the impact of capital structure and other firm specific variables on the value of the firm in Kuwait.

scholarly journals RISK TAKING BEHAVIOR OF INDONESIAN BANKS: ANALAYSIS ON THE IMPACT OF DEPOSIT INSURANCE COOPERATION ESTABLISHMENT

2013 ◽  
Vol 15 (3) ◽  
pp. 3-25
Author(s):  
Moch Doddy Ariefianto ◽  
Soenartomo Soepomo
Keyword(s):  
Risk Taking ◽  
Deposit Insurance ◽  
Interest Rate Risk ◽  
Basel Ii ◽  
Data Set ◽  
Explanatory Variables ◽  
Before And After ◽  
Bank Behavior ◽  
Risk Taking Behavior ◽  
The Impact

This paper studies the risk taking behavior of Indonesian Banking Industry, especially before and after the establishment and the implementation of Deposit Insurance Corporation (IDIC). Using common set of explanatory variables; we test several empirical models to reveal the conduct of risk management by banks. In the spirit of BASEL II Accord, this paper take closer look at three types of risk behaviors namely credit risk, market or interest rate risk and operational risk, prior and post the establishment of IDIC. We tested the hypotheses using panel data set of banks operational in period of 2000-2009. The dataset consists of 121 banks with semiannual frequency (2420 observations). Our findings show that these variables explain well the three type bank risk exposures. The implementation of IDIC alters the bank behavior albeit in somewhat different way than initially hypothesized. The risk taking responses also varies across bank types. We found that State Owned Enterprise banks (SOE) behave differently relative to the rest types of the bank. Related to size, SOE banks behave more conservative after the implementation of IDIC. On the other hand its response on conditioned capital post the IDIC implementation is the opposite; they became more aggressive. We view the public pressure on this state banks has influenced the way they manage the risk.Keywords : Risk taking behavior, BASEL II, Deposit Insurance.JEL Classification: G11, G21, G32, C23

The impact of corruption on sport demand

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlo Amenta ◽  
Paolo Di Betta
Keyword(s):  
Design Methodology ◽  
Linear Models ◽  
Negative Externalities ◽  
Data Set ◽  
Content Type ◽  
Long Run ◽  
Log Linear ◽  
Systemic Corruption ◽  
The Impact

PurposeThe article presents an empirical analysis that evaluates the effects of a systemic corruption scandal on the demand in the short and the long run. In 2006, the Calciopoli scandal uncovered the match rigging in the Italian soccer first division. The exemplary sportive sanction of relegating the primary culprit to the second division imposed further negative externalities on the other clubs. Should we prefer the sportive sanction on the team or the monetary fines for the club?Design/methodology/approachWe estimated two log-linear models of the demand side (stadium attendance) using a fixed effect estimator, on two panel data set made of all the Italian soccer clubs in the first and second division (Serie A and Serie B) for the seasons 2004/2005 to 2009/2010, considering the relegation of the Juventus as the event which impacted the demand for soccer.FindingsRelegating Juventus to Serie B caused an immediate decrease of 18.4% in the attendance for all the teams, both in Serie A and in Serie B, for the three seasons considered, and 1% decrease when all the seasons are considered to measure the fallout of the scandal on the fans' disaffection.Originality/valueThe effect of corruption in sport on demand is an important issue, and there are few studies already published. As for sports economics and management, our results are of interest for sport-governing bodies – as a case study that can help in designing a more effective sanctioning system to prevent corruption episodes.

Adjuvant treatment for resected rectal cancer: Effect of postoperative oxaliplatin added to 5-FU in the neoadjuvant era.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 672-672
Author(s):  
Benjamin Garlipp ◽  
Patrick Stuebs ◽  
Hans Lippert ◽  
Karsten Ridwelski ◽  
Henry Ptok ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Colon Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Adjuvant Treatment ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Data Set ◽  
Cox Regression Analysis ◽  
The Impact

672 Background: Oxaliplatin (Ox) added to postoperative 5-fluorouracil (5FU) based adjuvant treatment has shown a survival benefit in colon cancer. For rectal cancer, the impact of Ox on survival has almost exclusively been tested in studies using 5FU +/- Ox both as a component of preoperative chemoradiotherapy (CRT) and as adjuvant treatment. Only one study (NCT00807911) investigated adjuvant 5FU +/- Ox in patients undergoing preop 5FU based CRT without Ox. Thus, the evidence for the benefit of adding Ox to adjuvant 5FU in patients treated with preop 5FU based CRT is limited. Methods: Data from the prospective German multicenter Quality Assurance in Rectal Cancer observational trial involving more than 300 hospitals of all levels of care throughout Germany were retrospectively analyzed. Patients undergoing R0 total mesorectal excision (TME) for rectal cancer following neoadjuvant 5FU based treatment without oxaliplatin between 01/01/2008 and 12/31/2010 were included. Disease-free survival (DFS) in patients receiving adjuvant treatment with or without Ox was compared using the Kaplan Meier method. The impact of adjuvant treatment with 5FU with or without Ox on DFS was investigated in a Cox regression analysis including open vs. laparoscopic approach, pT stage, pN stage, tumor grading, TME quality grade, and presence of anastomotic leakage as potential confounding factors. Results: The entire data set included 1,861 patients. Data for all variables investigated were available for 599 patients of whom 512 (85%) and 89 (15%) received 5FU based adjuvant treatment without and with Ox, respectively. Mean DFS was not different in patients receiving 5FU only vs. 5FU with Ox (p=0.103). Cox regression analysis revealed no significant impact of adding Ox to adjuvant 5FU on DFS. Of all factors analyzed, only pN2 (vs. pN0) status had an independent adverse effect on DFS (Hazard ratio 4.22, p<0.001). Conclusions: These data indicate that adjuvant Ox added to 5FU does not provide a DFS benefit in rectal cancer patients treated with preoperative 5FU based CRT under routine care conditions. Rectal cancer patients may be different from patients with colon cancer with respect to benefit from adjuvant Ox.

Spurious effects in variational corpus linguistics

2014 ◽  
Vol 19 (4) ◽  
pp. 478-504 ◽  
Author(s):  
José Tummers ◽  
Dirk Speelman ◽  
Dirk Geeraerts
Keyword(s):  
Corpus Linguistics ◽  
Multiple Correspondence Analysis ◽  
Multivariate Model ◽  
Data Matrix ◽  
Response Variable ◽  
Explanatory Variables ◽  
Confounding Variables ◽  
The Impact ◽  
Attributive Adjectives

As repositories of spontaneously realized language, corpora generally have an uncontrolled and unbalanced structure where all variables operate simultaneously. Consequently, a variable’s real effect can be concealed when studied in isolation because of the exclusion of the impact of other potentially confounding variables. Analyzing a variational case study, the alternation between inflected and uninflected attributive adjectives in Dutch, it will be demonstrated how confounding variables alter the impact of explanatory variables on the response variable, resulting in spurious effects in the bivariate analyses. Multiple Correspondence Analysis will be used as a heuristic tool to unveil the association patterns between explanatory variables in the data matrix which induce the spurious effects. Based on these findings, we will argue for a thorough analysis of the database patterns to gain insight in the underlying associations between explanatory variables before modeling their real impact on the response variable in a multivariate model.

Road Closures and Freight Diversion

2012 ◽  
Vol 2269 (1) ◽  
pp. 51-57 ◽  
Author(s):  
David Pierce ◽  
Jeffrey Short
Keyword(s):  
Performance Measures ◽  
The United States ◽  
Valuable Insight ◽  
Data Set ◽  
Sensing Technology ◽  
Global Positioning ◽  
Data Points ◽  
System Data ◽  
The Impact

The FHWA-sponsored Freight Performance Measures (FPM) program generates and monitors a series of performance measures related to the freight transportation system of the United States. The primary information analyzed by the FPM program is a data set consisting of billions of Global Positioning System data points from trucks. These data can be used to demonstrate empirically changes in truck travel patterns and freight performance independent of the availability of roadside sensing technology. A case study that was based on the flooding closure of Arkansas Interstate 40 in May 2011 was presented to show how FPM data can be used to analyze diversion behavior around road closures. This type of empirical analysis is in contrast to the majority of current diversion analyses, which rely on modeling to generate results. Not only do FPM data provide a viable alternative to modeling for studying past events, but the data may provide valuable insight into the underlying assumptions of future models designed to predict the impact of disaster scenarios. By understanding more fully how previous events unfolded, planners can prepare better for the next disaster.

Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques

2000 ◽  
Vol 1722 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Murtaza Haider ◽  
Eric J. Miller
Keyword(s):  
Real Estate ◽  
Large Data ◽  
Transportation Infrastructure ◽  
Residential Real Estate ◽  
Housing Values ◽  
Data Set ◽  
Explanatory Variables ◽  
Greater Toronto Area ◽  
Spatial Autoregressive ◽  
The Impact

Proximity to transportation infrastructure (highways and public transit) influences residential real estate values. Housing values also are influenced by propinquity to a shopping facility or a recreational amenity. Spatial autoregressive (SAR) models were used to estimate the impact of locational elements on the price of residential properties sold during 1995 in the Greater Toronto Area. A large data set consisting of 27,400 freehold sales was used in the study. Moran’s I was estimated to determine the effects of spatial autocorrelation that existed in housing values. SAR models, using a combination of locational influences, neighborhood characteristics, and structural attributes, explained 83 percent variance in housing values. Using the “comparable sales approach,” a spatiotemporal lag variable was estimated for every property in the database. This research discovered that SAR models offered a better fit than nonspatial models. This study also discovered that in the presence of other explanatory variables, locational and transportation factors were not strong determinants of housing values. On the other hand, the number of washrooms and the average household income in a neighborhood were found to be significant determinants of housing values. Stepwise regression techniques were used to determine reduced spatial hedonic models.

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