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Research in the area of financial distress often uses a proportional hazards model to determine the influence of covariates on the duration of time that precedes financial distress. Acritical issue in the use of a proportional hazards model is the use of time‐invariant and time‐dependent covariates. Time‐invariant covariates remain fixed while time‐dependent covariates change during the estimation of the model. Although the choice of covariates might substantially affect the estimation of the proportional hazards model, existing literature often fails to consider the potential effect of this choice on model estimation. This paper reviews the distinction between time‐invariant and time‐dependent covariates and the effect of covariate selection on the estimation of a proportional hazards model. Using a sample of financially distressed and non‐financially distressed firms, this paper suggests the choice of time dependence substantially influences model estimation and that covariate selection should be given more serious consideration in financial distress research.

The software reliability models to describe the reliability growth phenomenon are formulated by any stochastic point process with state‐dependent or time‐dependent intensity function. On the other hand, to deal with the environmental data, which consists of covariates influencing times to software failure, it may be useful to apply the Cox’s proportional hazards model for assessing the software reliability. In this paper, we review the proportional hazards software reliability models and discuss the problem to determine the optimal software release time under the expected total software cost criterion. Numerical examples are devoted to examine the dependence of the covariate structure in both the software reliability prediction and the optimal software release decision.

This paper aims to present a case study where proportional hazards modeling software is used to evaluate the potential benefits of a condition‐based maintenance policy for military vehicle diesel engines.

Maintenance records for diesel engines were supplied by the UK Ministry of Defence. A proportional hazards model based on these data was created using EXAKT software. Covariate parameters were estimated using the maximum likelihood method and transition probabilities were established using a Markov Chain model. Finally, decision parameters were entered to create an optimal decision model.

Two significant covariates were identified as influencing the hazard rate of the engines. In addition, the optimal decision model indicated a potential economic saving of up to 30 per cent.

A model of this nature is particularly useful to predict failures, improve maintenance policies, and possibly reduce maintenance costs. In addition, the cost of implementing maintenance policies based on this model should be balanced with the potential to reduce the risk of danger to personnel.

The model presented provides military personnel with a decision tool that optimizes the maintenance policy for diesel engines installed in military vehicles.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

Industry decision makers often rely on a risk-based approach to perform inspection and maintenance planning. According to the Risk-Based Inspection and Maintenance Procedure project for the European industry, risk has two main components: probability of failure (PoF) and consequence of failure (CoF). As one of these risk drivers, a more accurate estimation of the PoF will contribute to a more accurate risk assessment. Current methods to estimate the PoF are either time-based or founded on expert judgement. This paper suggests an approach that incorporates the proportional hazards model (PHM), which is a statistical procedure to estimate the risk of failure for a component subject to condition monitoring, into the risk-based inspection (RBI) methodology, so that the PoF estimation is enhanced to optimize inspection policies.

To achieve the overall goal of this paper, a case study applying the PHM to determine the PoF for the real-time condition data component is discussed. Due to a lack of published data for risk assessment at this stage of the research, the case study considered here uses failure data obtained from the simple but readily available Intelligent Maintenance Systems bearing data, to illustrate the methodology.

The benefit of incorporating PHM into the RBI approach is that PHM uses real-time condition data, allowing dynamic decision-making on inspection and maintenance planning. An additional advantage of the PHM is that where traditional techniques might not give an accurate estimation of the remaining useful life to plan inspection, the PHM method has the ability to consider the condition as well as the age of the component.

This paper is proposing the development of an approach to incorporate the PHM into an RBI methodology using bearing data to illustrate the methodology. The CoF estimation is not addressed in this paper.

This paper presents the benefits related to the use of PHM as an approach to optimize the PoF estimation, which drives to the optimal risk assessment, in comparison to the time-based approach.

Reviews previous research on predicting financial distress and the effects of US Chapter 11 bankruptcy (C11B); and explains how survival analysis and Cox’s (1972) proportional hazards model can be used to estimate the financial outcome for the shareholders of C11B. Reduces a previous data set (Russel et al 1999) of 154 companies entering C11B between 1984 and 1993 to 59 (54 of which gave no value to shareholders) and estimates two models to predict this: one based on firm‐specific covariates only and the other adding market‐wide covariates. Explains the methodology, presents the results and uses receiver operating characteristic curves to compare the predictive accuracy of the two. Finds little difference between the and suggests using the simpler model. Briefly summarizes the variables which are most useful in predicting the value outcomes of C11B for shareholders and recognizes the limitations of the study.

Entrepreneurs nurture their ambitions of founding tech start-ups that facilitate significant innovations despite vulnerability and considerable uncertainty by resolutely addressing multiple challenges to avert failures. The paper aims to answer how soon do tech start-ups fail, given their lifecycle comprising multiple stages of formation and what attributes hasten failure of tech start-ups over their lifecycle? These questions have not been answered adequately, particularly in the context of India's emerging economy, where an aspiring start-up ecosystem is striving to flourish at an exceptional rate.

The study addressed two specific objectives: (1) Does life expectancy vary between life-cycle stages? and (2) What attributes impact tech start-ups' failures? Primary data were gathered from 151 cofounders (101 who have experienced failure and 50 who are successful and continuing their operations) from India's 6 leading start-up hubs. The survival analysis techniques were used, including non-parametric Kaplan–Meier estimator, to study the first objective and semi-parametric Cox proportional hazard regression to explore the second objective.

The survival probability log-rank statistics ascertain that life expectancy is different across the life-cycle stages, namely emergence, stability and growth. The hazard ratios (HRs) throw light on attributes like stage, revenue, conflict with investors, number of current start-ups, cofounder experience, level of confidence (LoC) and educational qualifications as the key attributes that influence start-up life expectancy over its lifecycle.

The empirical study on tech start-ups' life expectancy has practical implications for entrepreneurs and investors besides guiding the ecosystem's policymakers. First, the study helps entrepreneurs plan for resources and be aware of their start-up journey's potential pitfalls. Second, the study helps investors to establish the engagement framework and plan their future funding strategy. Third, the study helps policymakers to design and establish progressive support mechanisms that can prevent a start-up's failure.

First and foremost, start-up life expectancy study by life-cycle stages provide detailed insights on start-ups' failures. The theoretical framework defined is replicable, scalable and distinctly measurable for studying the start-up failure phenomenon. The life expectancy of tech start-ups by life-cycle stage is a critical empirical contribution. Next, the attributes impacting start-up life expectancy are identified in the context of an emerging economy.

The purpose of this paper is to discuss operation and maintenance challenges under Arctic conditions and to propose a methodology to assess systems' reliability, maintainability and maintenance costs under the influence of the Arctic operational environment.

A model is suggested for quantifying maintenance costs while taking into account uncertainty due to lack of appropriate data and operational experience using the proportional hazard model and proportional repair model as well as Monte Carlo simulation.

The results show that the operating environment has a considerable influence on the number of failures, the maintenance and repair times and consequently on maintenance cost. Forecasting the maintenance costs based on technical characteristics (e.g. reliability and maintainability) and considering the operational environment, as well as including uncertainty analysis using Monte Carlo simulation, provide more trustworthy information in the decision‐making process.

There are few data and little experience available regarding the operation of offshore oil and gas production systems in the Arctic region. Using the available data collected from similar systems, but in a different operational environment, may result in uncertain or incorrect analysis results. Hence, the method that is used for maintenance cost analysis must be able to quantify the effect of the operating environment on the system reliability and maintainability as well as to quantify the uncertainty.

The paper presents a statistical approach that will be useful in predicting maintenance cost considering the lack of appropriate reliability data from equipment operated in Arctic conditions. The approach presented is valuable for the industrial practitioners in the Arctic region, and may also be adapted to other areas where there is lack of data and operational experience.

Building on the steps to war model, this paper seeks to examine the impact that territorial Militarized Interstate Disputes (MID) have on the time it takes a dyad to go to war after it experiences its first MID.

A model common to epidemiological research, the hazard model, is employed to examine the dyadic relationship from the time of the first MID forward. This is an improvement to dyadic analysis, as most research examines the characteristics of individual MIDs in isolation.

Dyads with a history of territorial MIDs go to war much more quickly than dyads without a history of territorial MIDs. Future research should explore the relationship between territory, war, and power status to test the assertion that minor power states engage in power politics behavior less frequently.

Conflict resolution measures need to be employed more quickly when states have unresolved territorial issues. Mediation generally does not occur quickly, which may explain why territorial issues are less likely to be referred to mediators and less successfully mediated. The results presented herein highlight the need for flexible, quick responses to certain crises and the need to settle borders and other territorial disputes permanently to avoid war.

The paper tests a critical component of the steps to war model and examines the assertion that the historical relationship between states affects conflict decisions.