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This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

Based on a review of previous research of organizational learning from experience, this paper aims to point out the notable gaps and unresolved issues in the research area and proposes a “multilevel integrated model of learning from experience”, which could integrate current research findings and serve as the theoretical framework for further investigation.

This paper is a theoretical review.

From the individual, team, organizational and multiple levels, in an order of the outcome of success and failure, this study reviews previous research about organizational learning from experience down to the last detail and points out some of their limitations, including relative fragmented-wise, lack of grope about the underlying motivations, lack of overall framework, etc. Then, this study proposes the “multilevel integrated model of learning from experience”, which provides a systematic and fine-grained framework for studies in this field.

This paper emphasizes that true underlying motivations impelling learning from experience shall be identified and exploration for the antecedents shall be further deepened. Besides, this study figures out that various factors played their parts in the process and outcome of learning from experience through both subjective perception and objective experience. Thus, future research shall distinguish the influence of learning from experience, respectively, into “knowing” and “doing”.

This study is an attempt to review and integrate current research of learning from experience in multiple levels and further differentiates the influences of different experience outcomes (success vs failure). The proposed theoretical model provides clear suggestions of where future research should be directed.

In recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.

Presented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm (Tω) based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.

The result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.

Existing fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.

This study aims to investigate information technology security practices of very small enterprises.

The authors perform a formal information security field study using a representative sample. Using the Control Objectives for IT (COBIT) framework, the authors evaluate 67 information security controls and perform 206 related tests. The authors state six hypotheses about the findings and accept or reject those using inferential statistics. The authors explain findings using the social comparison theory and the rare events bias theory.

Only one-third of all the controls examined were designed properly and operated as expected. About half of the controls were either ill-designed or did not operate as intended. The social comparison theory and the rare events bias theory explain managers’s reliance on small experience samples which in turn leads to erroneous comprehension of their business environment, which relates to information security.

This information is valuable to executive branch policy makers striving to reduce information security vulnerability on local and national levels and small business organizations providing information and advice to their members.

Information security surveys are usually over-optimistic and avoid self-incrimination, yielding results that are less accurate than field work. To obtain grounded facts, the authors used the field research approach to gather qualitative and quantitative data by physically visiting active organizations, interviewing managers and staff, observing processes and reviewing written materials such as policies, procedure and logs, in accordance to common practices of security audits.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

The purpose of this paper is to analyze the spatial patterns of different phenomena in the same geographical space. Andresen’s spatial point pattern test computes a global index (the S-index) that informs on the similarity or dissimilarity of spatial patterns. This paper suggests a generalized S-index that allows perfect similarity and dissimilarity in all situations.

The relevance of the generalized S-index is illustrated with police data from the San Francisco Police Department. In all cases, the original S-index, its robust version – which excludes zero-crime areas – and the generalized alternative were computed.

In the first example, the number of crimes greatly exceeds the number of areas and there are no zero-value areas. A key feature of the second example is that most street segments were free of any criminal activity in both patterns. Finally, in the third case, one type of event is considerably rarer than the other. The original S-index is equal to the generalized index (Case 1) or theoretically irrelevant (Cases 2 and 3). Furthermore, the robust index is unnecessary and potentially biased when the number of at least one phenomenon being compared is lower than the number of areas under study. Thus, this study suggests to replace the S-index with its generalized version.

The generalized S-index is relevant for situations when events are relatively rare –as is the case with crime – and the unit of analysis is small but plentiful – such as addresses or street segments.

The authors examined whether stocks with higher left-tail risk measures earn higher or lower futures returns. Specifically, the authors estimate the cross-sectional principal component of a battery of left-tail risk measures and analyze future returns on stocks with high principal component values. In contrast to finance theories on the risk–return trade-off relationship, the study results show that high left-tail risk stocks have lower future returns. This finding is robust to various left-tail risk measures and controls for other risk factors. Moreover, the negative relationship between the left-tail risk and returns is more pronounced for stocks that are actively traded by retail investors. This empirical result is consistent with behavioral theory that when investors make decisions based on experience, they tend to underweight the likelihood of rare events.

Human error data in the form of human error probabilities should ideally form the corner‐stone of human reliability theory and practice. In the history of human reliability assessment, however, the collection and generation of valid and usable data have been remarkably elusive. In part the problem appears to extend from the requirement for a technique to assemble the data into meaningful assessments. There have been attempts to achieve this, THERP being one workable example of a (quasi) database which enables the data to be used meaningfully. However, in recent years more attention has been focused on the PerformanceShaping Factors (PSF) associated with human reliability. A “database for today” should therefore be developed in terms of PSF, as well as task/ behavioural descriptors, and possibly even psychological error mechanisms. However, this presumes that data on incidents and accidents are collected and categorised in terms of the PSF contributing to the incident, and such classification systems in practice are rare. The collection and generation of a small working database, based on incident records are outlined. This has been possible because the incident‐recording system at BNFL Sellafield does give information on PSF. Furthermore, the data have been integrated into the Human Reliability Management System which is a PSF‐based human reliability assessment system. Some of the data generated are presented, as well as the PSF associated with them, and an outline of the incident collection system is given. Lastly, aspects of human common mode failure or human dependent failures, particularly at the lower human error probability range, are discussed, as these are unlikely to be elicited from data collection studies, yet are important in human reliability assessment. One possible approach to the treatment of human dependent failures, the utilisation of human performance‐limiting values, is described.

Few studies track non‐lethal weapon use by law enforcement agencies (LEAs), the number/level of force used by these agencies, complaints for excessive force, and injuries to officers and suspects, both over time (especially recently) and with a national probability‐based sample. This study aims to address these gaps by developing longitudinal estimates to examine these use‐of‐force issues.

Two surveys of LEAs were conducted (n=518 and n=357 LEAs), covering 2003 to 2008, and statistical weights were used to align the data to be representative of all state and local LEAs in the USA, including adjustments for survey non‐response.

Conducted energy devices (CED) deployment has risen significantly (to about 70 percent of LEAs). However, standard baton use is down to 25 percent in 2008 and when available to the officer, batons are more likely to be left in their vehicles compared to CEDs. Baton use and empty‐hand tactics are becoming less commonly used by officers, but CED use was ranked among the most used tactics from 2005 to 2008. Excessive force complaints against LEAs, internally generated, have more than doubled from 2003 to 2008. Officer injuries varied little from 2003 to 2008, but they are still only about half as common as suspect injuries. Also, only 20 percent of LEAs collect injury data in a database, complicating future research.

This is one of the few studies to track, nationally, the types of non‐lethal weapons in use by LEAs, and force level used, providing aid to LEA executives and policymakers who need to follow new trends in non‐lethal weapons.

Supply chain disruptions can have severe negative consequences on companies. However, studies measuring the financial impacts of supply chain disruptions are largely confined to developed nations and large companies. Therefore, this study aims to analyze the impact of supply chain disruption on small companies in the context of an emerging nation. Further, an attempt has been made to classify supply chain disruptions and measure its impact by its type.

In this research, the event study on 335 supply chain disruption events for a 10 year period starting from 2009 to 2019 has been used.

The results state that the Indian small and medium companies lost −4.49% of shareholder wealth in disruption. The findings also indicate that the financial and environmental disruptions can have severe effect on shareholder wealth as compared to other category.

The study is confined to a developing country. Considering multiple countries can provide comparative results and therefore a global consensus could be achieved.

The outcomes of the results help managers to plan and prioritize supply chain disruptions, regulatory authorities can plug any possible insider trading practices for small companies in the event of supply chain disruptions. Investors can plan and take prudent investing decisions based on the nature of the disruptions.

To the best of the knowledge, this is the first study measuring the supply chain disruption effects on smaller companies in an emerging nation. The study is also novel in incorporating financial disruptions and measuring source wise impact on shareholder wealth.