Organisational Learning using Near-Miss and Accident Data within and outside your Organisation

1996 ◽  
pp. 153-167
Author(s):  
Floor Koornneef ◽  
André Spijkervet ◽  
Jurek Karczewski
Keyword(s):  
Organisational Learning ◽  
Near Miss ◽  
Accident Data

Markov Chain Analysis of Accident Data: The Case of an Oil and Gas Firm in the Niger Delta Area of Nigeria

2010 ◽  
Vol 1 ◽  
pp. 29-38 ◽  
Author(s):  
A.C. Igboanugo
Keyword(s):  
Niger Delta ◽  
Oil And Gas ◽  
Road Traffic ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Hazard Identification ◽  
Near Miss ◽  
Industrial Accidents ◽  
Identification Schemes ◽  
Accident Data

Concerned that management of job-oriented accidents has remained a daunting challenge for all stakeholders in Nigeria oil-industry, a longitudinal study was made to appraise the patterning of episodic incidence of industrial accidents in a typical oil and gas company operating in the Nigeria flank of the oil-rich Niger Delta Basin. A 10-year historical accident data were characterized and found to have absorbing chain properties. Four open-and-shot transition states namely : fatality, accident, near-miss and unsafe act, that are subject to ergodicity, were identified and used to craft a value digraph which define the stable transition probability matrix. The results, which suggest that on the average, 59% of staff are wasted through fatality or severe accidents, also points to escalating rates of fatalities and disabilities stemming from accidents thus calling for the need to review the existing hazard operations studies (HAZOPS) and hazard identification schemes (HAZIDS) so as to assist in whittling down, as low as reasonably practicable, the perceived unpleasant trend. The author is of the opinion that industrial accident victims, unlike in road traffic accident (RTA), habituate several times before entering the absorbing state.

scholarly journals Examination the Influence of Human Factors in Indian Building Construction Sites

2019 ◽  
Vol 8 (4S2) ◽  
pp. 212-218
Keyword(s):  
Human Factors ◽  
Rank Correlation ◽  
Language Education ◽  
Building Construction ◽  
Near Miss ◽  
Construction Sites ◽  
Moving Vehicle ◽  
Spearman’S Rank Correlation ◽  
Accident Data ◽  
Using Data

This research intends to explore the human factors which lead to non-fatal accidents (i.e. first aid case, near miss, lost time injury) and assessing safety supervision factors in building construction sites. A total of hundred non – fatal accident data is collected for the analysis. Human factors such as age, experience, language, education level and competency of the worker are explored using data analysis method. The root causes for the accidents are also determined and it is found that 22% of the construction site accidents are due to improper supervision. Furthermore a questionnaire survey is conducted among the Safety Engineers, Site Engineers, Supervisors and Workers to elicit the actual happenings about the safety supervision in the site. The questionnaire mainly focused on safety supervision factors and Relative Importance Index (RII) is used to rank the safety factors. As a result, monitoring hoist operation (0.73), moving vehicle (0.72) and checking the stability of scaffolds (0.69) shows higher level of disagreement which means that these factors have been given least priority. In order to measure the strength of relationship between the respondents, Spearman’s Rank Correlation is done. Through the Spearman’s correlation it is known that Safety Engineers vs Supervisors (0.83) and Safety Engineers vs Site Engineers (0.81) has high correlation values which indicates that these respondents have provided similar rankings.

Study on the Analysis of Near-Miss Ship Collisions Using Logistic Regression

2017 ◽  
Vol 21 (3) ◽  
pp. 467-473 ◽  
Author(s):  
Kwang-Il Kim ◽  
◽  
Jung Sik Jeong ◽  
Byung-Gil Lee ◽  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Regression Analysis ◽  
High Risk ◽  
High Frequency ◽  
Near Miss ◽  
Collision Risk ◽  
Undesired Event ◽  
Accident Data ◽  
Ship Collision

Generally, risk assessment for a ship collision can be performed by analyzing the trajectories of two ships as they get close to each other. A near-miss collision between ships is an undesired event that did not result in collision, but had a high risk of doing so. Due to the high frequency of these occurrences, many actual accident data samples can be obtained. In this paper, we extract various variables related to near-miss collisions from this data, such as Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA) and Collision Avoidance Variance (CAV). To assess near-miss collision risk, logistic regression analysis is performed by categorizing encounter types based on ship trajectories collected over 4 months in coastal water areas.

Think Metabolic Error in Cases of Near-Miss SIDS

2007 ◽  
Vol 37 (11) ◽  
pp. 19
Author(s):  
NANCY WALSH
Keyword(s):  
Near Miss

The Grasp Cell Hypothesis: Near-miss effect points to common neurological machinery in posterior parietal cortex for controllable objects and concepts

2018 ◽  
Author(s):  
Imogen M Kruse
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Near Miss ◽  
Illusion Of Control ◽  
Gambling Behaviour ◽  
Drug Seeking ◽  
Spatial Influence ◽  
Reward Probability ◽  
Posterior Parietal ◽  
Action Value

The near-miss effect in gambling behaviour occurs when an outcome which is close to a win outcome invigorates gambling behaviour notwithstanding lack of associated reward. In this paper I postulate that the processing of concepts which are deemed controllable is rooted in neurological machinery located in the posterior parietal cortex specialised for the processing of objects which are immediately actionable or controllable because they are within reach. I theorise that the use of a common machinery facilitates spatial influence on the perception of concepts such that the win outcome which is 'almost complete' is perceived as being 'almost within reach'. The perceived realisability of the win increases subjective reward probability and the associated expected action value which impacts decision-making and behaviour. This novel hypothesis is the first to offer a neurological model which can comprehensively explain many empirical findings associated with the near-miss effect as well as other gambling phenomena such as the ‘illusion of control’. Furthermore, when extended to other compulsive behaviours such as drug addiction, the model can offer an explanation for continued drug-seeking following devaluation and for the increase in cravings in response to perceived opportunity to self-administer, neither of which can be explained by simple reinforcement models alone. This paper therefore provides an innovative and unifying perspective for the study and treatment of behavioural and substance addictions.

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