ABSTRACT
OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis. METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents. RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers’ experience had the strongest influence on the severity of accidents. CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Subject(s)
Accidents, Occupational , Bayes Theorem , Focus Groups , Mining , Occupational InjuriesABSTRACT
OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents.CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
Subject(s)
Accidents, Occupational , Bayes Theorem , Focus Groups , Mining , Occupational InjuriesABSTRACT
Coptis chinensis (Huanglian) is a commonly used traditional Chinese medicine (TCM) herb and alkaloids are the most important chemical constituents in it. In the present study, an isocratic reverse phase high performance liquid chromatography (RP-HPLC) method allowing the separation of six alkaloids in Huanglian was for the first time developed under the quality by design (QbD) principles. First, five chromatographic parameters were identified to construct a Plackett-Burman experimental design. The critical resolution, analysis time, and peak width were responses modeled by multivariate linear regression. The results showed that the percentage of acetonitrile, concentration of sodium dodecyl sulfate, and concentration of potassium phosphate monobasic were statistically significant parameters (P < 0.05). Then, the Box-Behnken experimental design was applied to further evaluate the interactions between the three parameters on selected responses. Full quadratic models were built and used to establish the analytical design space. Moreover, the reliability of design space was estimated by the Bayesian posterior predictive distribution. The optimal separation was predicted at 40% acetonitrile, 1.7 g·mL(-1) of sodium dodecyl sulfate and 0.03 mol·mL(-1) of potassium phosphate monobasic. Finally, the accuracy profile methodology was used to validate the established HPLC method. The results demonstrated that the QbD concept could be efficiently used to develop a robust RP-HPLC analytical method for Huanglian.
Subject(s)
Alkaloids , Bayes Theorem , Chromatography, High Pressure Liquid , Methods , Reference Standards , Chromatography, Reverse-Phase , Methods , Coptis , Chemistry , Drugs, Chinese HerbalABSTRACT
In this paper I critically review the state of the art in population projections, focusing on how uncertainty is handled in three approaches: the classical cohort-component, the frequentist probabilistic model and the Bayesian paradigm. Next, I focus on recent developments on mortality, fertility and migration projections under the Bayesian setting, which have been clearly at the frontier of knowledge in demography. By evaluating the merits and limitations of each framework, I conclude that in the near future the Bayesian paradigm will offer the most promising approach to population projections, since it combines expert opinion, information that demographers have readily available from their empirical analyses and sophisticated statistical and computational methods to deal with uncertainty. Hence, the availability of population forecasts that take uncertainty carefully into account may enhance communication among demographers by allowing for greater flexibility in reflecting demographic beliefs.
O artigo apresenta, inicialmente, uma revisão crítica do estado da arte em projeções de população, focando em como a incerteza é tratada em três abordagens: no modelo clássico de coorte-componente; no modelo probabilístico frequentista; e no paradigma bayesiano. Em seguida, a análise se concentra sobre desenvolvimentos recentes nos modelos de projeções bayesianos de fecundidade, mortalidade e migração, os quais têm claramente se destacado na fronteira do conhecimento em demografia. Ao avaliar os méritos e limitações de cada abordagem, conclui-se que o paradigma bayesiano irá se destacar no futuro próximo como a abordagem mais promissora para as projeções de população, uma vez que combina a opinião de especialistas, as informações que os demógrafos têm disponíveis a partir de suas análises empíricas, assim como métodos estatísticos e computacionais sofisticados para lidar com a incerteza. Assim, a disponibilidade de previsões demográficas acuradas que levem em conta a incerteza pode melhorar a comunicação entre os demógrafos, permitindo uma maior flexibilidade na determinação das previsões demográficas.
El artículo presenta en primer lugar una revisión crítica del estado del arte sobre las proyecciones de población, centrándose en la forma en que se aborda la incertidumbre en tres enfoques diferentes: el modelo clásico de cohorte-componente, el probabilístico frecuentista y el paradigma bayesiano. A continuación, el análisis se centra en la evolución reciente de los modelos de proyecciones bayesianos de la fecundidad, la mortalidad y la migración, que claramente se ubican en la frontera del conocimiento en demografía. A partir de la evaluación de las ventajas y limitaciones de cada enfoque, se concluye que el paradigma bayesiano se destacará en el futuro cercano como el abordaje más prometedor para las proyecciones de población, ya que combina la opinión de los expertos, la información de la que disponen los demógrafos a través de sus análisis empíricos y métodos estadísticos y computacionales sofisticados para lidiar con la incertidumbre. De este modo, la disponibilidad de pronósticos demográficos precisos que tengan en cuenta la incertidumbre puede mejorar la comunicación entre los demógrafos, permitiendo una mayor flexibilidad para elaborar las proyecciones demográficas.