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In recent years, energy exchange has come to be accepted as the causative agent in accidental injuries. This paper focuses on two fundamental characteristics of accidental workplace injuries, their frequency, and their severity. I propose that the frequency of injuries is determined largely by the nature of the interactions between workers and production processes, while the severity of the occurring injuries will be more strongly affected by the maximum possible harm the production process can cause. Other aspects of the design of the production process may also affect the severity of injuries.

A theoretical model is presented for the probability distribution of wave crest amplitudes in severe seas states with wave breaking. As the severity of a sea state increases, nonlinearities cause an increase in the amplitudes of the largest wave crests with a subsequent modification of the distribution of wave crest amplitudes from the linear Rayleigh theory. In this paper, a theory for the probabilities of these nonlinear crest amplitudes is first reviewed based on earlier work. The further limitations on these nonlinear crest amplitudes by wave breaking are then considered. As a result, a theoretical model is presented to account for both: 1) the nonlinear increase in the highest wave crests, and 2) the selective reduction of some fraction of these high crests due to wave breaking. This model is then verified using several sets of laboratory data for severe breaking seas having approximate JONSWAP wave spectra.

Laboratory simulations of extreme random seas reveal that high wave crests occur more frequently than predicted by the Rayleigh distribution. In this paper, a theory is presented to account for nonlinearities in the sea state to second order resulting in a non-Rayleigh distribution of wave crest and trough amplitudes based on the narrow-band assumption. The resulting probability density functions are then used to predict average wave group characteristics through a modification of linear wave envelope theory which accounts, for example, for a significant decrease in the time intervals between successive runs of high crests compared to linear theory. The nonlinear theory is then verified based on a laboratory data set on deep water wave group statistics for severe seas described by Bretschneider and JONSWAP spectra.

Markov description of wave-crest statistics is considered for the case of severe random seas where Stokes nonlinearities require crests to rise higher than troughs fall. Of interest are the average number of consecutive high waves in runs of high waves and the average number of waves between beginnings of such runs, with high waves being defined in terms of a threshold level above mean water level. Two basic parameters are involved in the description: 1) the probability P that a high wave will occur at a fixed location, and 2) a coefficient C relating P to the probabilities associated with transitions from high to low waves and low to high waves. Comparison with laboratory measurements from scaled Bretschneider seas having a range of nonlinearities indicates that the basic Stokes nonlinearity is contained in the probability P, with no sensible dependence exhibited by the coefficient C. Additional comparison with scaled Jonswap seas illustrates the dependence of this coefficient on spectral shape. A final consideration of results from full-scale hurricane sea conditions, and from corresponding scaled laboratory data, demonstrates the general applicability of the laboratory results presented here to actual wind-driven seas.

Markov description of wave-crest statistics is considered for the case of severe random seas where Stokes nonlinearities require crests to rise higher than troughs fall. Of interest are the average number of consecutive high waves in runs of high waves and the average number of waves between beginnings of such runs, with high waves being defined in terms of a threshold level above mean water level. Two basic parameters are involved in the description: 1) the probability P that a high wave will occur at a fixed location, and 2) a coefficient C relating P to the probabilities associated with transitions from high to low waves and low to high waves. Comparison with laboratory measurements from scaled Bretschneider seas having a range of nonlinearities indicates that the basic Stokes nonlinearity is contained in the probability P, with no sensible dependence exhibited by the coefficient C. Additional comparison with scaled Jonswap seas illustrates the dependence of this coefficient on spectral shape. A final consideration of results from full-scale hurricane sea conditions, and from corresponding scaled laboratory data, demonstrates the general applicability of the laboratory results presented here to actual wind-driven seas.

Evidence from laboratory animals suggests that certain rates of deposition of dust can slow the normal clearance of the lungs by macrophages. If this occurs in humans, then estimates of exposure or dose for epidemiologic models may be improved by explicity incorporating this "overload" phenomenon into lung dose estimates. Using a model of dust overload, the authors estimated the lung dust dose for a group of workers exposed to silicon carbide dust. From a knowledge of underlying biologic mechanisms and using goodness of fit tests, the authors identified parameters for the dosimetric model that yielded dose estimates which fit the epidemiologic data well. The dosimetric estimates also compare favorably with cumulative exposure as risk predictors of radiographic evidence of pulmonary fibrosis. The evidence for overload is not strong, however, since the data are also consistent with other clearance patterns including very slow linear one-compartment clearance. Nevertheless, in some data sets, use of a dosimetric model instead of cumulative exposure to dust may reduce misclassification in exposure and improve fit to epidemiologic data. Furthermore, evidence of a better fit would provide valuable information about biologic mechanisms or aspects of dose such as the effect of short-term, high-intensity exposures. These in turn might provide information about prevention strategies such as standard setting.

A case control study of lung cancer in Florence, Italy was performed to investigate occupational risk factors for both men and women. The case series (n = 376) comprised all incident, histologically confirmed cases of primary lung cancer occurring in a three year period. Controls (n = 892) were patients in the same hospital of similar age, sex, date of admission, and smoking status with discharge diagnoses other than lung cancer or suicide. A detailed occupational history was collected from each subject directly. Logistic regression models were used to calculate odds ratios for specific occupations compared to all others. Among men, the lung cancer risk for bricklayers using firebrick and other refractory materials was elevated (adjusted odds ratio 6.5, 95% limits 2.1 and 20.9). Female hat makers, probably exposed to arsenic while making felt hats, had an elevated risk of lung cancer (6 cases versus 0 controls, p = 0.01). Risks in other occupations are discussed.

Risk of lung cancer related to region of birth in Italy was investigated among migrants to Florence, in a case control study of all histologically confirmed incident cases of primary lung cancer in a three year period in that city (n = 376). Controls (n = 892) were patients in the same hospital of similar age, sex, date of admission, and smoking status with discharge diagnoses other than lung cancer or suicide. Information on place of birth and year of migration to Florence was collected directly from each subject, along with a detailed occupational history. Logistic regression models were used to calculate the odds ratio for birth in the south of Italy relative to birth elsewhere. Male migrants from the south have an odds ratio of lung cancer of 0.5 (95% limits 0.3 to 0.7) relative to those born elsewhere. This "protective effect" is not explained by smoking or by any known occupational risk. The risk is lowest among those born on the island of Sicily (odds ratio 0.2 compared to those born in the centre-north).

A theoretical model is presented for the probability distribution of wave crest amplitudes in severe seas states with wave breaking. As the severity of a sea state increases, nonlinearities cause an increase in the amplitudes of the largest wave crests with a subsequent modification of the distribution of wave crest amplitudes from the linear Rayleigh theory. In this paper, a theory for the probabilities of these nonlinear crest amplitudes is first reviewed based on earlier work. The further limitations on these nonlinear crest amplitudes by wave breaking are then considered. As a result, a theoretical model is presented to account for both: 1) the nonlinear increase in the highest wave crests, and 2) the selective reduction of some fraction of these high crests due to wave breaking. This model is then verified using several sets of laboratory data for severe breaking seas having approximate JONSWAP wave spectra.

Laboratory simulations of extreme random seas reveal that high wave crests occur more frequently than predicted by the Rayleigh distribution. In this paper, a theory is presented to account for nonlinearities in the sea state to second order resulting in a non-Rayleigh distribution of wave crest and trough amplitudes based on the narrow-band assumption. The resulting probability density functions are then used to predict average wave group characteristics through a modification of linear wave envelope theory which accounts, for example, for a significant decrease in the time intervals between successive runs of high crests compared to linear theory. The nonlinear theory is then verified based on a laboratory data set on deep water wave group statistics for severe seas described by Bretschneider and JONSWAP spectra.