BASS 4: a software system for ergonomic design and evaluation of working hours

OBJECTIVE: To extend an existing computer programme for the evaluation and design of shift schedules (BASS 3) by integrating workload as well as economic aspects. METHODS: The redesigned prototype BASS 4 includes a new module with a suitable and easily applicable screening method (EBA) for the assessment of the intensity of physical, emotional and cognitive workload components and their temporal patterns. Specified criterion functions based on these ratings allow for an adjustment of shift and rest duration according to the intensity of physical and mental workload. Furthermore, with regard to interactive effects both workload and temporal conditions, e.g. time of day, are taken into account. In a second new module, important economic aspects and criteria have been implemented. Different ergonomic solutions for scheduling problems can now also be evaluated with regard to their economic costs. RESULTS: The new version of the computer programme (BASS 4) can now simultaneously take into account numerous ergonomic, legal, agreed and economic criteria for the design and evaluation of working hours. CONCLUSIONS: BASS 4 can now be used as an instrument for the design and the evaluation of working hours with regard to legal, ergonomic and economic aspects at the shop floor as well as in administrative (e.g. health and safety inspection) and research problems.

Health survey on the Ilha da Conceição, state of Rio de Janeiro: I. Study design and report of selected demographic and environmental characteristics

During July and August of 1968, a Health survey was conducted on the Ilha da Conceição, an area of Niterói containing approximately one thousand households. The survey was conducted by students from the Universidade Federal Fluminense and the University of Maryland, and was under the supervision of faculty of the Department of Tropical Medicine at U.F.F. and from the Department of Preventive Medicine at the University of Maryland, Baltimore, Maryland, U.S.A. The survey was focused on a 25 percent random sample of the households on the island. Information was obtained from a responsible adult at each Household for completion of a Health questionnaire. Physical measurements, as well as laboratory study information were obtained from, all children in these households. A number of environmental sanitation problems were identified on the Ilha da Conceição. In addition, the survey indicated that approximately half the children had not been adequately immunized against diphteria, pertussis and typhoid. Preventable communicable diseases were the major cause of reported deaths which had occurred in infants ou Household members. The Health of the population on the Ilha da Conceição could well be enhanced by the development of an intelligence system indicating the immunization status of all children in the area. In addition a Health education program for the residents could well be beneficial for improvement of sanitary conditions on the island, as well as maternity and well baby care.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.