The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P1 (pH and conductivity), P2 (nitrate and manganese) and P3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P2 and included in group P3,for which the sampling frequency is lower, based on the chemical parameters included in group P1. In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R2 values varying in the range 0.9960–0.9989 for the training set and 0.9993–0.9952 for the test set

The degradation of available water quality is a consequence of pollution, environmental degradation, and/or climate change resulting from human activities. Designing strategies to foster the adoption of sustainable behaviors requires an awareness within populations regarding water quality, water availability, and the proper consumption of water. The goal of the present work was to assess the population awareness of sustainable water usage practices. The data collection involved administering a questionnaire to a sample of 1723 participants, encompassing individuals of both genders and aged between 15 and 84 years. The questionnaire includes five specific themes (i.e., water quality, water availability, water resources, water saving, and water reuse). The outcomes indicate that despite a higher awareness of sustainable water usage practices, participants reveal low levels of awareness regarding water reuse. Furthermore, measures of participants’ overall awareness and the potential for improving it are presented. These metrics are crucial in enabling the grouping of participants according to similar characteristics, thereby streamlining the design of targeted interventions.

Psychosocial risks are increasingly a type of risk analyzed in organizations beyond chemical, physical, and biological risks. To this type of risk, a greater attention has been given following the update of ISO 9001: 2015, more precisely the requirement 7.1.4 for the process operation environment. The update of this normative reference was intended to approximate OHSAS 18001: 2007 reference updated in 2018 with the publication of ISO 45001. Thus, the organizations are increasingly committed to achieving and demonstrating good occupational health and safety performance. The aim of this study was to characterize the psychosocial risks in a cryopreservation laboratory and to develop a predictive model for psychosocial risk management. The methodology followed to collect the information was the inquiry by questionnaire that was applied to a sample comprising 200 employees. The results show that most of the respondents are aware of the psychosocial risks, identifying interpersonal relationships and emotional feelings as the main factors that lead to this type of risks. Furthermore, terms such as lack of resources, working hours, lab equipment, stress, and precariousness show strong correlation with psychosocial risks. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the psychosocial risks. This work presents the development of an intelligent system that allows identifying the weaknesses of the organization and contributing to the enhancement of the psychosocial risks management.

Predictive modelling is a process used in predictive analytics to create a statistical model of future behaviour. Predictive analytics is the area...