A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10-20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation.

Insider threats: the major challenge to security risk management

Security risk management is by definition, a subjective and complex exercise and it takes time to perform properly. Human resources are fundamental assets for any organization, and as any other asset, they have inherent vulnerabilities that need to be handled, i.e. managed and assessed. However, the nature that characterize the human behavior and the organizational environment where they develop their work turn these task extremely difficult, hard to accomplish and prone to errors. Assuming security as a cost, organizations are usually focused on the efficiency of the security mechanisms implemented that enable them to protect against external attacks, disregarding the insider risks, which are much more difficult to assess. All these demands an interdisciplinary approach in order to combine technical solutions with psychology approaches in order to understand the organizational staff and detect any changes in their behaviors and characteristics. This paper intends to discuss some methodological challenges to evaluate the insider threats and its impacts, and integrate them in a security risk framework, that was defined according to the security standard ISO/IEC_JTC1, to support the security risk management process.

Limitations and improvements to the risk assessment process in Olive Oil Mills: the views of OSH practitioners

Although most of the accidents occurred in Olive Oil Mill (OOM) resulted from “basic” risks, there is a need to apply adequate tools to support risk decisions that can meet the specificities of this sector. This study aims to analyse the views of Occupational, Safety & Health (OSH) practitioners about the risk assessment process in OOM, identifying the key difficulties inherent to the risk assessment process in these sector, as well as identifying some improvements to the current practice. This analysis was based on a questionnaire that was developed and applied to 13 OSH practitioners working at OOM. The results showed that the time available to perform the risk assessment is the more frequent limitation. They believe that the methodologies available are not an important limitation to this process. However, a specific risk assessment methodology, that includes acceptance criteria adjusted to the OOM reality, using risk metrics supported on the frequency of accidents and workdays lost, were indicated as being also an important contributions improve the process. A semi-quantitative approach, complemented with the use of the sector accident statistics, can be a good solution for this sector. However, further strategies should also be adopted, mainly those that can lead to an easy application of the risk assessment process.

Systematic design analysis and risk management on engineered nanoparticles occupational exposure

The production of nanotechnology-based products is increasing, along with the conscience of the possible harmful effects of some nanomaterials. The “safety-by-design” approaches are getting attention as helpful tools to develop safer products and production processes. The Systematic Design Analysis Approach could help to identify the solutions to control the workplace risks by defining the emission and exposure scenarios and the possible barriers to interrupt them. By applying this approach in a photocatalytic ceramic tiles development project it was possible to identify relevant nanoparticles emission scenarios and related barriers, and defining possible ways to reduce it.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Development of a model for occupational risk acceptance criteria definition in industrial settings

Doctoral Dissertation for PhD degree in Industrial and Systems Engineering