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(Rev Méd Chile 2009; 137: 1463-8).

The supplier selection problem has been discussed in literature within the supply chain management subject and it is extremely important due to its impact on the entire supply chain configuration, strategy and performance. This work presents a decision model based on the fuzzy analytic hierarchy process method and its application in a real case of maintenance supplier selection in a large Brazilian railway operator. Eight criteria were adopted -technical capacity, financial status, relationship, operations management, security management, infrastructure, historic performance and costs -for evaluating five potential suppliers. In the case study, both first and second ranked suppliers by the method have been selected by the company for providing the services and the model was adopted as a standard procedure within the organization for contracts over US$ 300,000.

The aim of the present study was to establish the prevalence of thyroid disturbances in patients consulting for panic and mood disorders. These data may be relevant because thyroid functional alterations affect the success of treatment in these pathologies. We studied prospectively 268 psychiatric outpatients (204 females and 64 males) diagnosed by DSM-IV criteria. We excluded patients with addictive disorders and major medical disease. We measured TSH, Free T4 (FT4) and antimicrosomal antibodies (AMA). We diagnosed classical hypothyroidism when the TSH value was >10 microUI/ml (NV=0.25-4.3) and subclinical hypothyroidism when the TSH value was between 5-10 microUI/ml. Hyperthyroidism was diagnosed when FT4 >1.4 (NV=0.8-1.4), the TSH suppressed and the radioiodine uptake >20% (NV=5-15). Positive antimicrosomal antibodies (AMA) titres were >1:100 dilution. Hypothyroidism was diagnosed in 26/268 patients (9.7%); 10 cases corresponded to the classical form (38.5%) and 16 cases to the subclinical form (61.5%). Hyperthyroidism was found in 6/268 patients (2.2%). Normal thyroid function with positive AMA was found in 28/268 patients (10.4%). Hypothyroidism was more common in patients with mood disorders, and hyperthyroidism in patients with panic disorders. Patients with panic disorder had significant higher levels of FT4. The prevalence of positive AMA, hypothyroidism and hyperthyroidism was higher in women than men. We found a high frequency of thyroid abnormalities in a psychiatric outpatient population. These data suggests that routine evaluation of thyroid function should be considered in patients consulting for mood and panic disorders.

Maintenance in small hydroelectric plants is fundamental for guaranteeing the expansion of clean energy sources and supplying the energy estimated to be necessary for the coming years. Most fault diagnosis models for hydroelectric generating units, proposed so far, are based on the distance between the normal operating profile and newly observed values. The extended isolation forest model is a model, based on binary trees, that has been gaining prominence in anomaly detection applications. However, no study so far has reported the application of the algorithm in the context of hydroelectric power generation. We compared this model with the PCA and KICA-PCA models, using one-year operating data in a small hydroelectric plant with time-series anomaly detection metrics. The algorithm showed satisfactory results with less variance than the others; therefore, it is a suitable candidate for online fault detection applications in the sector.

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Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators.