Valorization of agro-industrial wastes towards the production of rhamnolipids

In this work, oil mill wastewater (OMW), a residue generated during olive oil extraction, was evaluated as an inducer of rhamnolipid production. Using a medium containing as sole ingredients corn steep liquor (10%, v/v), sugarcane molasses (10%, w/v) and OMW (25%, v/v), Pseudomonas aeruginosa #112 produced 4.5 and 5.1 g of rhamnolipid per liter in flasks and reactor, respectively, with critical micelle concentrations as low as 13 mg/l. Furthermore, in the medium supplemented with OMW, a higher proportion of more hydrophobic rhamnolipid congeners was observed comparing with the same medium without OMW. OMW is a hazardous waste which disposal represents a serious environmental problem; therefore, its valorization as a substrate for the production of added-value compounds such as rhamnolipids is of great interest. This is the first report of rhamnolipid production using a mixture of these three agro-industrial by-products, which can be useful for the sustainable production of rhamnolipids.

Laccase immobilization on bacterial nanocellulose membranes: antimicrobial, kinetic and stability properties

This work studied the physical immobilization of a commercial laccase on bacterial nanocellulose (BNC) aiming to identify the laccase antibacterial properties suitable for wound dressings. Physico-chemical analysis demonstrates that the BNC structure is manly formed by pure crystalline I cellulose. The pH optimum and activation energy of free laccase depends on the substrate employed corresponding to pH 6, 7, 3 and 57, 22, 48 kJ mol1 for 2,6-dimethylphenol (DMP), catechol and 2,2 -azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), respectively. The Michaelis-Menten constant (Km) value for the immobilized laccase (0.77 mM) was found to be almost double of that of the free enzyme (0.42 mM). However, the specific activities of immobilized and free laccase are similar suggesting that the cage-like structure of BNC allows entrapped laccase to maintain some flexibility and favour substrate accessibility. The results clearly show the antimicrobial effect of laccase in Gram-positive (92%) and Gram-negative (26%) bacteria and cytotoxicity acceptable for wound dressing applications.

A aprendizagem do léxico na compreensão leitora

Relatório de estágio de mestrado em Ensino do Português no 3.º Ciclo do Ensino Básico e no Ensino Secundário e do Espanhol nos Ensinos Básico e Secundário

O ensino da escrita: produção de crónicas

Relatório de estágio de mestrado em Ensino do Português no 3º Ciclo do Ensino Básico e do Ensino Secundário e de Espanhol nos Ensinos Básico e Secundário

A design of experiments to assess phosphorous removal and crystal properties in struvite precipitation of source separated urine using different Mg sources

Supplementary data associated with this article can be found, in the online version, at: http://dx.doi.org/10.1016/j.cej.2016.03.148.

Mother’s depression at childbirth does not contribute to the effects of antenatal depression on neonate’s behavioral development

Background: Maternal depression is a worldwide phenomenon that has been linked to adverse developmental outcomes in neonates. Aims: To study the effect of antenatal depression (during the third trimester of pregnancy) on neonate behavior, preference, and habituation to both the mother and a stranger’s face/voice. To analyze mother’s depression at childbirth as a potential mediator or moderator of the relationship between antenatal depression and neonate behavioral development. Method: A sample of 110 pregnant women was divided in 2 groups according to their scores on the Edinburgh Postnatal Depression Scale during pregnancy (EPDS; ≥10, depressed; <10, non-depressed). In the first 5 days after birth, neonatal performance on the Neonatal Behavioral Assessment Scale (NBAS) and in the ‘Preference and habituation to the mother’s face/voice versus stranger’ paradigm was assessed; each mother filled out an EPDS. Results: Neonates of depressed pregnant women, achieved lower scores on the NBASs (regulation of state, range of state, and habituation); did not show a visual/auditory preference for the mother’s face/voice; required more trials to become habituated to the mother’s face/voice; and showed a higher visual/auditory preference for the stranger’s face/voice after habituation compared to neonates of non-depressed pregnant women. Depression at childbirth does not contribute to the effect of antenatal depression on neonatal behavioral development. Conclusion: Depression even before childbirth compromises the neonatal behavioral development. Depression is a relevant issue and should be addressed as a routine part of prenatal health care.

Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.

Consequences of the Prolonged Waiting Time for Patients Candidates for Heart Surgery

OBJECTIVE - To assess mortality and the psychological repercussions of the prolonged waiting time for candidates for heart surgery. METHODS - From July 1999 to May 2000, using a standardized questionnaire, we carried out standardized interviews and semi-structured psychological interviews with 484 patients with coronary heart disease, 121 patients with valvular heart diseases, and 100 patients with congenital heart diseases. RESULTS - The coefficients of mortality (deaths per 100 patients/year) were as follows: patients with coronary heart disease, 5.6; patients with valvular heart diseases, 12.8; and patients with congenital heart diseases, 3.1 (p<0.0001). The survival curve was lower in patients with valvular heart diseases than in patients with coronary heart disease and congenital heart diseases (p<0.001). The accumulated probability of not undergoing surgery was higher in patients with valvular heart diseases than in the other patients (p<0.001), and, among the patients with valvular heart diseases, this probability was higher in females than in males (p<0.01). Several patients experienced intense anxiety and attributed their adaptive problems in the scope of love, professional, and social lives, to not undergoing surgery. CONCLUSION - Mortality was high, and even higher among the patients with valvular heart diseases, with negative psychological and social repercussions.

Development of composite auxetic structures for civil engineering applications

Tese de Doutoramento em Engenharia Têxtil

Experimental validation of a proposed single-phase five-level active rectifier operating with model predictive current control

This paper presents a model predictive current control applied to a proposed single-phase five-level active rectifier (FLAR). This current control strategy uses the discrete-time nature of the active rectifier to define its state in each sampling interval. Although the switching frequency is not constant, this current control strategy allows to follow the reference with low total harmonic distortion (THDF). The implementation of the active rectifier that was used to obtain the experimental results is described in detail along the paper, presenting the circuit topology, the principle of operation, the power theory, and the current control strategy. The experimental results confirm the robustness and good performance (with low current THDF and controlled output voltage) of the proposed single-phase FLAR operating with model predictive current control.

A multi–physics approach applied to masonry structures with non–hydraulic lime mortars

Tese de Doutoramento em Engenharia Civil (área de especialização em Engenharia de Estruturas).