The increasing worldwide demand for energy bound to a strong dependence of fossil fuels has considerably intensified the concentration of greenhouse gases in the atmosphere, reaching alarming levels. ...Among those gases, carbon dioxide is considered the main responsible for global warming due to its higher concentration. In order to mitigate the negative effects of global warming and to reduce emissions, many technologies have been developed in the last decades to separate and recover carbon dioxide (CO2) at different capture scenarios. Adsorption processes rely on the use of highly porous solids such as activated carbons, which are either commercially. Pressure Swing Adsorption (PSA) is a cyclic adsorption process, which allows continuous separation of gas streams. PSA is performed by periodic changes of pressure aiming the optimization of contaminants removal and is considered viable for separation of CO2 from flue gases containing about 5-15% v/v. To achieve a certain performance, a PSA process may consist of several steps, columns and cycle time. One of the most basic configurations comprises four steps: pressurization, feed, blowdown and purge. The performance of a PSA process is usually evaluated by the purity, recovery and productivity reached. This study presents experimental and simulated data obtained from a bench-scale PSA, with a maximum pressure of 6bar for pressurization and feed steps and minimum of 1bar for blowdown and purge steps. The unit was tested with a mixture containing 85% of N2 and 15% of CO2 (on a molar basis). Carbon dioxide and nitrogen breakthrough curves were obtained under typical conditions of post combustion capture. A mathematical-phenomenological model combining momentum, mass and heat balances and using the Linear Driving Force approach (LDF) for mass transport and Langmuir model for equilibrium was applied in this study to simulate the dynamic behavior of the process. The performance tests presented productivity of 15mol h-1 kg-ads-1 and, according to the changes of step time, N2 purity of 97.7%. The model predicted reasonably the breakthrough curves and temperature profiles, with more precision for the latter. The combination of the simulation tool with and experimental PSA unit is very valuable for a deeper understanding of the involved phenomena and helpful with the design of optimized and efficient CO2 adsorption-based capture processes.
The development of malign cells that can grow in any part of the stomach, known as gastric cancer, is one of the most common causes of death worldwide. In order to increase the survival rate in ...patients with this condition, it is essential to improve the decision-making process leading to a better and more efficient selection of treatment strategies. Nowadays, with the large amount of information present in hospital institutions, it is possible to use data mining algorithms to improve the healthcare delivery. Thus, this study, using the CRISP methodology, aims to predict not only the mortality associated with this disease, but also the occurrence of any complication following surgery. A set of classification models were tested and compared in order to improve the prediction accuracy. The study showed that, on one hand, the J48 algorithm using oversampling is the best technique to predict the mortality in gastric cancer patients, with an accuracy of approximately 74%. On the other hand, the rain forest algorithm using oversampling presents the best results when predicting the possible occurrence of complications among gastric cancer patients after their in-hospital stays, with an accuracy of approximately 83%.
Adsorption processes with activated carbons can be used to remove heavy hydrocarbons from a natural gas flow. Using the Pressure Swing Adsorption (PSA) technology, one can introduce a flexible ...solution in pre-existing gas-processing units to deal with new marked demands, as for example a C
3+
free gas composition to be used as adsorbed natural gas to vehicles fuel tanks. However, designing a PSA process is a laborious task because several cycle configurations and materials are available to perform the separation. To fulfill such task, a multiscale procedure is proposed. Molecular simulation, through Grand Canonical Monte Carlo (GCMC) method, was used to obtain adsorption isotherms for natural gas components in three different carbons: WV-1050, NORIT R1 and MAXSORB. Bed geometry was set based on the minimum fluidization velocity and on the working capacity of the adsorbents. Working capacities were calculated using Langmuir Isotherm applied to Ideal Adsorbed Solution Theory (IAST) to represent the mixture. Each PSA column was simulated in Aspen Adsorption® and operates according to a four steps cycle (Skarstrom cycle): pressurization, adsorption at 40 bar, blowdown, and purge at 1 bar. The operating conditions of the process (such as flowrates, bed geometry and step times) were optimized, seeking the maximization of the process performance parameters: purity, recovery and productivity. A preliminary design of the PSA unit indicates the carbon WV1050 as the best adsorbent to produce C
3+
free gas fuel, ideal for storage by adsorption.
NaCMC is a biocompatible polymer that can be crosslinked with citric acid to form a gel matrix. Melaleuca oils have antimicrobial and anti-inflammatory properties with potential for wound healing. ...The goal of this work was to investigate the characteristics of NaCMC-Melaleuca oils gels. The gels were characterized by FTIR, TGA, mechanical analysis, and in vitro swelling and S. aureus inhibition tests. The oils were characterized using chromatography, presenting high values of (1,8 cineol/terpinen-4-ol), and evaluated for confirmation of their effect against S. aureus. The samples showed physical interactions between NaCMC, citric acid and the Melaleuca oils. Erosion in saline solution was higher in the gels with oils, attributable to interference with crosslinking. The membranes presented high contribution of relaxation mechanism and low contribution of Fickian diffusion regarding the swelling ability. The presence of the oils increased thermal stability and diminished gel fraction and mechanical properties, indicating that the oils interact with the matrix anchoring the chains. Although melaleuca oils themselves were active against S. aureus and CA was responsible for the NaCMC hydrogels activity, the incorporation of melaleuca oils in NaCMC gels was not reported previously. This report indicates that NaCMC hydrogel may be a proper matrix for essential oils incorporation.
Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. ...Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.
Epidural anesthesia in dogs is a locoregional anesthesia technique used in veterinary medicine, becoming an important integrated application in the anesthetic protocol to provide safer and more ...effective analgesia to patients. For this, professionals must adhere to rigorous guidelines and possess technical skills. In this context, in veterinary education, the development of practical clinical skills represents a crucial aspect in the training of these professionals. However, traditional teaching methods have proven insufficient to ensure a consistent level of competence among recent graduates. The introduction of non-animal alternatives for educational purposes has contributed to the development of simulation-based teaching, an innovative and accessible field capable of enhancing pre-clinical proficiency in students and reducing the use of live animals and cadavers. Despite its application in various areas of veterinary education, there are no conclusive results regarding the development of accessible simulators capable of effectively enhancing training in epidural anesthesia in dogs. Therefore, this article represents a pioneering study aimed at sharing a method for creating SimuVet, a realistic simulator for training epidural anesthesia in dogs. The simulator was fully developed by veterinary researchers with limited experience in 3D printing and, after preliminary analysis, demonstrated excellent performance and ultrasonographic anatomy. Future work will focus on the formal validation of this simulator with the aim of improving the teaching and learning process for students and experts in performing epidural anesthesia in companion animals.
Considering the great economic and environmental interests in the capture and separation of CO
2
and the wide availability of faujasites zeolites (FAU), we propose a set of parameters based on ...classical force fields that has good transferability among Na-FAU sieves and CO
2
. In addition to CO
2
, the parameterization strategy was tested for H
2
S, O
2
, N
2
and CH
4
gases. For these gases, the force field adequately predicts the adsorption isotherms at low pressure. The force field was also tested for N
2
in the FAU framework with different monovalent and divalent cations, resulting in quantitative agreement for monovalent cations and qualitative agreement for divalent cations. The good tradeoff between the reliability and ease of implementation will enable rapid evaluation of the adsorption properties of gaseous mixtures of industrial relevance. The reasoning of the re-parameterization strategy is also discussed in detail.
The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by ...discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets
Hepatic cirrhosis represents an advanced stage of fibrosis in the liver, resulting from various conditions such as hepatitis and chronic alcoholism. This article uses data from the Mayo Clinic’s ...clinical trial on primary bile cirrhosis carried out between 1974 and 1984. Rapid and accurate identification of the condition is crucial for the implementation of effective interventions, capable of mitigating liver damage and preventing complications, especially in the early stages of the disease. The focus of the research is the evaluation of Data Mining (DM) techniques, using clinical data, to predict survival outcomes in patients with cirrhosis. The study uses the CRISP-DM methodology, analysing different classification algorithms, including k-Nearest Neighbours, Random Forest, Decision Trees, Gradient Boosting and Naive Bayes. The effectiveness of the model is evaluated, highlighting the Random Forest with Holdout Sampling and a ratio of 0.7, whose performance averages around 80%. The nominal attributes were also analysed in order to discover possible patterns of association.
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