Optimal design and operation of compact simulated moving bed processes for enantioseparations

Simulated moving bed (SMB) chromatography is attracting more and more attention since it is a powerful technique for complex separation tasks. Nowadays, more than 60% of preparative SMB units are installed in the pharmaceutical and in the food in- dustry [SDI, Preparative and Process Liquid Chromatography: The Future of Process Separations, International Strategic Directions, Los Angeles, USA, 2002. http://www. strategicdirections.com]. Chromatography is the method of choice in these ¯elds, be- cause often pharmaceuticals and ¯ne-chemicals have physico-chemical properties which di®er little from those of the by-products, and they may be thermally instable. In these cases, standard separation techniques as distillation and extraction are not applicable. The noteworthiness of preparative chromatography, particulary SMB process, as a sep- aration and puri¯cation process in the above mentioned industries has been increasing, due to its °exibility, energy e±ciency and higher product purity performance. Consequently, a new SMB paradigm is requested by the large number of potential small- scale applications of the SMB technology, which exploits the °exibility and versatility of the technology. In this new SMB paradigm, a number of possibilities for improving SMB performance through variation of parameters during a switching interval, are pushing the trend toward the use of units with smaller number of columns because less stationary phase is used and the setup is more economical. This is especially important for the phar- maceutical industry, where SMBs are seen as multipurpose units that can be applied to di®erent separations in all stages of the drug-development cycle. In order to reduce the experimental e®ort and accordingly the coast associated with the development of separation processes, simulation models are intensively used. One impor- tant aspect in this context refers to the determination of the adsorption isotherms in SMB chromatography, where separations are usually carried out under strongly nonlinear conditions in order to achieve higher productivities. The accurate determination of the competitive adsorption equilibrium of the enantiomeric species is thus of fundamental importance to allow computer-assisted optimization or process scale-up. Two major SMB operating problems are apparent at production scale: the assessment of product quality and the maintenance of long-term stable and controlled operation. Constraints regarding product purity, dictated by pharmaceutical and food regulatory organizations, have drastically increased the demand for product quality control. The strict imposed regulations are increasing the need for developing optically pure drugs.(...)

Estudo do controlo motor via Sincronias entre sinais de EEG e de EMG / Motor control study through sincrony between EEG and EMG signals

Um dos maiores desafios da neurofisiologia é o de compreender a forma como a informação é transmitida através do sistema nervoso. O estudo do sistema nervoso tem várias aplicações, tanto na neurologia, permitindo avanços ao nível clínico, como noutras áreas, e.g., nos sistemas de processamento de informação baseados em redes neuronais. A transmissão de informação entre neurónios é feita por via de sinais elétricos. A compreensão deste fenómeno é ainda incompleta e há projectos a nível europeu e mundial com o objetivo de modular o sistema nervoso no seu todo de forma a melhor o compreender. Uma das teses que se desenvolve hoje em dia é a de que a transmissão de sinais elétricos no sistema nervoso é influenciada por fenómenos de sincronia. O objetivo desta dissertação é o de otimizar um protocolo de aquisição e análise de dados reais de eletroencefalograma e eletromiograma com o propósito de observar fenómenos de sincronia, baseando-se num algoritmo (análise por referência de fase, ou RPA, do inglês reference phase analysis) que deteta sincronias de fase entre os sinais de eletroencefalograma (EEG) e um sinal de referência, que é, no caso presente, o eletromiograma (EMG). A otimização deste protocolo e sua validação indicaram a existência de fenómenos significativos de sincronia no sinal elétrico, transmitido entre os músculos da mão e o córtex motor, no decorrer da ação motora.

Long term (five-year) survival following radical surgical treatment plus adjuvant chemotherapy (FAM) in advanced gastric cancer: a controlled study

Several drugs and their associations are being used for adjuvant or complementary chemotherapy with the aim of improving results of gastric cancer treatment. The objective of this study was to verify the impact of these drugs on nutrition and on survival rate after radical treatment of 53 patients with gastric cancer in stage III of the TNM classification. A control group including 28 patients who had only undergone radical resection was compared to a group of 25 patients who underwent the same operative technique followed by adjuvant polychemotherapy with FAM (5-fluorouracil, Adriamycin, and mitomycin C). In this latter group, chemotherapy toxicity in relation to hepatic, renal, cardiologic, neurological, hematologic, gastrointestinal, and dermatological functions was also studied. There was no significant difference on admission between both groups in relation to gender, race, macroscopic tumoral type of tumor according to the Borrmann classification, location of the tumor in the stomach, length of the gastric resection, or response to cutaneous tests on delayed sensitivity. Chemotherapy was started on average, 2.3 months following surgical treatment. Clinical and laboratory follow-up of all patients continued for 5 years. The following conclusions were reached: 1) The nutritional status and incidence of gastrointestinal manifestation were similar in both groups; 2) There was no occurrence of cardiac, renal, neurological, or hepatic toxicity or death due to the chemotherapeutic method per se; 3) Dermatological alterations and hematological toxicity occurred exclusively in patients who underwent polychemotherapy; 4) There was no significant difference between the rate and site of tumoral recurrence, the disease-free interval, or the survival rate of both study groups; 5) Therefore, we concluded, after a 5-year follow-up, chemotherapy with the FAM regimen did not increase the survival rate.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Dispersion and re-agglomeration of graphite nanoplates in polypropylene melts under controlled flow conditions

The kinetics of GnP dispersion in polypropylene melt was studied using a prototype small scale modular extensional mixer. Its modular nature enabled the sequential application of a mixing step, melt relaxation, and a second mixing step. The latter could reproduce the flow conditions on the first mixing step, or generate milder flow conditions. The effect of these sequences of flow constraints upon GnP dispersion along the mixer length was studied for composites with 2 and 10 wt.% GnP. The samples collected along the first mixing zone showed a gradual decrease of number and size of GnP agglomerates, at a rate that was independent of the flow conditions imposed to the melt, but dependent on composition. The relaxation zone induced GnP re-agglomeration, and the application of a second mixing step caused variable dispersion results that were largely dependent on the hydrodynamic stresses generated.

Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

The quantified-Self and wearable technologies in the workplace: implications and challenges for their implementations

Introduction of technologies in the workplace have led to a dramatic change. These changes have come with an increased capacity to gather data about one’s working performance (i.e. productivity), as well as the capacity to track one’s personal responses (i.e. emotional, physiological, etc.) to this changing workplace environment. This movement of self-monitoring or self-sensing using diverse types of wearable sensors combined with the use of computing has been identified as the Quantified-Self. Miniaturization of sensors, reduction in cost and a non-stop increase in the computer power capacity has led to a panacea of wearables and sensors to track and analyze all types of information. Utilized in the personal sphere to track information, a looming question remains, should employers use the information from the Quantified-Self to track their employees’ performance or well-being in the workplace and will this benefit employees? The aim of the present work is to layout the implications and challenges associated with the use of Quantified-Self information in the workplace. The Quantified-Self movement has enabled people to understand their personal life better by tracking multiple information and signals; such an approach could allow companies to gather knowledge on what drives productivity for their business and/or well-being of their employees. A discussion about the implications of this approach will cover 1) Monitoring health and well-being, 2) Oversight and safety, and 3) Mentoring and training. Challenges will address the question of 1) Privacy and Acceptability, 2) Scalability and 3) Creativity. Even though many questions remain regarding their use in the workplace, wearable technologies and Quantified-Self data in the workplace represent an exciting opportunity for the industry and health and safety practitioners who will be using them.

Predictors of dropout in a controlled clinical trial of psychotherapy for moderate depression

A significant number of psychotherapy clients remain untreated, and dropping out is one of the main reasons. Still, the literature around this subject is incoherent. The present study explores potential pre-treatment predictors of dropout in a sample of clients who took part in a clinical trial designed to test the efficacy of narrative therapy for major depressive disorder compared to cognitive-behavioral therapy. Logistic regression analysis showed that: (1) treatment assignment did not predict dropout, (2) clients taking psychiatric medication at intake were 80% less likely to drop out from therapy, compared to clients who were not taking medication, and (3) clients presenting anxious comorbidity at intake were 82% less likely to dropout compared to those clients not presenting anxious comorbidity. Results suggest that clinicians should pay attention to depressed clients who are not taking psychiatric medication or have no comorbid anxiety. More research is needed in order to understand this relationship.

Light-controlled resistive switching in laser-assisted annealed Ba0.8Sr0.2TiO3 thin films

In this work, Ba0.8Sr0.2TiO3 (BST)/ITO structures were grown on glass substrate and laser assisted annealing (LAA) was performed to promote the crystallization of BST. Atomic force microscopy and X-ray diffraction studies confirm the crack free and polycrystalline perovskite phase of BST. White light controlled resistive switching (RS) effect in Au/BST/ITO device is investigated. The device displays the electroforming-free bipolar RS characteristics and are explained by the modulationof the width and height of barrier at the BST/ITO interface via ferroelectric polarization. Moreover, the RS effect is signifi- cantly improved under white light illumination compared to that in the dark. The enhanced RS and photovoltaic effects are explained by considering depolarization field and charge distribution at the interface. The devices exhibit stable retention characteristics with low currents (mA), which make them attractive for non volatile memory devices.

Cognitive stimulation for portuguese older adults with cognitive impairment: a randomized controlled trial of efficacy, comparative duration, feasibility and experiential relevance

Although some studies point to cognitive stimulation as a beneficial therapy for older adults with cognitive impairments, this area of research and practice is still lacking dissemination and is underrepresented in many countries. Moreover, the comparative effects of different intervention durations remain to be established and, besides cognitive effects, pragmatic parameters, such as cost-effectiveness and experiential relevance to participants, are seldom explored. In this work, we present a randomized con- trolled wait-list trial evaluating 2 different intervention durations (standard 1⁄4 17 vs brief 1⁄4 11 sessions) of a cognitive stimulation program developed for older adults with cognitive impairments with or without dementia. 20 participants were randomly assigned to the standard duration intervention program (17 sessions, 1.5 months) or to a wait-list group. At postintervention of the standard intervention group, the wait-list group crossed over to receive the brief intervention program (11 sessions, 1 month). Changes in neuropsychological, functionality, quality of life, and caregiver outcomes were evaluated. Experience during intervention and costs and feasibility were also evaluated. The current cognitive stimulation programs (ie, standard and brief) showed high values of experiential relevance for both intervention durations. High adherence, completion rates, and reasonable costs were found for both formats. Further studies are needed to definitively establish the potential efficacy, optimal duration, cost-effectiveness, and experiential relevance for participants of cognitive intervention approaches.