Aplicação de Al-PILC na adsorção de Cu2+, Ni2+ e Co2+ utilizando modelos físico-químicos de adsorção

Amostra de esmectita pertencente a Serra de Maicuru (Estado do Pará, Norte do Brasil, região amazônica) foi pilarizada com Al₁₃, A Argila pilarizada com alumínio (Al-PILC) foi caracterizada por DRX, MEV e EDS. Para a análise textural foram utilizadas isotermas de adsorção-desorção utilizando o nitrogênio. Este artigo é dirigido ao estudo da adsorção de metais pesados. A adsorção dos íons de Cu²⁺, Ni²⁺e Co²⁺ foi realizadas com a matriz Al-PILC em temperatura ambiente com soluções aquosas contendo os íons metálicos. Os modelos de adsorção adotados foram os de Langmuir, Freundlich e Temkin que foram aplicados aos valores obtidos experimentalmente com regressão linear. A equação de Langmuir foi o melhor modelo de linearização com r = 0,999. A equação de Freundlich apresentou limitações em altas concentrações, mas foram obtidos valores (K_f e n) bastante aceitáveis utilizando este modelo. Os parâmetros foram utilizados para calcular a quantidade de N_f em função de Cs.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Síntese de zeólitas e argilas ácidas pilarizadas a partir de matérias primas naturais

Structural changes in waste for zeolites synthesis are subject of many studies carried out in the synthesis of molecular sieves. These materials are named molecular sieves because they have well defined pore sizes and they have the capacity of select molecules by its size. In this work, it was studied the synthesis processes of two types of molecular sieves: pillared acid clays using as starting material one natural montmorillonite clay and the synthesis of zeolites from a silico-aluminous residue. This residue is a byproduct of the extraction of lithium -spodumene. The preparation of pillared acid clays was performed in two steps: 1° acid treatment of clay samples (time and temperature studies) and 2°pilarization of them with Al13 (Keggin ion). The temperature and acid concentration affect the removal of cations in the structure and porosity of the material obtained. The analysis of X-ray diffraction (XRD) and infrared spectroscopy (IR), showed that increasing the severity of the acid treatment compromises the structural material. Also the pore size distribution is approximately uniform. Despite presenting a structural disorganization, the samples were pillared. As evidenced by XRD increasing the basal spacing, specific area and uniform porosity by adsorption of N2. Regarding the microporous molecular sieves were synthesized zeolites A and NaP1 from a silico-aluminous residue, a byproduct of extracting lithium. The temperature and time of agitation during the synthesis were the most important factors for obtaining zeolite A. The aging of the gel and the highest crystallization time promoted the formation of zeolite NaP1 using a Si / Al ratio = 3.2

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^