Structural Health Monitoring Inside Concrete and Grout Using the Wireless Identification and Sensing Platform (WISP)

This research investigates the implementation of battery-less RFID sensing platforms inside lossy media, such as, concrete and grout. Both concrete and novel grouts can be used for nuclear plant decommissioning as part of the U.S. Department of Energy’s (DOE’s) cleanup projects. Our research examines the following: (1) material characterization, (2) analytical modeling of transmission and propagation losses inside lossy media, (3) maximum operational range of RFID wireless sensors embedded inside concrete and grout, and (4) best positioning of antennas for achieving longer communication range between RFID antennas and wireless sensors. Our research uses the battery-less Wireless Identification and Sensing Platform (WISP) which can be used to monitor temperature, and humidity inside complex materials. By using a commercial Agilent open-ended coaxial probe (HP8570B), the measurements of the dielectric permittivity of concrete and grout are performed. Subsequently, the measured complex permittivity is used to formulate analytical Debye models. Also, the transmission and propagation losses of a uniform plane wave inside grout are calculated. Our results show that wireless sensors will perform better in concrete than grout. In addition, the maximum axial and radial ranges for WISP are experimentally determined. Our work illustrates the feasibility of battery-less wireless sensors that are embedded inside concrete and grout. Also, our work provides information that can be used to optimize the power management, sampling rate, and antenna design of such sensors.

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].^

The Role of Existential Beliefs Within the Relation of Centenarians’ Health and Well-Being

The purpose of this study was to explore the role of existential beliefs in mediating the influence of health on centenarians' well-being. A total of 80 centenarians (mean age 101.1; SD = 1.3; 81.3 % women) with no/minor cognitive impairment were included. The OARS questionnaire for diseases and functional capacity (ADL, IADL), the Satisfaction with Life Scale, and the existential beliefs subscale were used for data collection. The findings suggest that existential resources are a crucial element for mitigating the impact of health constraints in subjective well-being in this population. Appropriate models of intervention for very old age that recognize the importance of religion, spirituality, and meaning of life are to be considered.

Do health care providers use online patient ratings to improve the quality of care?

Background: Physician-rating websites have become a popular tool to create more transparency about the quality of health care providers. So far, it remains unknown whether online-based rating websites have the potential to contribute to a better standard of care. Objective: Our goal was to examine which health care providers use online rating websites and for what purposes, and whether health care providers use online patient ratings to improve patient care. Methods: We conducted an online-based cross-sectional study by surveying 2360 physicians and other health care providers (September 2015). In addition to descriptive statistics, we performed multilevel logistic regression models to ascertain the effects of providers' demographics as well as report card-related variables on the likelihood that providers implement measures to improve patient care. Results: Overall, more than half of the responding providers surveyed (54.66%, 1290/2360) used online ratings to derive measures to improve patient care (implemented measures: mean 3.06, SD 2.29). Ophthalmologists (68%, 40/59) and gynecologists (65.4%, 123/188) were most likely to implement any measures. The most widely implemented quality measures were related to communication with patients (28.77%, 679/2360), the appointment scheduling process (23.60%, 557/2360), and office workflow (21.23%, 501/2360). Scaled-survey results had a greater impact on deriving measures than narrative comments. Multilevel logistic regression models revealed medical specialty, the frequency of report card use, and the appraisal of the trustworthiness of scaled-survey ratings to be significantly associated predictors for implementing measures to improve patient care because of online ratings. Conclusions: Our results suggest that online ratings displayed on physician-rating websites have an impact on patient care. Despite the limitations of our study and unintended consequences of physician-rating websites, they still may have the potential to improve patient care.

MEF2C: expression, regulation and interaction with target genes in health and diseases

Tese de doutoramento, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Endemic zoonoses: a one health approach

Endemic zoonotic diseases remain a serious but poorly recognised problem in affected communities in developing countries. Despite the overall burden of zoonoses on human and animal health, information about their impacts in endemic settings is lacking and most of these diseases are continuously being neglected. The non-specific clinical presentation of these diseases has been identified as a major challenge in their identification (even with good laboratory diagnosis), and control. The signs and symptoms in animals and humans respectively, are easily confused with other non-zoonotic diseases, leading to widespread misdiagnosis in areas where diagnostic capacity is limited. The communities that are mostly affected by these diseases live in close proximity with their animals which they depend on for livelihood, which further complicates the understanding of the epidemiology of zoonoses. This thesis reviewed the pattern of reporting of zoonotic pathogens that cause febrile illness in malaria endemic countries, and evaluates the recognition of animal associations among other risk factors in the transmission and management of zoonoses. The findings of the review chapter were further investigated through a laboratory study of risk factors for bovine leptospirosis, and exposure patterns of livestock coxiellosis in the subsequent chapters. A review was undertaken on 840 articles that were part of a bigger review of zoonotic pathogens that cause human fever. The review process involves three main steps: filtering and reference classification, identification of abstracts that describe risk factors, and data extraction and summary analysis of data. Abstracts of the 840 references were transferred into a Microsoft excel spread sheet, where several subsets of abstracts were generated using excel filters and text searches to classify the content of each abstract. Data was then extracted and summarised to describe geographical patterns of the pathogens reported, and determine the frequency animal related risk factors were considered among studies that investigated risk factors for zoonotic pathogen transmission. Subsequently, a seroprevalence study of bovine leptospirosis in northern Tanzania was undertaken in the second chapter of this thesis. The study involved screening of serum samples, which were obtained from an abattoir survey and cross-sectional study (Bacterial Zoonoses Project), for antibodies against Leptospira serovar Hardjo. The data were analysed using generalised linear mixed models (GLMMs), to identify risk factors for cattle infection. The final chapter was the analysis of Q fever data, which were also obtained from the Bacterial Zoonoses Project, to determine exposure patterns across livestock species using generalized linear mixed models (GLMMs). Leptospira spp. (10.8%, 90/840) and Rickettsia spp. (10.7%, 86/840) were identified as the most frequently reported zoonotic pathogens that cause febrile illness, while Rabies virus (0.4%, 3/840) and Francisella spp. (0.1%, 1/840) were least reported, across malaria endemic countries. The majority of the pathogens were reported in Asia, and the frequency of reporting seems to be higher in areas where outbreaks are mostly reported. It was also observed that animal related risk factors are not often considered among other risk factors for zoonotic pathogens that cause human fever in malaria endemic countries. The seroprevalence study indicated that Leptospira serovar Hardjo is widespread in cattle population in northern Tanzania, and animal husbandry systems and age are the two most important risk factors that influence seroprevalence. Cattle in the pastoral systems and adult cattle were significantly more likely to be seropositive compared to non-pastoral and young animals respectively, while there was no significant effect of cattle breed or sex. Exposure patterns of Coxiella burnetii appear different for each livestock species. While most risk factors were identified for goats (such as animal husbandry systems, age and sex) and sheep (animal husbandry systems and sex), there were none for cattle. In addition, there was no evidence of a significant influence of mixed livestock-keeping on animal coxiellosis. Zoonotic agents that cause human fever are common in developing countries. The role of animals in the transmission of zoonotic pathogens that cause febrile illness is not fully recognised and appreciated. Since Leptospira spp. and C. burnetii are among the most frequently reported pathogens that cause human fever across malaria endemic countries, and are also prevalent in livestock population, control and preventive measures that recognise animals as source of infection would be very important especially in livestock-keeping communities where people live in close proximity with their animals.

Quinn’s Leadership Roles: A Confirmatory Factor Analysis Study in Portuguese Health Services

Purpose: To evaluate psychometric properties of Quinn’s leadership questionnaire (CFV questionnaire; 1988) to the Portuguese health services. Design: Cross-sectional study, using the Quinn’s leadership questionnaire, administered to registered nurses and physicians in Portuguese health care services (N = 687). Method: Self-administered survey applied to two samples. In the first (of convenience; N = 249 Portuguese health professionals) were performed exploratory factor and reliability analysis to the CFV questionnaire. In the second sample (stratified; N = 50 surgical units of 33 Portuguese hospitals) was performed confirmatory factor analysis using LISREL 8.80. Findings: The first sample supported an eight-factor solution accounting for 65.46% of the variance, in an interpretable factorial structure (loadings> .50), with Cronbach’s α upper than .79. This factorial structure, replicated with the second sample, showed reasonable fit for each of the 8 leadership roles, quadrants, and global model. The models evidenced, generally, nomological validity, with scores between good and acceptable (.235 < x2/df < 2.055 e .00 < RMSEA < .077). Conclusions: Quinn’s leadership questionnaire presented good reliability and validity for the eight leadership roles, showing to be suitable for use in hospital health care context. Key-Words: Leadership; Quinn’s CVF questionnaire; health services; Quinn’s competing values.

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.