Extra-terrestrial construction processes - advancements, opportunities and challenges

Government space agencies, including NASA and ESA, are conducting preliminary studies on building alternative space-habitat systems for deep-space exploration. Such studies include development of advanced technologies for planetary surface exploration, including an in-depth understanding of the use of local resources. Currently, NASA plans to land humans on Mars in the 2030s. Similarly, other space agencies from Europe (ESA), Canada (CSA), Russia (Roscosmos), India (ISRO), Japan (JAXA) and China (CNSA) have already initiated or announced their plans for launching a series of lunar missions over the next decade, ranging from orbiters, landers and rovers for extended stays on the lunar surface. As the Space Odyssey is one of humanity’s oldest dreams, there has been a series of research works for establishing temporary or permanent settlement on other planetary bodies, including the Moon and Mars. This paper reviews current projects developing extra-terrestrial construction, broadly categorised as: (i) ISRU-based construction materials; (ii) fabrication methods; and (iii) construction processes. It also discusses four categories of challenges to developing an appropriate construction process: (i) lunar simulants; (ii) material fabrication and curing; (iii) microwave-sintering based fabrication; and (iv) fully autonomous and scaled-up construction processes

A PROBABILISTIC APPROACH TO DATA INTEGRATION IN BIOMEDICAL RESEARCH: THE IsBIG EXPERIMENTS

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical research has produced vast amounts of new information in the last decade but has been slow to find its use in clinical applications. Data from disparate sources such as genetic studies and summary data from published literature have been amassed, but there is a significant gap, primarily due to a lack of normative methods, in combining such information for inference and knowledge discovery. In this research using Bayesian Networks (BN), a probabilistic framework is built to address this gap. BN are a relatively new method of representing uncertain relationships among variables using probabilities and graph theory. Despite their computational complexity of inference, BN represent domain knowledge concisely. In this work, strategies using BN have been developed to incorporate a range of available information from both raw data sources and statistical and summary measures in a coherent framework. As an example of this framework, a prototype model (In-silico Bayesian Integration of GWAS or IsBIG) has been developed. IsBIG integrates summary and statistical measures from the NIH catalog of genome wide association studies (GWAS) and the database of human genome variations from the international HapMap project. IsBIG produces a map of disease to disease associations as inferred by genetic linkages in the population. Quantitative evaluation of the IsBIG model shows correlation with empiric results from our Electronic Medical Record (EMR) – The Regenstrief Medical Record System (RMRS). Only a small fraction of disease to disease associations in the population can be explained by the linking of a genetic variation to a disease association as studied in the GWAS. None the less, the model appears to have found novel associations among some diseases that are not described in the literature but are confirmed in our EMR. Thus, in conclusion, our results demonstrate the potential use of a probabilistic modeling approach for combining data from disparate sources for inference and knowledge discovery purposes in biomedical research

Design de fiabilidade bidimensional do software de múltiplos lançamentos tendo em conta o fator de redução de falhas na depuração imperfeita

Introduction: The present research was conducted at the University of Delhi, India in 2017. Methods: We develop a software reliability growth model to assess the reliability of software products released in multiple versions under limited availability of resources and time. The Fault Reduction Factor (frf) is considered to be constant in imperfect debugging environments while the rate of fault removal is given by Delayed S-Shaped model. Results: The proposed model has been validated on a real life four-release dataset by carrying out goodness of fit analysis. Laplace trend analysis was also conducted to judge the trend exhibited by data with respect to change in the system’s reliability. Conclusions: A number of comparison criteria have been calculated to evaluate the performance of the proposed model relative to only time-based multi-release Software Reliability Growth Model (srgm). Originality: In general, the number of faults removed is not the same as the number of failures experienced in given time intervals, so the inclusion of frf in the model makes it better and more realistic. A paradigm shift has been observed in software development from single release to multi release platform. Limitations: The proposed model can be used by software developers to take decisions regarding the release time for different versions, by either minimizing the development cost or maximizing the reliability and determining the warranty policies.Introducción: la presente investigación se realizó en la Universidad de Delhi, India en 2017. Métodos: desarrollamos un modelo de crecimiento de confiabilidad de software para evaluar la confiabilidad de los productos de software lanzados en múltiples versiones bajo disponibilidad limitada de recursos y tiempo. El factor de reducción de fallas (frf) se considera una constante en entornos de depuración imperfecta, mientras que la tasa de eliminación de fallas está dada por el modelo de forma retardada en S. Resultados: se valida el modelo propuesto en un conjunto de datos de cuatro lanzamientos de la vida real mediante un análisis de bondad de ajuste. También se aplicó el análisis de tendencia de Laplace para juzgar la tendencia que presentan los datos con respecto al cambio en la confiabilidad del sistema. Conclusiones: se calculó una serie de criterios de comparación para evaluar el rendimiento del modelo propuesto en relación con el modelo de crecimiento de confiabilidad del software (srgm) de múltiples lanzamientos basado únicamente en el tiempo. Originalidad: en general, el número de fallas eliminadas no es el mismo que el número de fallas experimentadas en intervalos de tiempo determinados, por lo que la inclusión de frf en el modelo lo mejora y lo hace más realista. Se ha observado un cambio de paradigma en el desarrollo de software, que pasa de un lanzamiento único a una plataforma múltiples lanzamientos. Limitaciones: los desarrolladores de software pueden emplear el modelo propuesto para tomar decisiones con respecto al tiempo de lanzar diferentes versiones, ya sea minimizando el costo de desarrollo o maximizando la confiabilidad y determinando las políticas de la garantía.Introdução: esta pesquisa foi realizada na Universidade de Deli, na Índia, em 2017. Métodos: desenvolvemos um modelo de crescimento de confiabilidade de software para avaliar a confiabilidade dos produtos de software lançados em múltiplas versões sob disponibilidade limitada de recursos e tempo. O fator de redução de falhas (frf) é considerado uma constante em contextos de depuração imperfeita, enquanto a taxa de eliminação de falhas é dada pelo modelo de forma retardada em S.Resultados: o modelo proposto é avaliado em um conjunto de dados de quatro lançamentos da vida real mediante uma análise de bondade de ajuste. Também foi utilizada a análise de tendência de Laplace para avaliar a tendência apresentada pelos dados com respeito à mudança na confiabilidade do sistema.Conclusões: uma série de critérios de comparação foi calculada para avaliar o rendimento do modelo proposto em relação com o modelo de crescimento de confiabilidade do software (srgm) de múltiplos lançamentos baseado unicamente no tempo.Originalidade: em geral, o número de falhas eliminadas não é o mesmo que o número de falhas existentes em intervalos de tempo determinados, sendo assim, a inclusão do frf no modelo o torna melhor e mais realista. Foi observada uma mudança de paradigma no desenvolvimento de software, que passa de um lançamento único a uma plataforma de múltiplos lançamentos.Limitações: o modelo proposto pode ser utilizado pelos desenvolvedores de software para tomar decisões com respeito ao tempo de lançar diferentes versões, seja para minimizar o custo de desenvolvimento ou maximizar a confiabilidade e determinar as políticas de garantia

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to ‘phenotype the population’ seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services

Secondhand smoke exposure, parental depressive symptoms and preschool behavioral outcomes

Little is known about the association of secondhand smoke (SHS) exposure and behavioral conditions among preschoolers. A cross-sectional analysis was used to examine billing and pharmacy claims from November 2004 to June 2012 linked to medical encounter-level data for 2,441 children from four pediatric community health clinics. Exposure to SHS was associated with attention deficit-hyperactivity disorder/ADHD and disruptive behavior disorder/DBD after adjusting for potential confounding factors. Assessment of exposure to SHS and parental depressive symptoms in early childhood may increase providers' ability to identify children at higher risk of behavioral issues and provide intervention at the earliest stages

Prevalence of infant television viewing and maternal depression symptoms

BACKGROUND: Early television (TV) viewing has been linked with maternal depression and has adverse health effects in children. However, it is not known how early TV viewing occurs. This study evaluated the prevalence at which parents report TV viewing for their children if asked in the first 2 years of life and whether TV viewing is associated with maternal depression symptoms. METHODS: Using a cross-sectional design, TV viewing was evaluated in children 0 to 2 years of age in 4 pediatric clinics in Indianapolis, IN, between January 2011 and April 2012. Families were screened for any parental report of depression symptoms (0-15 months) and for parental report of TV viewing (before 2 years of age) using a computerized clinical decision support system linked to the patient's electronic health record. RESULTS: There were 3254 children in the study. By parent report, 50% of children view TV by 2 months of age, 75% by 4 months of age, and 90% by 2 years of age. Complete data for both TV viewing and maternal depression symptoms were available for 2397 (74%) of children. In regression models, the odds of parental report of TV viewing increased by 27% for each additional month of child's age (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.25-1.30; p < .001). The odds of TV viewing increased by almost half with parental report of depression symptoms (OR, 1.47; CI, 1.07-2.00, p = .016). Publicly insured children had 3 times the odds of TV viewing compared to children with private insurance (OR, 3.00; CI, 1.60-5.63; p = .001). Black children had almost 4 times the odds (OR, 3.75; CI, 2.70-5.21; p < .001), and white children had one-and-a-half times the odds (OR, 1.55; CI, 1.04-2.30; p = .032) of TV viewing when compared to Latino children. CONCLUSIONS: By parental report, TV viewing occurs at a very young age in infancy, usually between 0 and 3 months and varies by insurance and race/ethnicity. Children whose parents report depression symptoms are especially at risk for early TV viewing. Like maternal depression, TV viewing poses added risks for reduced interpersonal interactions to stimulate infant development. This work suggests the need to develop early targeted developmental interventions. Children as young as 0 to 3 months are viewing TV on most days. In the study sample of 0 to 2 year olds, the odds of TV viewing increased by more than a quarter for each additional month of child's age and by as much as half when the mother screened positive for depression symptoms

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Investigating the microwave heating behaviour of lunar soil simulant JSC-1A at different input powers

For a sustainable human presence on the Moon, it is critical to develop technologies that could utilise the locally available resources (a.k.a. in situ resource utilisation or ISRU) for habitat construction. As the surface soil is one of the most widely available resources at the Moon, we have investigated the viability of microwave heating of a lunar soil simulant (JSC-1A). JSC-1A was thermally treated in a bespoke microwave apparatus using 2.45 GHz frequency, using five different microwave powers in the range of 250 W to 1000 W. The structural properties of the resulting products were analysed to determine whether their microstructures and mechanical strengths differ under different input powers; and whether input power plays a crucial role in triggering thermal runaway, for identifying the optimum power for developing a microwave-heating. Our key findings are: (i) the higher input powers (800 W and 1000 W) generate the highest yields and microstructures with the greatest mechanical strengths, at the shortest fabrication times, and (ii) thermal runaway improves the microwave heating efficiency despite the rapid increase in temperature, once it is triggered. Our findings are of key importance for developing a microwave-heating payload for future lunar ISRU demonstration missions, contributing towards 3D printing-based extra-terrestrial construction processes

Pediatric decision support using adapted Arden Syntax

BACKGROUND: Pediatric guidelines based care is often overlooked because of the constraints of a typical office visit and the sheer number of guidelines that may exist for a patient's visit. In response to this problem, in 2004 we developed a pediatric computer based clinical decision support system using Arden Syntax medical logic modules (MLM). METHODS: The Child Health Improvement through Computer Automation system (CHICA) screens patient families in the waiting room and alerts the physician in the exam room. Here we describe adaptation of Arden Syntax to support production and consumption of patient specific tailored documents for every clinical encounter in CHICA and describe the experiments that demonstrate the effectiveness of this system. RESULTS: As of this writing CHICA has served over 44,000 patients at 7 pediatric clinics in our healthcare system in the last decade and its MLMs have been fired 6182,700 times in "produce" and 5334,021 times in "consume" mode. It has run continuously for over 10 years and has been used by 755 physicians, residents, fellows, nurse practitioners, nurses and clinical staff. There are 429 MLMs implemented in CHICA, using the Arden Syntax standard. Studies of CHICA's effectiveness include several published randomized controlled trials. CONCLUSIONS: Our results show that the Arden Syntax standard provided us with an effective way to represent pediatric guidelines for use in routine care. We only required minor modifications to the standard to support our clinical workflow. Additionally, Arden Syntax implementation in CHICA facilitated the study of many pediatric guidelines in real clinical environments