ASU Scholarship Showcase

Background: Modern advances in sequencing technology have enabled the census of microbial members of many natural ecosystems. Recently, attention is increasingly being paid to the microbial residents of human-made, built ecosystems, both private (homes) and public (subways, office buildings, and hospitals). Here, we report results of the characterization of the microbial ecology of a singular built environment, the International Space Station (ISS). This ISS sampling involved the collection and microbial analysis (via 16S rRNA gene PCR) of 15 surfaces sampled by swabs onboard the ISS. This sampling was a component of Project MERCCURI (Microbial Ecology Research Combining Citizen and University Researchers on ISS). Learning more about the microbial inhabitants of the “buildings” in which we travel through space will take on increasing importance, as plans for human exploration continue, with the possibility of colonization of other planets and moons.

Results: Sterile swabs were used to sample 15 surfaces onboard the ISS. The sites sampled were designed to be analogous to samples collected for (1) the Wildlife of Our Homes project and (2) a study of cell phones and shoes that were concurrently being collected for another component of Project MERCCURI. Sequencing of the 16S rRNA genes amplified from DNA extracted from each swab was used to produce a census of the microbes present on each surface sampled. We compared the microbes found on the ISS swabs to those from both homes on Earth and data from the Human Microbiome Project.

Conclusions: While significantly different from homes on Earth and the Human Microbiome Project samples analyzed here, the microbial community composition on the ISS was more similar to home surfaces than to the human microbiome samples. The ISS surfaces are OTU-rich with 1,036–4,294 operational taxonomic units (OTUs per sample). There was no discernible biogeography of microbes on the 15 ISS surfaces, although this may be a reflection of the small sample size we were able to obtain.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Successful public transit systems increase the value of locations they serve. Capturing this location value to help fund transit is often sensible, but challenging. This article defines location value capture, and synthesizes lessons learned from six European and North American transit agencies that have experience with location value capture funding. The opportunities for and barriers to implementing location value capture fall into three categories: agency institutional authority, agency organizational mission, and public support for transit. When any of these factors is incompatible with a location value capture strategy, implementation becomes difficult. In four of the cases studied, dramatic institutional change was critical for success. In five cases, acute crisis was a catalyst for institutional change, value capture implementation, or both. Using value capture strategies to fund transit requires practitioners to both understand agency organizational constraints, and to view transit agencies as institutions that can transform in response to changing situations.

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

What's a profession without a code of ethics? Being a legitimate profession almost requires drafting a code and, at least nominally, making members follow it. Codes of ethics (henceforth “codes”) exist for a number of reasons, many of which can vary widely from profession to profession - but above all they are a form of codified self-regulation. While codes can be beneficial, it argues that when we scratch below the surface, there are many problems at their root. In terms of efficacy, codes can serve as a form of ethical window dressing, rather than effective rules for behavior. But even more that, codes can degrade the meaning behind being a good person who acts ethically for the right reasons.

As part of a larger trend across industrialized nations, European research policy discourse has placed increasing emphasis on socio-technical integration: the explicit incorporation of activities devoted to broader social aspects into scientific activities. In order to compare these high-level integration discourses against patterns at the level of resource allocation, we analyze nearly 2500 research solicitations from the three European Framework Programmes for R&D during the period 1998-2010. We identify four distinct types of integration (socio-ethical, stakeholder, socio-economic and industrial) that occur either as core or parallel components of R&D solicitations. Quantitative analysis reveals an overall trend towards increasing integration, with requests integrating industrial and socio-economic aspects substantially outnumbering those integrating socio-ethical and stakeholder aspects-by a 2 to 1 margin. Meanwhile, calls for socio-technical integration have become slightly more extensive (ranging across a broader range of research areas addressed), significantly more pervasive (shifting from the periphery to the core of R&D practices), and arguably less diverse (involving a wider variety of integration types) over time. The relative lack of attention to socio-ethical aspects and stakeholder participation in European research is particularly notable given that we focus on potentially controversial areas (life sciences, energy, and nanotechnology), which likely overemphasizes the prevalence of integration throughout the Framework Programmes.

The similarity attraction hypothesis posits that humans are drawn toward others who behave and appear similar to themselves. Two experiments examined this hypothesis with middle-school students learning electrical circuit analysis in a computer-based environment with an Animated Pedagogical Agent (APA). Experiment 1 was designed to determine whether matching the gender of the APA to the student has a positive impact on learning outcomes or student perceptions. One hundred ninety-seven middle-school students learned with the computer-based environment using an APA that matched their gender or one which was opposite in gender. Female students reported higher program ratings when the APA matched their gender. Male students, on the other hand, reported higher program ratings than females when the APA did not match their gender. Experiment 2 systematically tested the impact of providing learners the choice among four APAs on learning outcomes and student perceptions. Three hundred thirty-four middle-school students received either a pre-assigned random APA or were free to choose from four APA options: young male agent, older male agent, young female agent, or older female agent. Learners had higher far transfer scores when provided a choice of animated agent, but student perceptions were not impacted by having the ability to make this choice. We suggest that offering students learner control positively impacts student motivation and learning by increasing student perceptions of autonomy, responsibility for the success of the instructional materials, and global satisfaction with the design of materials.

Recent advances in nonequilibrium statistical physics have provided unprecedented insight into the thermodynamics of dynamic processes. The author recently used these advances to extend Landauer’s semi-formal reasoning concerning the thermodynamics of bit erasure, to derive the minimal free energy required to implement an arbitrary computation. Here, I extend this analysis, deriving the minimal free energy required by an organism to run a given (stochastic) map π from its sensor inputs to its actuator outputs. I use this result to calculate the input-output map π of an organism that optimally trades off the free energy needed to run π with the phenotypic fitness that results from implementing π. I end with a general discussion of the limits imposed on the rate of the terrestrial biosphere’s information processing by the flux of sunlight on the Earth.

We find that the flow of attention on the Web forms a directed, tree-like structure implying the time-sensitive browsing behavior of users. Using the data of a news sharing website, we construct clickstream networks in which nodes are news stories and edges represent the consecutive clicks between two stories. To identify the flow direction of clickstreams, we define the “flow distance” of nodes (L_i), which measures the average number of steps a random walker takes to reach the ith node. It is observed that L_i is related with the clicks (C_i) to news stories and the age (T_i) of stories. Putting these three variables together help us understand the rise and decay of news stories from a network perspective. We also find that the studied clickstream networks preserve a stable structure over time, leading to the scaling between users and clicks. The universal scaling behavior is confirmed by the 1,000 Web forums. We suggest that the tree-like, stable structure of clickstream networks reveals the time-sensitive preference of users in online browsing. To test our assumption, we discuss three models on individual browsing behavior, and compare the simulation results with empirical data.