ASU Scholarship Showcase

Perchloroethylene (PCE) is a highly utilized solvent in the dry cleaning industry because of its cleaning effectiveness and relatively low cost to consumers. According to the 2006 U.S. Census, approximately 28,000 dry cleaning operations used PCE as their principal cleaning agent. Widespread use of PCE is problematic because of its adverse impacts on human health and environmental quality. As PCE use is curtailed, effective alternatives must be analyzed for their toxicity and impacts to human health and the environment. Potential alternatives to PCE in dry cleaning include dipropylene glycol n-butyl ether (DPnB) and dipropylene glycol tert-butyl ether (DPtB), both promising to pose a relatively smaller risk. To evaluate these two alternatives to PCE, we established and scored performance criteria, including chemical toxicity, employee and customer exposure levels, impacts on the general population, costs of each system, and cleaning efficacy. The scores received for PCE were 5, 5, 3, 5, 3, and 3, respectively, and DPnB and DPtB scored 3, 1, 2, 2, 4, and 4, respectively. An aggregate sum of the performance criteria yielded a favorably low score of “16” for both DPnB and DPtB compared to “24” for PCE. We conclude that DPnB and DPtB are preferable dry cleaning agents, exhibiting reduced human toxicity and a lesser adverse impact on human health and the environment compared to PCE, with comparable capital investments, and moderately higher annual operating costs.

We designed and evaluated an active sampling device, using as analytical targets a family of pesticides purported to contribute to honeybee colony collapse disorder. Simultaneous sampling of bulk water and pore water was accomplished using a low-flow, multi-channel pump to deliver water to an array of solid-phase extraction cartridges. Analytes were separated using either liquid or gas chromatography, and analysis was performed using tandem mass spectrometry (MS/MS). Achieved recoveries of fipronil and degradates in water spiked to nominal concentrations of 0.1, 1, and 10 ng/L ranged from 77 ± 12 to 110 ± 18%. Method detection limits (MDLs) were as low as 0.040–0.8 ng/L. Extraction and quantitation of total fiproles at a wastewater-receiving wetland yielded concentrations in surface water and pore water ranging from 9.9 ± 4.6 to 18.1 ± 4.6 ng/L and 9.1 ± 3.0 to 12.6 ± 2.1 ng/L, respectively. Detected concentrations were statistically indistinguishable from those determined by conventional, more laborious techniques (p > 0.2 for the three most abundant fiproles). Aside from offering time-averaged sampling capabilities for two phases simultaneously with picogram-per-liter MDLs, the novel methodology eliminates the need for water and sediment transport via in situ solid phase extraction.

The Future of Wastewater Sensing workshop is part of a collaboration between Arizona State University Center for Nanotechnology in Society in the School for the Future of Innovation in Society, the Biodesign Institute’s Center for Environmental Security, LC Nano, and the Nano-enabled Water Treatment (NEWT) Systems NSF Engineering Research Center. The Future of Wastewater Sensing workshop explores how technologies for studying, monitoring, and mining wastewater and sewage sludge might develop in the future, and what consequences may ensue for public health, law enforcement, private industry, regulations and society at large. The workshop pays particular attention to how wastewater sensing (and accompanying research, technologies, and applications) can be innovated, regulated, and used to maximize societal benefit and minimize the risk of adverse outcomes, when addressing critical social and environmental challenges.

Background: Methylmercury (MeHg) may affect fetal growth; however, prior research often lacked assessment of mercury speciation, confounders, and interactions.

Objective: Our objective was to assess the relationship between MeHg and fetal growth as well as the potential for confounding or interaction of this relationship from speciated mercury, fatty acids, selenium, and sex.

Methods: This cross-sectional study includes 271 singletons born in Baltimore, Maryland, 2004–2005. Umbilical cord blood was analyzed for speciated mercury, serum omega-3 highly unsaturated fatty acids (n-3 HUFAs), and selenium. Multivariable linear regression models controlled for gestational age, birth weight, maternal age, parity, pre-pregnancy body mass index, smoking, hypertension, diabetes, selenium, n-3 HUFAs, and inorganic mercury (IHg).

Results: Geometric mean cord blood MeHg was 0.94 μg/L (95% CI: 0.84, 1.07). In adjusted models for ponderal index, βln(MeHg) = –0.045 (g/cm[superscript 3]) × 100 (95% CI: –0.084, –0.005). There was no evidence of a MeHg × sex interaction with ponderal index. Contrastingly, there was evidence of a MeHg × n-3 HUFAs interaction with birth length [among low n-3 HUFAs, βln(MeHg) = 0.40 cm, 95% CI: –0.02, 0.81; among high n-3 HUFAs, βln(MeHg) = –0.15, 95% CI: –0.54, 0.25; p-interaction = 0.048] and head circumference [among low n-3 HUFAs, βln(MeHg) = 0.01 cm, 95% CI: –0.27, 0.29; among high n-3 HUFAs, βln(MeHg) = –0.37, 95% CI: –0.63, –0.10; p-interaction = 0.042]. The association of MeHg with birth weight and ponderal index was affected by n-3 HUFAs, selenium, and IHg. For birth weight, βln(MeHg) without these variables was –16.8 g (95% CI: –75.0, 41.3) versus –29.7 (95% CI: –93.9, 34.6) with all covariates. Corresponding values for ponderal index were –0.030 (g/cm[superscript 3]) × 100 (95% CI: –0.065, 0.005) and –0.045 (95% CI: –0.084, –0005).

Conclusion: We observed an association of increased MeHg with decreased ponderal index. There is evidence for interaction between MeHg and n-3 HUFAs; infants with higher MeHg and n-3 HUFAs had lower birth length and head circumference. These results should be verified with additional studies.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.