ASU Scholarship Showcase

A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimation. The proposed method is applied to model commute mode choice among four alternatives (auto, transit, bicycle and walk) using travel behavior data from Argau, Switzerland. Comparisons between the multinomial logit model and the proposed semi-nonparametric model show that violations of the standard Gumbel distribution assumption lead to considerable inconsistency in parameter estimates and model inferences.

A general consensus on the concept of rainfall intermittency has not yet been reached, and intermittency is often attributed to different aspects of rainfall variability, including the fragmentation of the rainfall support (i.e., the alternation of wet and dry intervals) and the strength of intensity fluctuations and bursts. To explore these different aspects, a systematic analysis of rainfall intermittency properties in the time domain is presented using high-resolution (1-min) data recorded by a network of 201 tipping-bucket gauges covering the entire island of Sardinia (Italy). Four techniques, including spectral and scale invariance analysis, and computation of clustering and intermittency exponents, are applied to quantify the contribution of the alternation of dry and wet intervals (i.e., the rainfall support fragmentation), and the fluctuations of intensity amplitudes, to the overall intermittency of the rainfall process. The presence of three ranges of scaling regimes between 1 min to ~ 45 days is first demonstrated. In accordance with past studies, these regimes can be associated with a range dominated by single storms, a regime typical of frontal systems, and a transition zone.

The positions of the breaking points separating these regimes change with the applied technique, suggesting that different tools explain different aspects of rainfall variability. Results indicate that the intermittency properties of rainfall support are fairly similar across the island, while metrics related to rainfall intensity fluctuations are characterized by significant spatial variability, implying that the local climate has a significant effect on the amplitude of rainfall fluctuations and minimal influence on the process of rainfall occurrence. In addition, for each analysis tool, evidence is shown of spatial patterns of the scaling exponents computed in the range of frontal systems. These patterns resemble the main pluviometric regimes observed on the island and, thus, can be associated with the corresponding synoptic circulation patterns. Last but not least, we demonstrate how the methodology adopted to sample the rainfall signal from the records of the tipping instants can significantly affect the intermittency analysis, especially at smaller scales. The multifractal scale invariance analysis is the only tool that is insensitive to the sampling approach. Results of this work may be useful to improve the calibration of stochastic algorithms used to downscale coarse rainfall predictions of climate and weather forecasting models, as well as the parameterization of intensity-duration-frequency curves, adopted for land planning and design of civil infrastructures.

Delays are a major cause for concern in the construction industry in Saudi Arabia. This paper identifies the main causes of delay in infrastructure projects in Mecca, Saudi Arabia, and compares these with projects around the country and other Gulf countries. Data was obtained from 49 infrastructure projects undertaken by the owner and were analyzed quantitatively to understand the severity and causes of delay. 10 risk factors were identified and were grouped into four categories. Average delay in infrastructure projects in Mecca was found to be 39%. The most severe cause of delay was found to be the land acquisition factor. This highlights the critical land ownership and acquisition issues that are prevailing in the city. Additionally, other factors that contribute to delay include contractors’ lack of expertise, re-designing, and haphazard underground utilities (line services). It is concluded that the majority of project delays were caused from the owner's side as compared to contractors, consultants, and other project's stakeholders. This finding matched with the research findings of the Gulf Countries Construction (GCC) Industry's literature. This study fills an important practice and research gap for improving the efficiency in delivering infrastructure projects in the holy city of Mecca and Gulf countries at large.

There are an increasing variety of applications in which peptides are both synthesized and used attached to solid surfaces. This has created a need for high throughput sequence analysis directly on surfaces. However, common sequencing approaches that can be adapted to surface bound peptides lack the throughput often needed in library-based applications. Here we describe a simple approach for sequence analysis directly on solid surfaces that is both high speed and high throughput, utilizing equipment available in most protein analysis facilities. In this approach, surface bound peptides, selectively labeled at their N-termini with a positive charge-bearing group, are subjected to controlled degradation in ammonia gas, resulting in a set of fragments differing by a single amino acid that remain spatially confined on the surface they were bound to. These fragments can then be analyzed by MALDI mass spectrometry, and the peptide sequences read directly from the resulting spectra.

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

The human hand comprises complex sensorimotor functions that can be impaired by neurological diseases and traumatic injuries. Effective rehabilitation can bring the impaired hand back to a functional state because of the plasticity of the central nervous system to relearn and remodel the lost synapses in the brain. Current rehabilitation therapies focus on strengthening motor skills, such as grasping, employ multiple objects of varying stiffness so that affected persons can experience a wide range of strength training. These devices have limited range of stiffness due to the rigid mechanisms employed in their variable stiffness actuators. This paper presents a novel soft robotic haptic device for neuromuscular rehabilitation of the hand, which is designed to offer adjustable stiffness and can be utilized in both clinical and home settings. The device eliminates the need for multiple objects by employing a pneumatic soft structure made with highly compliant materials that act as the actuator of the haptic interface. It is made with interchangeable sleeves that can be customized to include materials of varying stiffness to increase the upper limit of the stiffness range. The device is fabricated using existing 3D printing technologies, and polymer molding and casting techniques, thus keeping the cost low and throughput high. The haptic interface is linked to either an open-loop system that allows for an increased pressure during usage or closed-loop system that provides pressure regulation in accordance to the stiffness the user specifies. Preliminary evaluation is performed to characterize the effective controllable region of variance in stiffness. It was found that the region of controllable stiffness was between points 3 and 7, where the stiffness appeared to plateau with each increase in pressure. The two control systems are tested to derive relationships between internal pressure, grasping force exertion on the surface, and displacement using multiple probing points on the haptic device. Additional quantitative evaluation is performed with study participants and juxtaposed to a qualitative analysis to ensure adequate perception in compliance variance. The qualitative evaluation showed that greater than 60% of the trials resulted in the correct perception of stiffness in the haptic device.

Antigen-antibody complexes are central players in an effective immune response. However, finding those interactions relevant to a particular disease state can be arduous. Nonetheless many paths to discovery have been explored since deciphering these interactions can greatly facilitate the development of new diagnostics, therapeutics, and vaccines. In silico B cell epitope mapping approaches have been widely pursued, though success has not been consistent. Antibody mixtures in immune sera have been used as handles for biologically relevant antigens, but these and other experimental approaches have proven resource intensive and time consuming. In addition, these methods are often tailored to individual diseases or a specific proteome, rather than providing a universal platform. Most of these methods are not able to identify the specific antibody’s epitopes from unknown antigens, such as un-annotated neo antigens in cancer. Alternatively, a peptide library comprised of sequences unrestricted by naturally-found protein space provides for a universal search for mimotopes of an antibody’s epitope. Here we present the utility of such a non-natural random sequence library of 10,000 peptides physically addressed on a microarray for mimotope discovery without sequence information of the specific antigen. The peptide arrays were probed with serum from an antigen-immunized rabbit, or alternatively probed with serum pre-absorbed with the same immunizing antigen. With this positive and negative screening scheme, we identified the library-peptides as the mimotopes of the antigen. The unique library peptides were successfully used to isolate antigen-specific antibodies from complete immune serum. Sequence analysis of these peptides revealed the epitopes in the immunized antigen. We present this method as an inexpensive, efficient method for identifying mimotopes of any antibody’s targets. These mimotopes should be useful in defining both components of the antigen-antibody complex.

Immunosignaturing shows promise as a general approach to diagnosis. It has been shown to detect immunological signs of infection early during the course of disease and to distinguish Alzheimer’s disease from healthy controls. Here we test whether immunosignatures correspond to clinical classifications of disease using samples from people with brain tumors. Blood samples from patients undergoing craniotomies for therapeutically naïve brain tumors with diagnoses of astrocytoma (23 samples), Glioblastoma multiforme (22 samples), mixed oligodendroglioma/astrocytoma (16 samples), oligodendroglioma (18 samples), and 34 otherwise healthy controls were tested by immunosignature. Because samples were taken prior to adjuvant therapy, they are unlikely to be perturbed by non-cancer related affects. The immunosignaturing platform distinguished not only brain cancer from controls, but also pathologically important features about the tumor including type, grade, and the presence or absence of O⁶-methyl-guanine-DNA methyltransferase methylation promoter (MGMT), an important biomarker that predicts response to temozolomide in Glioblastoma multiformae patients.

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM_2.5 index. The results demonstrate that the image analysis method provides good prediction of PM_2.5 indexes, and different features have different significance levels in the prediction.