ASU Scholarship Showcase

In previous work from this laboratory, it has been found that the urban heat island intensity (UHI) can be scaled with the urban length scale and the wind speed, through the time-dependent energy balance. The heating of the urban surfaces during the daytime sets the initial temperature, and this overheating is dissipated during the night-time through mean convection motion over the urban surface. This may appear to be in contrast to the classical work by Oke (1973). However, in this work, we show that if the population density is used in converting the population data into urbanized area, then a good agreement with the current theory is found. An additional parameter is the “urban flow parameter,” which depends on the urban building characteristics and affects the horizontal convection of heat due to wind. This scaling can be used to estimate the UHI intensity in any cities and therefore predict the required energy consumption during summer months. In addition, all urbanized surfaces are expected to exhibit this scaling, so that increase in the surface temperature in large energy-consumption or energy-producing facilities (e.g., solar electric or thermal power plants) can be estimated.

Owing to the suprasegmental behavior of emotional speech, turn-level features have demonstrated a better success than frame-level features for recognition-related tasks. Conventionally, such features are obtained via a brute-force collection of statistics over frames, thereby losing important local information in the process which affects the performance. To overcome these limitations, a novel feature extraction approach using latent topic models (LTMs) is presented in this study. Speech is assumed to comprise of a mixture of emotion-specific topics, where the latter capture emotionally salient information from the co-occurrences of frame-level acoustic features and yield better descriptors. Specifically, a supervised replicated softmax model (sRSM), based on restricted Boltzmann machines and distributed representations, is proposed to learn naturally discriminative topics. The proposed features are evaluated for the recognition of categorical or continuous emotional attributes via within and cross-corpus experiments conducted over acted and spontaneous expressions. In a within-corpus scenario, sRSM outperforms competing LTMs, while obtaining a significant improvement of 16.75% over popular statistics-based turn-level features for valence-based classification, which is considered to be a difficult task using only speech. Further analyses with respect to the turn duration show that the improvement is even more significant, 35%, on longer turns (>6 s), which is highly desirable for current turn-based practices. In a cross-corpus scenario, two novel adaptation-based approaches, instance selection, and weight regularization are proposed to reduce the inherent bias due to varying annotation procedures and cultural perceptions across databases. Experimental results indicate a natural, yet less severe, deterioration in performance - only 2.6% and 2.7%, thereby highlighting the generalization ability of the proposed features.

Alkali-activated slag concretes are being extensively researched because of its potential sustainability-related benefits. For such concretes to be implemented in large scale concrete applications such as infrastructural and building elements, it is essential to understand its early and long-term performance characteristics vis-à-vis conventional ordinary portland cement (OPC) based concretes. This paper presents a comprehensive study of the property and performance features including early-age isothermal calorimetric response, compressive strength development with time, microstructural features such as the pore volume and representative pore size, and accelerated chloride transport resistance of OPC and alkali-activated binder systems. Slag mixtures activated using sodium silicate solution (SiO₂-to-Na₂O ratio or M_s of 1–2) to provide a total alkalinity of 0.05 (Na₂O-to-binder ratio) are compared with OPC mixtures with and without partial cement replacement with Class F fly ash (20 % by mass) or silica fume (6 % by mass). Major similarities are noted between these binder systems for: (1) calorimetric response with respect to the presence of features even though the locations and peaks vary based on M_s, (2) compressive strength and its development, (3) total porosity and pore size, and (4) rapid chloride permeability and non-steady state migration coefficients. Moreover, electrical impedance based circuit models are used to bring out the microstructural features (resistance of the connected pores, and capacitances of the solid phase and pore-solid interface) that are similar in conventional OPC and alkali-activated slag concretes. This study thus demonstrates that performance-equivalent alkali-activated slag systems that are more sustainable from energy and environmental standpoints can be proportioned.

With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the idea that visual analytics (VA) can help enable the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a VA solution is still lacking. A proposed VA toolkit extracts data from Bitly and Twitter to predict movie revenue and ratings. Results from the 2013 VAST Box Office Challenge demonstrate the benefit of an interactive environment for predictive analysis, compared to a purely statistical modeling approach. The VA approach used by the toolkit is generalizable to other domains involving social media data, such as sales forecasting and advertisement analysis.

Atomistic simulations have shown that the grain boundary (GB) structure affects a number of physical, mechanical, thermal, and chemical properties, which can have a profound effect on macroscopic properties of polycrystalline materials. The research objective herein is to use atomistic simulations to explore the role that GB structure and the adjacent crystallographic orientations have on the directional asymmetry of an intergranular crack (i.e. cleavage behavior is favored along one direction, while ductile behavior along the other direction of the interface) for aluminum grain boundaries. Simulation results from seven 〈110〉 symmetric tilt grain boundaries (STGBs) show that the GB structure and the associated free volume directly influence the stress–strain response, crack growth rate, and crack tip plasticity mechanisms for middle-tension (M(T)) crack propagation specimens. In particular, the structural units present within the GB promote whether a dislocation or twinning-based mechanism operates at the crack tip during intergranular fracture along certain GBs (e.g., the ‘E’ structural unit promotes twinning at the crack tip in Al). Furthermore, the crystallography of the adjacent grains, and therefore the available slip planes, can significantly affect the crack growth rates in both directions of the crack – this creates a strong directional asymmetry in the crack growth rate in the Σ11 (113) and the Σ27 (552) STGBs. Upon comparing these results with the theoretical Rice criterion, it was found that certain GBs in this study (Σ9 (221), Σ11 (332) and Σ33 (441)) show an absence of directional asymmetry in the observed crack growth behavior, in conflict with the Rice criterion. The significance of the present research is that it provides a physical basis for the role of GB character and crystallographic orientation on intergranular crack tip deformation behavior.

Feedback has a strong influence on effective learning from computer-based instruction. Prior research on feedback in computer-based instruction has mainly focused on static feedback schedules that employ the same feedback schedule throughout an instructional session. This study examined transitional feedback schedules in computer-based multimedia instruction on procedural problem-solving in electrical circuit analysis. Specifically, we compared two transitional feedback schedules: the TFS-P schedule switched from initial feedback after each problem step to feedback after a complete problem at later learning states; the TFP-S schedule transitioned from feedback after a complete problem to feedback after each problem step. As control conditions, we also considered two static feedback schedules, namely providing feedback after each practice problem-solving step (SFS) or providing feedback after attempting a complete multi-step practice problem (SFP). Results indicate that the static stepwise (SFS) and transitional stepwise to problem (TFS-P) feedback produce higher problem solving near-transfer post-test performance than static problem (SFP) and transitional problem to step (TFP-S) feedback. Also, TFS-P resulted in higher ratings of program liking and feedback helpfulness than TFP-S. Overall, the study results indicate benefits of maintaining high feedback frequency (SFS) and reducing feedback frequency (TFS-P) compared to low feedback frequency (SFP) or increasing feedback frequency (TFP-S) as novice learners acquire engineering problem solving skills.

An outstanding and fundamental problem in contemporary physics is to include and probe the many-body effect in the study of relativistic quantum manifestations of classical chaos. We address this problem using graphene systems described by the Hubbard Hamiltonian in the setting of resonant tunneling. Such a system consists of two symmetric potential wells separated by a potential barrier, and the geometric shape of the whole domain can be chosen to generate integrable or chaotic dynamics in the classical limit. Employing a standard mean-field approach to calculating a large number of eigenenergies and eigenstates, we uncover a class of localized states with near-zero tunneling in the integrable systems. These states are not the edge states typically seen in graphene systems, and as such they are the consequence of many-body interactions. The physical origin of the non-edge-state type of localized states can be understood by the one-dimensional relativistic quantum tunneling dynamics through the solutions of the Dirac equation with appropriate boundary conditions. We demonstrate that, when the geometry of the system is modified to one with chaos, the localized states are effectively removed, implying that in realistic situations where many-body interactions are present, classical chaos is capable of facilitating greatly quantum tunneling. This result, besides its fundamental importance, can be useful for the development of nanoscale devices such as graphene-based resonant-tunneling diodes.

Dynamical systems based on the minority game (MG) have been a paradigm for gaining significant insights into a variety of social and biological behaviors. Recently, a grouping phenomenon has been unveiled in MG systems of multiple resources (strategies) in which the strategies spontaneously break into an even number of groups, each exhibiting an identical oscillation pattern in the attendance of game players. Here we report our finding of spontaneous breakup of resources into three groups, each exhibiting period-three oscillations. An analysis is developed to understand the emergence of the striking phenomenon of triple grouping and period-three oscillations. In the presence of random disturbances, the triple-group/period-three state becomes transient, and we obtain explicit formula for the average transient lifetime using two methods of approximation. Our finding indicates that, period-three oscillation, regarded as one of the most fundamental behaviors in smooth nonlinear dynamical systems, can also occur in much more complex, evolutionary-game dynamical systems. Our result also provides a plausible insight for the occurrence of triple grouping observed, for example, in the U.S. housing market.

To test reproducibility of a technical specification under development for potential-induced degradation (PID) and polarization, three crystalline silicon module types were distributed in five replicas each to five laboratories. Stress tests were performed in environmental chambers at 60 °C, 85% relative humidity, 96 h, and with module nameplate system voltage applied. Results from the modules tested indicate that the test protocol can discern susceptibility to PID according to the pass/fail criteria with acceptable consistency from lab to lab; however, areas for improvement are indicated to achieve better uniformity in temperature and humidity on the module surfaces. In the analysis of variance of the results, 6% of the variance was attributed to laboratory influence, 34% to module design, and 60% to variability in test results within a given design. Testing with the additional factor of illumination with ultraviolet light slowed or arrested the degradation. Testing at 25 °C with aluminum foil as the module ground was also examined for comparison. The foil, as tested, did not itself achieve consistent contact to ground at all surfaces, but methods to ensure more consistent grounding were found and proposed. The rates of degradation in each test are compared, and details affecting the rates are discussed.

Sensorimotor control theories propose that the central nervous system exploits expected sensory consequences generated by motor commands for movement planning, as well as online sensory feedback for comparison with expected sensory feedback for monitoring and correcting, if needed, ongoing motor output. In our study, we tested this theoretical framework by quantifying the functional role of expected vs. actual proprioceptive feedback for planning and regulation of gait in humans. We addressed this question by using a novel methodological approach to deliver fast perturbations of the walking surface stiffness, in conjunction with a virtual reality system that provided visual feedback of upcoming changes of surface stiffness. In the “predictable” experimental condition, we asked subjects to learn associating visual feedback of changes in floor stiffness (sand patch) during locomotion to quantify kinematic and kinetic changes in gait prior to and during the gait cycle. In the “unpredictable” experimental condition, we perturbed floor stiffness at unpredictable instances during the gait to characterize the gait-phase dependent strategies in recovering the locomotor cycle. For the “unpredictable” conditions, visual feedback of changes in floor stiffness was absent or inconsistent with tactile and proprioceptive feedback. The investigation of these perturbation-induced effects on contralateral leg kinematics revealed that visual feedback of upcoming changes in floor stiffness allows for both early (preparatory) and late (post-perturbation) changes in leg kinematics. However, when proprioceptive feedback is not available, the early responses in leg kinematics do not occur while the late responses are preserved although in a, slightly attenuated form. The methods proposed in this study and the preliminary results of the kinematic response of the contralateral leg open new directions for the investigation of the relative role of visual, tactile, and proprioceptive feedback on gait control, with potential implications for designing novel robot-assisted gait rehabilitation approaches.