ASU Scholarship Showcase

Cities in the Global South face rapid urbanization challenges and often suffer an acute lack of infrastructure and governance capacities. Smart Cities Mission, in India, launched in 2015, aims to offer a novel approach for urban renewal of 100 cities following an area‐based development approach, where the use of ICT and digital technologies is particularly emphasized. This article presents a critical review of the design and implementation framework of this new urban renewal program across selected case‐study cities. The article examines the claims of the so‐called “smart cities” against actual urban transformation on‐ground and evaluates how “inclusive” and “sustainable” these developments are. We quantify the scale and coverage of the smart city urban renewal projects in the cities to highlight who the program includes and excludes. The article also presents a statistical analysis of the sectoral focus and budgetary allocations of the projects under the Smart Cities Mission to find an inherent bias in these smart city initiatives in terms of which types of development they promote and the ones it ignores. The findings indicate that a predominant emphasis on digital urban renewal of selected precincts and enclaves, branded as “smart cities,” leads to deepening social polarization and gentrification. The article offers crucial urban planning lessons for designing ICT‐driven urban renewal projects, while addressing critical questions around inclusion and sustainability in smart city ventures.`

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

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.

The low pH of the stomach serves as a barrier to ingested microbes and must be overcome or bypassed when delivering live bacteria for vaccine or probiotic applications. Typically, the impact of stomach acidity on bacterial survival is evaluated in vitro, as there are no small animal models to evaluate these effects in vivo. To better understand the effect of this low pH barrier to live attenuated Salmonella vaccines, which are often very sensitive to low pH, we investigated the value of the histamine mouse model for this application. A low pH gastric compartment was transiently induced in mice by the injection of histamine. This resulted in a gastric compartment of approximately pH 1.5 that was capable of distinguishing between acid-sensitive and acid-resistant microbes. Survival of enteric microbes during gastric transit in this model directly correlated with their in vitro acid resistance. Because many Salmonella enterica serotype Typhi vaccine strains are sensitive to acid, we have been investigating systems to enhance the acid resistance of these bacteria. Using the histamine mouse model, we demonstrate that the in vivo survival of S. Typhi vaccine strains increased approximately 10-fold when they carried a sugar-inducible arginine decarboxylase system. We conclude that this model will be a useful for evaluating live bacterial preparations prior to clinical trials.

Leucine-responsive regulatory protein (Lrp) is known to be an indirect activator of type 1 fimbriae synthesis in Salmonella enterica serovar Typhimurium via direct regulation of FimZ, a direct positive regulator for type 1 fimbriae production. Using RT-PCR, we have shown previously that fimA transcription is dramatically impaired in both lrp-deletion (Δlrp) and constitutive-lrp expression (lrp^C) mutant strains. In this work, we used chromosomal P_fimA-lacZ fusions and yeast agglutination assays to confirm and extend our previous results. Direct binding of Lrp to P_fimA was shown by an electrophoretic mobility shift assay (EMSA) and DNA footprinting assay. Site-directed mutagenesis revealed that the Lrp-binding motifs in P_fimA play a role in both activation and repression of type 1 fimbriae production. Overproduction of Lrp also abrogates fimZ expression. EMSA data showed that Lrp and FimZ proteins independently bind to P_fimA without competitive exclusion. In addition, both Lrp and FimZ binding to P_fimA caused a hyper retardation (supershift) of the DNA-protein complex compared to the shift when each protein was present alone. Nutrition-dependent cellular Lrp levels closely correlated with the amount of type 1 fimbriae production. These observations suggest that Lrp plays important roles in type 1 fimbriation by acting as both a positive and negative regulator and its effect depends, at least in part, on the cellular concentration of Lrp in response to the nutritional environment.

Researchers have iterated that the future of synthetic biology and biotechnology lies in novel consumer applications of crossing biology with engineering. However, if the new biology's future is to be sustainable, early and serious efforts must be made towards social sustainability. Therefore, the crux of new applications of synthetic biology and biotechnology is public understanding and acceptance. The RASVaccine is a novel recombinant design not found in nature that re-engineers a common bacteria ( Salmonella) to produce a strong immune response in humans. Synthesis of the RASVaccine has the potential to improve public health as an inexpensive, non-injectable product. But how can scientists move forward to create a dialogue of creating a 'common sense' of this new technology in order to promote social sustainability? This paper delves into public issues raised around these novel technologies and uses the RASVaccine as an example of meeting the public with a common sense of its possibilities and limitations.

Background: Salmonella has been employed to deliver therapeutic molecules against cancer and infectious diseases. As the carrier for target gene(s), the cargo plasmid should be stable in the bacterial vector. Plasmid recombination has been reduced in E. coli by mutating several genes including the recA, recE, recF and recJ. However, to our knowledge, there have been no published studies of the effect of these or any other genes that play a role in plasmid recombination in Salmonella enterica.

Results: The effect of recA, recF and recJ deletions on DNA recombination was examined in three serotypes of Salmonella enterica. We found that (1) intraplasmid recombination between direct duplications was RecF-independent in Typhimurium and Paratyphi A, but could be significantly reduced in Typhi by a ΔrecA or ΔrecF mutation; (2) in all three Salmonella serotypes, both ΔrecA and ΔrecF mutations reduced intraplasmid recombination when a 1041 bp intervening sequence was present between the duplications; (3) ΔrecA and ΔrecF mutations resulted in lower frequencies of interplasmid recombination in Typhimurium and Paratyphi A, but not in Typhi; (4) in some cases, a ΔrecJ mutation could reduce plasmid recombination but was less effective than ΔrecA and ΔrecF mutations. We also examined chromosome-related recombination. The frequencies of intrachromosomal recombination and plasmid integration into the chromosome were 2 and 3 logs lower than plasmid recombination frequencies in Rec[superscript +] strains. A ΔrecA mutation reduced both intrachromosomal recombination and plasmid integration frequencies.

Conclusions: The ΔrecA and ΔrecF mutations can reduce plasmid recombination frequencies in Salmonella enterica, but the effect can vary between serovars. This information will be useful for developing Salmonella delivery vectors able to stably maintain plasmid cargoes for vaccine development and gene therapy.

Background: To be effective, orally administered live Salmonella vaccines must first survive their encounter with the low pH environment of the stomach. To enhance survival, an antacid is often given to neutralize the acidic environment of the stomach just prior to or concomitant with administration of the vaccine. One drawback of this approach, from the perspective of the clinical trial volunteer, is that the taste of a bicarbonate-based acid neutralization system can be unpleasant. Thus, we explored an alternative method that would be at least as effective as bicarbonate and with a potentially more acceptable taste. Because ingestion of protein can rapidly buffer stomach pH, we examined the possibility that the protein-rich Ensure® Nutrition shakes would be effective alternatives to bicarbonate.

Results: We tested one Salmonella enterica serovar Typhimurium and three Salmonella Typhi vaccine strains and found that all strains survived equally well when incubated in either Ensure® or bicarbonate. In a low gastric pH mouse model, Ensure® worked as well or better than bicarbonate to enhance survival through the intestinal tract, although neither agent enhanced the survival of the S. Typhi test strain possessing a rpoS mutation.

Conclusions: Our data show that a protein-rich drink such as Ensure® Nutrition shakes can serve as an alternative to bicarbonate for reducing gastric pH prior to administration of a live Salmonella vaccine.