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.

The lack of lipidome analytical tools has limited our ability to gain new knowledge about lipid metabolism in microalgae, especially for membrane glycerolipids. An electrospray ionization mass spectrometry-based lipidomics method was developed for Nannochloropsis oceanica IMET1, which resolved 41 membrane glycerolipids molecular species belonging to eight classes. Changes in membrane glycerolipids under nitrogen deprivation and high-light (HL) conditions were uncovered. The results showed that the amount of plastidial membrane lipids including monogalactosyldiacylglycerol, phosphatidylglycerol, and the extraplastidic lipids diacylglyceryl-O-4′-(N, N, N,-trimethyl) homoserine and phosphatidylcholine decreased drastically under HL and nitrogen deprivation stresses. Algal cells accumulated considerably more digalactosyldiacylglycerol and sulfoquinovosyldiacylglycerols under stresses. The genes encoding enzymes responsible for biosynthesis, modification and degradation of glycerolipids were identified by mining a time-course global RNA-seq data set. It suggested that reduction in lipid contents under nitrogen deprivation is not attributable to the retarded biosynthesis processes, at least at the gene expression level, as most genes involved in their biosynthesis were unaffected by nitrogen supply, yet several genes were significantly up-regulated. Additionally, a conceptual eicosapentaenoic acid (EPA) biosynthesis network is proposed based on the lipidomic and transcriptomic data, which underlined import of EPA from cytosolic glycerolipids to the plastid for synthesizing EPA-containing chloroplast membrane lipids.

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.

Nitrogen availability and cell density each affects growth and cellular astaxanthin content of Haematococcus pluvialis, but possible combined effects of these two factors on the content and productivity of astaxanthin, especially under outdoor culture conditions, is less understood. In this study, the effects of the initial biomass densities IBDs of 0.1, 0.5, 0.8, 1.5, 2.7, 3.5, and 5.0 g L-1 DW and initial nitrogen concentrations of 0, 4.4, 8.8, and 17.6 mM nitrate on growth and cellular astaxanthin content of H. pluvialis Flotow K-0084 were investigated in outdoor glass column photobioreactors in a batch culture mode. A low IBD of 0.1 g L-1 DW led to photo-bleaching of the culture within 1-2 days. When the IBD was 0.5 g L-1 and above, the rate at which the increase in biomass density and the astaxanthin content on a per cell basis was higher at lower IBD. When the IBD was optimal (i.e., 0.8 g L-1), the maximum astaxanthin content of 3.8% of DW was obtained in the absence of nitrogen, whereas the maximum astaxanthin productivity of 16.0 mg L-1 d(-1) was obtained in the same IBD culture containing 4.4 mM nitrogen. The strategies for achieving maximum Haematococcus biomass productivity and for maximum cellular astaxanthin content are discussed.

Major progress has been made in the past decade towards understanding of the biosynthesis of red carotenoid astaxanthin and its roles in stress response while exploiting microalgae-based astaxanthin as a potent antioxidant for human health and as a coloring agent for aquaculture applications. In this review, astaxanthin-producing green microalgae are briefly summarized with Haematococcus pluvialis and Chlorella zofingiensis recognized to be the most popular astaxanthin-producers. Two distinct pathways for astaxanthin synthesis along with associated cellular, physiological, and biochemical changes are elucidated using H. pluvialis and C. zofingiensis as the model systems. Interactions between astaxanthin biosynthesis and photosynthesis, fatty acid biosynthesis and enzymatic defense systems are described in the context of multiple lines of defense mechanisms working in concert against photooxidative stress. Major pros and cons of mass cultivation of H. pluvialis and C. zofingiensis in phototrophic, heterotrophic, and mixotrophic culture modes are analyzed. Recent progress in genetic engineering of plants and microalgae for astaxanthin production is presented. Future advancement in microalgal astaxanthin research will depend largely on genome sequencing of H pluvialis and C. zofingiensis and genetic toolbox development. Continuous effort along the heterotrophic-phototrophic culture mode could lead to major expansion of the micro algal astaxanthin industry.

Background: Microalgae are promising feedstock for production of lipids, sugars, bioactive compounds and in particular biofuels, yet development of sensitive and reliable phylotyping strategies for microalgae has been hindered by the paucity of phylogenetically closely-related finished genomes.

Results: Using the oleaginous eustigmatophyte Nannochloropsis as a model, we assessed current intragenus phylotyping strategies by producing the complete plastid (pt) and mitochondrial (mt) genomes of seven strains from six Nannochloropsis species. Genes on the pt and mt genomes have been highly conserved in content, size and order, strongly negatively selected and evolving at a rate 33% and 66% of nuclear genomes respectively. Pt genome diversification was driven by asymmetric evolution of two inverted repeats (IRa and IRb): psbV and clpC in IRb are highly conserved whereas their counterparts in IRa exhibit three lineage-associated types of structural polymorphism via duplication or disruption of whole or partial genes. In the mt genomes, however, a single evolution hotspot varies in copy-number of a 3.5 Kb-long, cox1-harboring repeat. The organelle markers (e.g., cox1, cox2, psbA, rbcL and rrn16_mt) and nuclear markers (e.g., ITS2 and 18S) that are widely used for phylogenetic analysis obtained a divergent phylogeny for the seven strains, largely due to low SNP density. A new strategy for intragenus phylotyping of microalgae was thus proposed that includes (i) twelve sequence markers that are of higher sensitivity than ITS2 for interspecies phylogenetic analysis, (ii) multi-locus sequence typing based on rps11_mt-nad4, rps3_mt and cox2-rrn16_mt for intraspecies phylogenetic reconstruction and (iii) several SSR loci for identification of strains within a given species.

Conclusion: This first comprehensive dataset of organelle genomes for a microalgal genus enabled exhaustive assessment and searches of all candidate phylogenetic markers on the organelle genomes. A new strategy for intragenus phylotyping of microalgae was proposed which might be generally applicable to other microalgal genera and should serve as a valuable tool in the expanding algal biotechnology industry.

The unicellular green microalga Desmodesmus sp. S1 can produce more than 50% total lipid of cell dry weight under high light and nitrogen-limitation conditions. After irradiation by heavy ¹²C⁶⁺ ion beam of 10, 30, 60, 90 or 120 Gy, followed by screening of resulting mutants on 24-well microplates, more than 500 mutants were obtained. One of those, named D90G-19, exhibited lipid productivity of 0.298 g L^-1⋅d^-1, 20.6% higher than wild type, likely owing to an improved maximum quantum efficiency (Fv/Fm) of photosynthesis under stress. This work demonstrated that heavy-ion irradiation combined with high-throughput screening is an effective means for trait improvement. The resulting mutant D90G-19 may be used for enhanced lipid production.