Abstract

Metal nanoparticles have shown exceptional catalytic properties with relatively high chemical activity and specificity of the interaction. The chemical reactivity of metal nanoparticles is intimately linked to their elementary composition, atomic arrangement, shape, and size, a variety of features whose role is often intertwined. Understanding synergistic effects by studying the electronic structure and interaction of molecules in a reaction is essential to fine-tune the catalytic properties of bimetallic nano-alloy clusters which might be used in designing novel efficient catalysts. Density functional theory PBE0 calculations were performed to examine the structure and energetics of various intermediates involved in the CO oxidation reaction catalyzed by Au3−xYx (x = 0-3 and Y denotes Ag or, Pt or, Pd) trimer clusters through two possible pathways: Eley–Rideal (ER) and Langmuir–Hinshelwood (LH) mechanisms. The results of this investigation show that the catalytic behavior of the nano-cluster highly depends on its composition and the reaction site taken into consideration. It is observed that with a lower dopant percentage in the cluster composition, the barrier heights for a bimetallic trimer varies highly over different reaction sites. Thus, the reaction site becomes an important aspect of catalysis with clusters having a low dopant percentage. The gold-silver clusters and AuPt2 prefer ER mechanism due to low barrier height and higher extent of O–O bond activation for respective ER transition states whereas, gold-palladium and Au2Pt selectively favor LH mechanism due to low barrier height and more charge transfer from the corresponding cluster. The most active reaction centres found for gold-silver, gold-palladium, gold-platinum clusters are gold, palladium and platinum atoms respectively which, is also supported by the barrier height analysis, charge transfer analysis, and adsorption energetics. Our results show that the bimetallic clusters, in general, are more catalytically efficient in comparison to their pristine mono-metallic counterparts, owing to the strong synergistic effects. They efficiently activate the O–O bond in the oxidation reaction and have relatively easy CO2 dissociation. Positive linear correlations between transition state energies, ETS (ER) and ETS (LH), and the binding energies, ECO + EO2 and ECO-ADSORPTION, are found for the bimetallic clusters with Pearson correlation coefficient of 0.92 and 0.86, respectively. A correlation between ECO-ADSORPTION and ECO + EO2 is also found to be positive linear with a 0.92 Pearson correlation coefficient. Overall results of this investigation indicate that the alloyed clusters could potentially have better catalytic activity as compared to pure gold clusters for CO oxidation at low temperatures.

Abstract

Urbanization is a rapid and inevitable trend around the world with seemingly no sign of abating. The way in which urban sprawls are sustainably managed can be a major force for global adverse climate change mitigation and adaptation strategies; that entails better mass transit, more efficient lighting and heating systems for buildings, improved waste management and cleaner sources of energy production. All can ultimately have large positive effects for reducing climate change but are rooted in locally oriented strategies. The thesis thus aims to analyze the relationship between urban expansion and microclimate temperature fluctuations and develop an algorithm to forecast the land surface temperature based on the predicted land use-land cover changes. The thesis also extends to develop and test green measures on the adaptation front to reduce the intensifying daily surface temperature. Future increase in the size of the world's urban population is expected to be highly concentrated in developing countries that include India as well. The total number of towns and urban agglomerations were reported as 393 and 465 as per the 2001 and 2011 India census. In this regard, the thesis examines the Vijayawada urban agglomeration, Andhra Pradesh, India as the case study to validate the developed algorithm. The city, Vijayawada is one of the educational hubs of the state and is witnessing rapid population migration as well as temperature fluctuations both during summer and winter. Over the period, the city expanded at the cost of natural vegetation, hills, and water bodies leading to an increase in greenhouse gas emissions and loss of green spaces and wetlands existing within the city. The city expansion and the associated temperature fluctuations are studied with the aid of remote sensing technology. Temporal Landsat satellite images of four years viz., 1990, 2000, 2010 and 2018 are used to generate Land Use/Land Cover maps with four major classes viz.; built-up, vegetation, water body and others. Change detection and transition of the natural land cover to man-made land use was computed for the study area. Sprawl analysis of the city was carried out by generating multiple buffer rings over the study region to evaluate the urban density and annual urban growth rate. Shannon‘s entropy was employed to identify the nature of city expansion. The seasonal variation of the land surface temperature was studied using Mono-window algorithm. The temperature variation over individual classes was computed with the aid of a self-designed random point method. Results showed a steady increasing trend in the urban density and land surface temperature with the distinct formation of a heat island over the city especially during winters throughout the study period. The built-up area has increased from 28.20 km2 in 1990 to 138.01 km2 in 2018. The directional growth analysis captured the pattern of city growth as tentacle type development in conjunction with infill development. The prepared land use/land cover maps were taken forward to model future scenarios of the built-up dynamics of the city in cellular automata (CA) based environment. Accurate identification of the dominant driving factors in the expansion of a city is essential for CAbased urban expansion modeling. The drivers of change for the city were examined and then applied to model the future scenario of the city. Further, the study also aims at comparing the efficiency of the renowned CA-based urban growth model, SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hill shade), and a self-designed CA-based hybrid model developed in combination with genetic algorithm (GA) to model future scenario of the city. Analysis of the statistical significance of the driving factors shows that the terrain and population density are the two dominant factors influencing the expansion of the city. Both the models' output on predicting the urban growth indicate that the available open spaces within the existing city extent get further converted to built-up indicating infill development, and more growth occurs along the fringes of the existing city. Validation of both the models showed overall accuracy higher than 80%. The inclusion of the demographic variable and utilization of the GA ensured that the hybrid model performs better than the SLEUTH model. Analysis of the built-up densification of the cityscape showed a shift in the pattern of built-up dynamics over time. An algorithm to forecast the future land surface temperature for the modeled scenario of the city in 2020 and 2030 has been developed based on multivariate kernel ridge regression. The land surface temperature is predicted for the modeled Land Use/Land Cover of the city by deriving the constraining ratio between the spatial and climatic variables. Development of this algorithm adds to the existing knowledge on microclimate change studies as seldom studies di

Abstract

Within the academic scholarly environment, the contribution of an author in the form of publications holds an intrinsic value responsible for the effective dissemination of knowledge for the scientific community. In such a social system of science, citation practices characterize social functions like the conferral of recognition upon the work of others as well as the acknowledgement of one’s intellectual debt. Citations and references operate in a collective cognitive and moral framework designed to provide historical lineage of knowledge and repayment of the intellectual debts through their acknowledgement. However, the structure of intellectual influence is misrepresented when only the immediate citations and their cardinality are taken into consideration. Citation analysis has the potential in providing valuable insights about the social system of science, including the hierarchical and fractal nature of scientific development spanning across a wide range of topics. Thus, in order to better understand the associative dissemination of influence and approximately construe the anatomy of this structure, complex interactions in the convoluted network of authors and papers need to be probed. Our study aims at understanding these influence associations eventuated between authors based on citation data in the heterogeneous bibliographic network. Besides learning the existence of such associations, our work focuses on quantifying the extent of the pairwise impact measure between authors. For the bibliographic dataset of authors and publications, we define proxy scores that attempt to determine the associative bilateral influence of the cited author over the citing author using a diffusion-based influence aggregation approach. Since the ranks of authors range across a broad spectrum, we present a detailed qualitative analysis and suggest suitable extensions for variants in academic networks. For assessing predictive capacity and competency of the devised scores, we develop an objective approach of evaluating the influence model for the bibliographic dataset. We perform an empirical analysis and validation using author representations derived by using the proposed bilateral influence scores and the baseline weights. These representations are generated by profiling author interactions within the bibliographic network using representation learning. Subsequently, we discuss the experimental results subject to a classification task along with a comparative study against observations with those obtained with the baseline. The latent vector representations of authors developed harnesses the structural connectivity and capture the network topology of the academic network. While the efficacy of these representations in the context of the problem statement is discussed, they form a generic multivalent result. It constitutes the underlying mechanism of the proposed influence model and suggests utility for a diverse set of statistical modeling tasks within the bibliographic domain. The results summarized in this thesis significantly deepen our understanding of the underlying influence mechanisms governing the system of science. Finally, our work in this thesis lies in the broader research initiative of citation analysis and aims to contribute to the literature on the identifying and quantifying influence associations in the scientific network.

Abstract

Metallic and polymeric nanomaterials have been at the forefront of variousstudies and applications for a long time. Development of new, greener, fast, scalable, and controlled synthesis methods, understanding their structures and properties, and exploration of new uses and futuristic applications of these nanomaterials have been the major goals of the researchers recently. The thesis work involves the studies of selected nanoparticle systems of the metal,gold and a conducting polymer (CP), polypyrrole.Gold nanoparticles are one of the most extensively studied nanomaterials systems due to their stability, tunable optical properties, and facile surface chemistry as well as wide-ranging promising applications in nanomedicine, catalysis, electronics, and so on. Among various kinds of polymers, conducting polymersare especially important because the unique structure-dependent conducting propertysupplements the other useful properties ofa polymeric substance. Since the properties of nanomaterials depend on their size, dimensional anisotropy, structure, and crystallinity, there is a constant effort to develop ways to tune these features and hence their properties. Though substantial progress has been made in the size and shape-controlled synthesis of metal nanoparticles, on the contrary, the post-synthesis tuning of the size and morphology of the nanomaterials has been less explored. However, such post synthesis size- and morphology-tuning of nanomaterials can offer novel materials-features that may be difficult to obtain otherwise. New facile, faster, greener, and economical methodologies to manipulate materials at the nanoscale are the need of the day.We discuss various chemical and photochemical methodologies that we have explored and developed for the controlled post-synthesis tuning of the size and morphology of diverse kinds of gold nanoparticles. We have demonstrated a simple, rapid, and practical chemical method that allows post-synthesis tuning of the size and surface morphology of three different kinds of gold nanoparticles under mild conditions. We have employed aqueous KI3as a chemical etching agent and have studied the effects of I3- concentration on the tuning process. I2 is a mild oxidizing agent and allows controlled tuning of the particles at room temperature. This method does not involve any hazardous chemicals, sophisticated equipment, or tedious process such as heating, continuous oxygen bubbling, maintenance of pH, etc. The change in the particle size, surface structure, and morphology during the post-synthesis tuning has been followed by the UV-visible and surface-enhanced Raman scattering (SERS) spectroscopies and transmission electron microscopic (TEM) studies. Excellent surface-enhanced Raman scatteringsubstrates have been prepared by the tuning of the gold nanoparticles. We have studied thephotochemical post-synthesis modifications of three different kinds of nanoparticles. UV-C light (254 nm wavelength) has been used for the photochemical etching of gold nanoparticles. The roles of the etchant ferric chloride, the particle-surface capping agent CTAB, particle number density, and light intensity have been explored. The mechanisms of the etching reactions have been investigated. We have demonstrated that one can easily and controllably modify the morphologies of the preformed gold nanoparticles by using photochemical and chemical etching methods by tuning the exposure of UV-light and other reaction parameters. The photochemical method is very fast, straight forward and does not need any tedious process or sophisticated equipment. The effects of the post-synthesis treatments of three different kinds of gold nanoparticles on their catalytic and SERS properties, which are known to be highly sensitive to the surface characteristics of the nanomaterials, have been investigated. We have demonstrated that the surface modifications produce large differences in the catalytic and SERS properties of gold nanostars, which have extremely complex morphologies, compared to gold nanorods and nanospheres. We have explored an oxidative photo-polymerization technique for the synthesis of polypyrroles (PPYs).The photochemical method of PPY synthesis is rapid, versatile, inexpensive, and technologically simple to implement. The method is a one-step surfactant-free method that does not use any external semiconductor photo-catalyst nanoparticles or chiral dopants. We have studied the effects of the light energy and the period of light irradiation on the photo-polymerization of pyrrole by using different UV and visible light sources. For a comparative study, we have also used an oxidative chemical polymerization by using ferric chloride oxidant. The synthesized polypyrroles have been characterized by various techniques including UV-Vis spectroscopy, scanning electron microscopy (SEM), thermogravimetry, and CD spectroscopy. The level of doping, morphology, and chiral conformation of the PPYs can be varied by us

Abstract

Metallic and polymeric nanomaterials have been at the forefront of various studies and applications for a long time. Development of new, greener, fast, scalable, and controlled synthesis methods, understanding their structures and properties, and exploration of new uses and futuristic applications of these nanomaterials have been the major goals of the researchers recently. The thesis work involves the studies of selected nanoparticle systems of the metal, gold and a conducting polymer (CP), polypyrrole. Gold nanoparticles are one of the most extensively studied nanomaterials systems due to their stability, tunable optical properties, and facile surface chemistry as well as wide-ranging promising applications in nanomedicine, catalysis, electronics, and so on. Among various kinds of polymers, conducting polymers are especially important because the unique structure-dependent conducting property supplements the other useful properties of a polymeric substance. Since the properties of nanomaterials depend on their size, dimensional anisotropy, structure, and crystallinity, there is a constant effort to develop ways to tune these features and hence their properties. Though substantial progress has been made in the size and shape-controlled synthesis of metal nanoparticles, on the contrary, the post-synthesis tuning of the size and morphology of the nanomaterials has been less explored. However, such post synthesis size- and morphology-tuning of nanomaterials can offer novel materials-features that may be difficult to obtain otherwise. New facile, faster, greener, and economical methodologies to manipulate materials at the nanoscale are the need of the day. We discuss various chemical and photochemical methodologies that we have explored and developed for the controlled post-synthesis tuning of the size and morphology of diverse kinds of gold nanoparticles. We have demonstrated a simple, rapid, and practical chemical method that allows post-synthesis tuning of the size and surface morphology of three different kinds of gold nanoparticles under mild conditions. We have employed aqueous KI3 as a chemical etching agent and have studied the effects of I3- concentration on the tuning process. I2 is a mild oxidizing agent and allows controlled tuning of the particles at room temperature. This method does not involve any hazardous chemicals, sophisticated equipment, or tedious process such as heating, continuous oxygen bubbling, maintenance of pH, etc. The change in the particle size, surface structure, and morphology during the post-synthesis tuning has been followed by the UV-visible and surface-enhanced Raman scattering (SERS) spectroscopies and transmission electron microscopic (TEM) studies. Excellent surface-enhanced Raman scattering substrates have been prepared by the tuning of the gold nanoparticles. We have studied the photochemical post-synthesis modifications of three different kinds of nanoparticles. UV-C light (254 nm wavelength) has been used for the photochemical etching of gold nanoparticles. The roles of the etchant ferric chloride, the particle-surface capping agent CTAB, particle number density, and light intensity have been explored. The mechanisms of the etching reactions have been investigated. We have demonstrated that one can easily and controllably modify the morphologies of the preformed gold nanoparticles by using photochemical and chemical etching methods by tuning the exposure of UV-light and other reaction parameters. The photochemical method is very fast, straight forward and does not need any tedious process or sophisticated equipment. The effects of the post-synthesis treatments of three different kinds of gold nanoparticles on their catalytic and SERS properties, which are known to be highly sensitive to the surface characteristics of the nanomaterials, have been investigated. We have demonstrated that the surface modifications produce large differences in the catalytic and SERS properties of gold nanostars, which have extremely complex morphologies, compared to gold nanorods and nanospheres. We have explored an oxidative photo-polymerization technique for the synthesis of polypyrroles (PPYs). The photochemical method of PPY synthesis is rapid, versatile, inexpensive, and technologically simple to implement. The method is a one-step surfactant-free method that does not use any external semiconductor photo-catalyst nanoparticles or chiral dopants. We have studied the effects of the light energy and the period of light irradiation on the photo-polymerization of pyrrole by using different UV and visible light sources. For a comparative study, we have also used an oxidative chemical polymerization by using ferric chloride oxidant. The synthesized polypyrroles have been characterized by various techniques including UV-Vis spectroscopy, scanning electron microscopy (SEM), thermogravimetry, and CD spectroscopy. The level of doping, morphology, and chiral conformation of the PPYs can be var

Abstract

Rice is a major staple food for half the world’s population. With the completion of rice genome sequencing projects and the availability of several high-throughput experimental platforms, a large amount of data is now available in the public domain. However, there still exists a gap between data generation and effective bioinformatic analyses of such data in the scientific community. In the era of Big Data, network-based approaches have made great strides in the field of biomedical research, personal therapeutics as well as in evo-devo studies. Taking cues from these studies, in this thesis, we use a network biology approach to get a systems-level understanding of the rice stress transcriptome, using microarray and RNA-seq data. In the first part of the study, we considered transcriptome data from a drought tolerant rice genotype and constructed a weighted condition-dependent gene co-expression network. Based on differential gene expression analysis, we identified tissue and stage-specific co-expressed modules harbouring differentially expressed genes that are induced or repressed due to stress. Network topological properties such as degree along with fold-change information is used to identify “hub” genes. Unannotated genes that are topologically important in the network and co-clustered with known stress-responsive genes are selected for functional characterization. Network-based concepts such as conserved network neighbourhood and guide-gene approach for the annotation of these novel drought-responsive genes together with promoter analysis led to the annotations of 26 uncharacterized genes. We also used alternate network construction methods to confirm the associations of the uncharacterized genes with their conserved neighbours. In the second part of the thesis we present a meta-analytic study combining gene-expression data from seven drought-tolerant genotypes to identify various drought-adaptive processes in leaf tissue using network-based approach. Here, we propose an integrated approach that incorporates protein-protein interactions with co-expression of genes to construct networks of up and down-regulated genes and identify tightly-coupled gene clusters. Based on the processes/pathways represented by these clusters, we identified some important drought adaptive processes exhibited by these genotypes. Key transcription factors, viz., bZIPs, ABA signalling machinery and interaction of its signalling components with metabolic pathways playing a role in stress adaptive pathways was highlighted. In tandem do this, stomatal regulation and photosynthesis were observed to be important among the down-regulated processes. In the final part of the thesis we present our analysis of RNAseq data to analyze stress responsive processes in rice under different abiotic stress conditions, namely, drought, cold, flood, salinity, etc. For this study, publicly available large-scale mRNA sequencing data of rice(Oryza sativa L. cv. Nipponbare) obtained under uniform experimental conditions for different abiotic stress conditions and time-points was considered. A weighted co-expression network was constructed across four stress conditions and two hormone treatments using differentially expressed genes (DEGs). Co-expressed modules enriched with DEGs with respect to different stress/treatment conditions were identified. Two type of analyses are presented: (i) module-based analysis to identify metabolic pathways that are affected under a given stress condition, and (ii) time-course analysis of the signalling pathways involved in stress response

Abstract

Water is one of the most essential resources on this planet, of which the potable water for human consumption and use is quite limited. This valuable resource is distributed around to the population though a system of interconnected pipelines, mostly situated underground. There is a high possibility of leaks in such pipelines, which is difficult to detect, given that the system is mostly underground. Since water is a very scarce resource and has to be used judiciously, leaks in the pipeline system will prove detrimental to the world. Thus, there is a pressing need to devise solutions that can help detect leaks in such underground pipelines. Though impossible for the naked eye, advancements in technology have paved the way for development of effective monitoring mechanisms. With the advancements in sensor technologies, flow parameters can be measured without manual intervention even when these are relatively small. Leakage in pipes, however minor can cause huge loss of water if not detected early. Since a leak causes change in the flow rate and pressure in the pipeline, these changes, however small, can be picked up by some of these advanced sensors. While monitoring systems help in the identification of changes and there might be ways at addressing it, it is important to devise methods that can help detect and identify the location of water pipeline leaks. The proposed method takes inputs from water flow sensors for a given water distribution network to estimate changes to the hydraulic conditions, if any, at equilibrium and leak induced conditions. This method leverages the electronic sensor data to detect anomalies and flags the corresponding pipeline in the network. The sensors are installed as part of the water distribution network. The entire network is loaded in a Geospatial platform, like QGIS, to help visualize both the distribution network and leak locations. From a users ease point of view, a plugin was developed over the open source GIS platform of QGIS. The plugin helps trigger leak detection based on the sensor input over the spatial water distribution network. The proposed method has been tested over two lab-scale test beds of water distribution network with varying number of joints and pipes. In addition, using these two networks, the system performance was evaluated for different leak locations and varying number of leaks. Overall these cases consisted of different sensor locations, variable number of sensors, different leak locations and variable number vi vii of leaks. The leak detection system can be run in two modes - one being the on-demand mode and the other being the continuous monitoring mode. In both the modes the accuracy of the calibrated flow rate was compared with the sensor data at equilibrium and was found to be satisfactory. It is expected that the method proposed in this work will help water-pipeline managers to detect and locate the leaks in the pipeline networks, enabling them to take necessary measures to avoid wastage of water.

Abstract

Identifying Multiword Expressions (MWEs) is quite an important task when they compose almost 40 percent of the language, as in Hindi. Yet, the serious work on this started only during the last couple of decades. This is quite a challenging task as the multiword expressions do not follow any set of syntactic, semantic or lexical rules which could make them unique or different from any of the other expressions. The apparent vagueness in defining of MWEs is at the core of this problem. They have been defined in several ways by the researchers, one of them even going as far as calling them “A pain in the neck for NLP” for their idiosyncratic interpretations that cross word boundaries [Sag et al., 2002]. In this work, we try to come up with ways to semi-automatically identify the multiword expressions occurring in Hindi language. We focus our work on MWEs that can be defined on the basis of syntactic type of their constituents. Moreover, we also look at ways to identify collocations in the language that form a subset of the MWEs. We make use of some of the existing resources in Hindi, MWE extraction tools and word similarity tools to help us navigate through our problem. In a major work here, we propose a technique to identify Noun+Verb MWEs using Hindi Dependency Treebank [HDTB] as the existing knowledge base. The criteria of word similarity has been used to find an association between the test expression (a potential MWE) and an existing MWE. We hypothesize that the words which are similar tend to be part of similar idiosyncratic constructions and such expressions generally occur in sentences which has similar syntactic construction (karaka and vibhakti similarity). Thus there is a relatively higher possibility for these expressions to be MWE. We also prepare what we call a karaka chart from the HDTB knowledge base which has karaka frame details for the MWEs. Each MWE has a karaka frame, which is a list of potential karakas that the Noun+Verb expression takes for the construction of a valid and meaningful sentence. It is used to see how similar are the karaka labels of the test expression to that of its most similar MWE present in the karaka chart. The similarity is calculated using a popular word similarity tool. This work is done in an effort to get a deeper understanding of a correlation between word similarity and the karaka frame similarity. Our work on collocations delves into identifying adjective-noun bigrams using a technique proposed by us. The criteria of non-substitutability of the constituent within a collocation is a major parameter in the algorithm for collocation identification. The two commonly used methods of automatic collocation extraction, namely, t-score test and pointwise mutual information test, have also been used to perform the experiments on the same data set.

Abstract

In early stage of any building design, the design team has to probe several building models to analyze the building energy consumption. This requires simulation of numerous different models using building energy modelling software. The number of combinations increase exponentially with increasing parameters. This requires a lot of computation to simulate energy consumption for all the input combinations. In a scenario considering five building variables, with ten design choices for each variable, the design team will be required to simulate a hundred thousand different models using building energy management software. This study presents a machine learning based solution to estimate energy consumption of a building in the early phase. The existing research in this domain doesn’t focus on determining the best sample to train the regression models. This study uses a clustering based sampling method to identify the training data. Using clustering, top characteristic feature vectors are derived. The annual energy consumption is simulated for the extracted samples using EnergyPlus. A relationship is then established between the input samples and their corresponding energy consumption through various regression models. These regression models are in turn used to estimate the energy consumption for the large input data sets. This method of surrogate modeling saves a lot of computation by reducing the number of computations by a 100-fold. This method is tested for data from Jaipur and Hyderabad cities of India. Approximately hundred thousand simulations are performed for each location using cloud based parallel computation. By simulating approximately one percent of the input combinations, annual energy consumption for the large set of combinations are estimated using SVR and k-means clustering for Jaipur with accuracy greater than 93% for 99.8% of the input combinations. When the same model is trained for Hyderabad, it produced accuracy greater than 93% for 98% of the input combinations. Using a python based REST framework, an open source REST based web API service - FASTSIM API is developed which can be integrated into any existing software using HTTP requests. The data that we generated for Hyderabad and Jaipur cities is also made publicly available for the research community.

Abstract

Technology is rapidly changing the face of the world by digitizing our experiences from mobile interfaces to affective computing to immersive/ non-immersive 360-degree field of view (FOV). Despite growing importance of the 360-degree FOV, very few studies have evaluated the effectiveness of user-interface (UI) design and individual differences, such as gaming experience, in such interactions. Further, these studies lack the evaluation of time-induced stress during such complex interactions specifically during demanding situations like military operations or reconnaissance.The current thesis focuses on identifying the cognitive challenges with 360-degree 2D remote visual display interface. The studies have two main objectives: 1. Identify necessary display characteristics and cognitive issues related to, specifically, spatial perception and cognitive mapping for 360-degree 2D virtual display interface design 2. Evaluation of Stimulus-Response compatibility as 360-degree virtual environment differs from real world settings We conducted series of experiment to assess the effect of gaming experience under timer vs. no-timer condition, on the navigation, direction estimation, and spatial memory while using various 360-degree 2D interface designs. Participants’ tasks were to navigate in the 3D virtual terrain, locate the targets with perceived egocentric direction, and later place the targets on the overhead map of the terrain. In a follow up study, participants were asked to draw a cognitive map of the virtual environment (VE) post navigation. Interface display designs, gaming experience, and timer (inducing stress) were compared based on navigation time, egocentric direction estimation, target placement and mapping accuracy. Behavioural results showed an advantage of gaming experience in navigation and in direction estimation. Though non-timer condition favoured the gamers in direction estimation, the timer condition did not show any gain based on gaming experience. Interface display designs with visual boundaries showed an advantage in direction estimation. However, no effect of interface display design and gaming experience was observed on target placement task, indicating feeble mental map development during the exploration phase. Results show better cognitive mapping with two split screens 360-degree view compared to other UI designs, indicating a better spatial compatibility. Further, gamers scored better than non-gamers across the interfaces in 360-degree 2D virtual display. In addition, the data indicates that it’s not only the UI designs, but also the individual capabilities such as gaming experience and gender that can influence the task performance. Results from current research will give us an insight into effective 360-degree 2D UI display designs that might be used in security and safety, entertainment, surveillance, or driving simulation purposes