This paper undertakes a hauntological reading of time and existence, in Amitav Ghosh’s Gun Island (2019). Hauntology, Jacques Derrida’s portmanteau coinage (haunt-plus-ontology, or haunted-ontology, Spectres of Marx, 1994), is a concept that avers that “this instant…is not docile to time, at least to what we call time.” Therefore positing existence as something of “doubtful contemporaneity,” simultaneously contemporaneous and intergenerational, making the present non-contemporaneous, detecting in the present both past and future. Unlike Heidegger’s “care” where the individual is finitely contained within death’s horizon, hauntology ideates a collective global existence beyond the “living present,” enfolding “the ghosts of those who are not yet born or who are already dead.” Not to be confused with cliched ghosts, hauntology, holds time and existence as “spectral” because “effective presence” (norm, law, hegemonic discourse) and its “other” (disarticulated, invisibilised, spectralised presence- as-non-present) both constitute our existence. Gun Island, often called the “Bengali Da Vinci Code,” foregrounds 21st century’s human and eco-displacements inside a detective story. Its central query, “Desh Koi” (where’s home?), problematises our current existence into one of identitylessness, most evident in current refugee/migrant crises as-well-as ecological disasters. Ghosh avers that these are fuelled by disavowed criminal nexus between corporates, smuggling syndicates, and governments/regimes, whose closest analogues are archaic systemic oppressions, thus gridlocking us into a perennially violently unequal world. This paper asks why Ghosh theorises our contemporary constitutedness thus. It argues that Ghosh, in seeing our post-fordist contemporaneity as continuums of archaic habits of systemic injustices, considers our 21st century temporality as a cancellation of future, thus liquifying the concept of a homogeneous, empty, calendar, or teleologically linear time. Detective fiction, with its meta-reading detective figure and analeptic closure of the narratorial logico-temporal gap provides a structural shorthand for Gun Island’s braiding of past, present, and future, where medieval Bengal, and Venice, contemporary India, America, and Italy, coalesce, to detect our existences as globally interconnected continuums of transhistoric, transcultural processes, which manifest as temporal bleeds, indexing hegemonic injustices, despite their systemic disarticulations. Like Giorgio Agamben, Ghosh too believes that the hidden foundation of all regimes lies in the prestidigitated inner solidarity between imperii arcana (archaic sovereignty), democracy and totalitarianism (Agamben, Homo Sacer). Gun Island, limns these disavowed processes and avers that our conventional empirical sense-making systems remain short-sighted in noticing these connections. Ghosh asserts that until the spectral, that-is, spectres and spectre-making (spectragenic) processes, are visibilised, our identities and politics will remain congealed in unequal, violent relatedness, and time will bring forth nothing new. Gun Island imagines a just world fuelled by a collective awareness of equality without leaving anyone out. This entails transitioning from the tyranny of presences to an awareness of the spectral third, beyond the present/absent, seen/unseen totalising binaries. This third, a clarifying agent for the ineffable failure of our future, requires a spectral historiography wherein time and being is placed outside commonplace tempero-spatial fixities. This paper therefore studies Gun Island’s treatment of existence and temporality as attempts towards historicising those no-longer- here, and those yet-to-be.Keywords- Spectres, Hauntology, Detective Fiction, Temporality, Absent presences.

English

(SoHSS) School of Humanities and Social Sciences

Re-Temporalising the Cultural in India

Haunted Time in Amitav Ghosh's Gun Island

The chapter traces the development of Recalling Jewish Calcutta, an early example of a digital public memory project that houses artefacts pertaining to the history and memory of the Jewish community in the city of Calcutta. As a collaborative project, it sees the active participation of independent researchers, academics with institutional affiliations, students, and the wider public. In the building of this project, several lessons are learnt; this chapter provides a reflection on those issues and locates three pedagogical moments to illustrate the importance of practice-based scholarship. The first discusses the value of digital collections and the need for openness in scholarship; the second focuses on the transition of the digital humanist from a user of electronic resources to a maker of electronic collections; the third reminds us of the fragility of digital resources and highlights the need for infrastructural support.

Practices of Digital Humanities in India: Learning by Doing

Jewish Calcutta, Recalled: Lessons From Building a Digital Public Memory Resource

Selecting a business site is a significant decision in managing a commercial organization. It becomes more complex in countries like India, where population density is high, and it is tough to distribute services fairly. With this difficulty, investing in a location without a thorough survey is challenging. This study aims to provide an information map that can help choose the optimal neighborhood for a particular business. The research aims to use data science powers to provide an unsupervised machine-learning approach to offer a recommendation-based solution that suggests the best location for a given use case. The objective of this paper is the in-depth analysis for site-finding that includes clustering regions based on various factors such as geographical locations (like schools, bus and railway stations, and hospitals), sanitation, distance, footfall, and other entities. The idea is to obtain the user's use case query and then create hotspots on the map that show the most suitable locations where the specific use case will benefit the stakeholders the most. We need to identify neighborhoods where there may be an unsatisfied need for Indian restaurants. Additionally, we require places that are uncrowded and have little competition. If the first two requirements are satisfied, we would also like a location close to a prominent city neighborhood. The advantages of each area will then be clearly expressed so that stakeholders can choose the best possible final location.

Computer Science and Engineering

(SoE) School of Engineering

2024 IEEE International Conference on Contemporary Computing and Communications

Location-Based Ideal Site Selection using Clustering

This research explores the intricate relationship between weather-related variables and the transmission dynamics of two pivotal diseases: COVID-19 and dengue. Our primary goal is to advance our understanding of how climate factors influence the occurrence and spread of these diseases through a comparative analysis. In the context of the pressing need for accurate prediction systems, our research aims to contribute to the refinement of public health planning and decision-making strategies. In this work, our findings underscore the superiority of certain models in predicting epidemic outbreaks. Notably, Random Forest consistently demonstrated exceptional performance across all metrics for both COVID-19 and Dengue predictions. The model exhibited superior accuracy, precision, and correlation with actual values.

Asian Conference on Intelligent Information and Database Systems

Predicting Epidemic Outbreak Using Climatic Factors

World University root node

Mechanical Engineering

Tuijin Jishu/Journal of Propulsion Technology

Application of Artificial Intelligence to Measure the Productivity of an Assembly - Line Worker

Herb-based extracellular vesicles (EV), inherently replete with bioactive proteins, RNA, lipids, and other medicinal compounds, are noncytotoxic and uniquely capable of cellular delivery to meet the ever-stringent challenges of ongoing clinical applications. EVs are abundant in nature, affordable, and scalable, but they are also incredibly fragile and stuffed with many biomolecules. To address the low drug binding abilities and poor stability of EVs, we demonstrated herb-based EVs (isolated from neem, mint, and curry leaves) conjugated with chitosan (CS) and PEGylated graphene oxide (GP) that led to their transformation into robust and efficient vectors. The designed conjugates successfully delivered estrogen receptor α (ERα1)-targeting siRNA to breast cancer MCF7 cells. Our data revealed that neem-based EV-CS-GP conjugates were most efficient in cellular siRNA delivery, which could be attributed to hyaluronic acid-mediated recognition of neem EVs by MCF7 cells via CD44 receptors. Our approach shows a futuristic direction in designing clinically viable, sustainable, nontoxic EV-based vehicles that can deliver a variety of functional siRNA cargos.

Chemistry

(SoNS) School of Natural Sciences

ACS Applied Bio Materials

Herb Extracellular Vesicle-Chitosan-PEGylated Graphene Oxide Conjugate Delivers Estrogen Receptor α Targeting siRNA to Breast Cancer Cells

Diffusion tensor imaging (DTI) is a non-invasive technique for analyzing the movement of water in the brain. However, the precision of measurements required for tracking white matter pathways can lead to long scan times, which can be challenging for some patient populations such as pediatric patients. To address this issue, researchers have been experimenting with deep learning techniques for faster estimation of DTI parameters, which are helpful in neurological diagnosis, of diffusion-weighted images. Our proposed solution is a transformer neural network-based approach for fast estimation of diffusion tensor parameters using sparse measurements. While there have been attempts to address this problem, our proposed model handles both scalable and generalized estimation of DTI parameters using multiple sparse measurements. Through experimentation on the Human Connectome Project (HCP) Young Adult benchmark dataset, our proposed model demonstrated state-of-the-art results in terms of fractional anisotropy (FA), axial diffusivity (AD), and mean diffusivity (MD) when compared to traditional linear least square (LLS) fitting and 3D U-Net model with input size (3D U-Net16).

Neural Computing and Applications

SwinDTI: Swin transformer based generalized fast estimation of diffusion tensor parameters from sparse data

9th International Conference on Internet of Things, Big Data and Security (IoTBDS 2024)

Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition

IFAC-PapersOnLine

Controlling and Optimization of On-time Delivery for Assemble-to-order Production System

Alzheimer disease is one of the most common neuro-degenerative diseases, with an estimated 6.2 million cases in the United States. This research article investigates the potential of Transformer-based deep learning techniques to accelerate the processing of diffusion tensor imaging (DTI) measures and improve the early diagnosis of Alzheimer disease (AD) using sparse data. Diffusion Weighted Imaging (DWI) is a time-consuming process, with each diffusion direction taking between 2-5 minutes, and at least 40 diffusion directions are needed for routine clinical diagnosis, which needs scanning duration exceeding 3 hours for each patient. By leveraging the attention mechanism, our proposed model generates quantitative measures of fractional anisotropy (FA), axial diffusivity (AxD), and mean diffusivity (MD) using 5 and 21 diffusion directions, making it useful for clinical diagnosis through reduced scanning time of more than half. Our experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed model outperforms the traditional linear least square method, achieving accurate quantitative measurement of FA, AxD, and MD scores for early diagnosis of AD patients from healthy controls using sparse diffusion directions. Our analysis highlights the potential of Swin-Transformer attention-based deep learning framework to improve the early diagnosis and treatment of Alzheimer’s disease.

Proceedings of Machine Learning Research

Early Diagnosis of Alzheimer through Swin-Transformer-Based Deep Learning Framework using Sparse Diffusion Measures

A mononuclear air-stable and water soluble six coordinated cadmium (II) complex [Cd(HL)6](ClO4)2 (1) with bi-dentate phenol based ligand [HL = 2-(benzylimino)methyl)phenol] have been synthesized. The complex has been characterized by various spectroscopic techniques. The solid-state structure, by single crystal X-ray diffraction, revealed that Cd(II) complex crystallizes in trigonal space group R-3. Interestingly, single crystal Xray structure data show that the Schiff base in the complex is present in a zwitterionic form and coordinated through the phenolic oxygen atom only, providing the first example of more than four monodendate Schiff base in zwitterionic form to be coordinated with the central metal ion. Cyclic voltamogram of the cadmium (II) complex (1) exhibits a quasi-reversible oxidation peak in the positive potential region. The redox process occurs due to the generation of phenoxyl radical from the pheolate group of the attached ligand with the central metal ion. Luminescence spectra show a strong emission band around 500 nm. Additionally, the mononuclear cadmium complex (1) shows catechol oxidase activity in methanol under aerobic conditions. The turnover number of the process is 2.766 × 103 h–1. The oxidation process occurs via formation of ligand oriented phenoxyl radical species.

SNU Management

Journal of Molecular Structure

A protonated imine-based water-soluble cadmium complex with N, O donor chelating agent: Synthesis, structure, fluorescence efficiency and catecholase activity

Frontotemporal dementia (FTD) is a devastating neurodegenerative disorder that primarily affects the frontal and temporal lobes of the brain, leading to cognitive decline and behavioral changes. Early and accurate diagnosis of FTD is crucial for initiating timely interventions and providing appropriate care to patients. In the opinion of the experts, about 12-22 persons out of the population of 100,000 persons experience FTD. That means between 1.2 million and 1.8 million people have it worldwide. This research paper proposes a novel deep learning framework that utilizes sparse diffusion measures extracted from neuroimaging data to aid in the early diagnosis of Frontotemporal Dementia. The proposed model leverages the power of deep learning techniques to automatically learn relevant features from the data and effectively distinguish between healthy individuals and those with FTD. The experimental results demonstrate the promising potential of the proposed approach in improving FTD diagnosis and paving the way for future research in this area.

IEEE/CVF International Conference on Computer Vision

Deep Learning Framework using Sparse Diffusion MRI for Diagnosis of Frontotemporal Dementia

This paper aims to improve the identification of taxi hotspots in spatio-temporal space. We propose an approach that combines ensemble-weighted degree and node entropy measures within a dominating set to enhance the identification of taxi hotspots. First, we construct a graph representation of the spatio-temporal space, where nodes represent potential taxi hotspots. The weighted degree of each node is computed by considering the weights of its adjacent edges, which correspond to the distances between the two adjacent nodes on the road network. This measure quantifies the importance and connectivity of a node. We calculate node entropy to capture the level of uncertainty and randomness associated with each node. The entropy measure provides insights into the diversity of each potential hotspot, helping to distinguish between highly active and less predictable areas. We then integrate the ensemble weighted degree and node entropy measures into a dominating set approach. By iteratively selecting the nodes with the highest combined scores, we construct an optimal set of taxi hotspots that effectively cover the spatio-temporal space while considering both connectivity and uncertainty. The proposed approach enables a more comprehensive identification of taxi hotspots, taking into account the connectivity and importance of potential locations and their level of uncertainty. To evaluate the effectiveness of the proposed approach, we conducted experiments using New York taxi trip data. The results demonstrate that the ensemble weighted degree and node entropy measures enhance the identification of taxi hot spots compared to traditional dominating set methods.

<a href="https://link.springer.com/chapter/10.1007/978-3-031-48316-5_31">Enhancing Taxi Placement in Urban Areas Using Dominating Set Algorithm with Node and Edge Weights</a>

Congress on Smart Computing Technologies

Intelligent Systems in Production Planning for an Assemble-to-Order Production System

This paper examines the ambivalences, and tensions of the imperial project encoded in 19th century botanical codification of poisonous plants in British colonies, through the materiality of alkaloids in Sherlock Holmes stories. I argue that these tales reveal the orientalising gaze of botanical codifications which merged in the case of study of alkaloids with medico-criminal discourse on plants, that grouped indigenous plants, and indigenous people under the canopy of toxicology, constructing the native population as criminal and primitive, thereby giving imperial crimes a clean-chit. I contend that this bundling of forensics and the sociological was damage control of the faltering British self-image in the wake of Britain’s anti-opium movement and atrocities in the Boer war. The Holmes mysteries link phytotoxins to colonies’ cultural practices, thus reattaching criminality to the colonies, to re-institute British selfhood in metropolitan consciousness by creating via the flora of the colonies, a criminal Other. However, I point out that the institutional weight of imperial machinery could not fully contain the unease of Britain’s colonial contact. The anxiety of reverse-colonialism (1857 Sepoy Mutiny), continued to haunt the British psyche, appearing as exotic alkaloids that leave no trace, and can only be deduced by Holmes’ genius.&nbsp; Such that, plant poisons in these tales simultaneously repair British loss of self-prestige, as-well-as, self-subvert it. I study homicidal poisoning tales, “The Adventure of the Devil’s Foot” and “The Sign of the Four,” as stories of revenant, disavowed (thereby spectral, intangible) imperial unease, using Jacques Derrida’s political idea of systemic evils as spectral (Specters of Marx, 1994). The plant-poisons of these tales, I argue, are conceptual tools, revealing spectres (not to be confused with cliched ghosts) of imperial anxieties, disturbing the stability of imperialism’s coerced archiving, gathering us under its revealing spectral shade.

The Research Post

Under a Spectral Shade: Plant Poisons and Imperial Anxieties in Arthur Conan-Doyle’s Sherlock Holmes Mysteries

Food security is a global challenge that aligns with several Sustainable Development Goals (SDGs), including SDG 1 - “No Poverty”, SDG 2 - “Zero Hunger,” SDG 3 - “Good Health and Well-being,” SDG 13 - “Climate Action,” and SDG 15 - “Life on Land.”. To effectively address this issue, a convergence of agriculture and technology is crucial, incorporating precision agriculture, sustainable agriculture, bio-economy and advanced technologies such as machineries, Artificial intelligence-meachine learning&nbsp;and geospatial technology. Recent trends in food security worldwide have witnessed the adoption of technological advancements. However, it is important to consider biodiversity when implementing and adopting technological advancements. The integration of technology in agricultural practices aim to reduce chemical usage&nbsp;in farms, while increased production remains the main objective of smart farming and Precision Agriculture. By minimizing the use or implementing localized approaches for chemicals on farms, we can preserve soil-faunal diversity, which is at risk along with targets due to excessive applications of pesticides, herbicides, and insecticides. Geospatial technology plays a significant role by leveraging remotely sensed images acquired through satellites, aerial platforms, and GPS-tagged drones to enhance agricultural practices, improve crop growth and increase crop yields. Through geospatial technology, we can map and monitor soil and crop conditions, and effectively conserve, protect, and manage biodiversity on a global scale. The applications of remote sensing technology consider ecological and environmental parameters, soil factors, crop conditions, and plant-soil diversity to optimize yields and agricultural productivity. Additionally, the evaluation of various agricultural practices such as sustainable agriculture, bio-economy, using technology can contribute to increased yields, profitability, and biodiversity conservation as central components of these practices. This study emphasizes that achieving the SDG objectives is attainable through agriculture, which has direct and indirect connections to all SDGs. Geospatial technology plays a vital role in supporting the objectives at spatial and temporal scales by facilitating agricultural practices, ensuring&nbsp;food security, preserving biodiversity, monitoring soil and crop conditions, and promoting sustainable development.

Center for Environmental Sciences & Engineering

Sustainable Development

Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals

The present study is taken up to record variations in the extent of area of two polygons–(i)a ground measured area of a university campus, (ii)enclosing the Ganga basin and a polygon covering (iii)India to examine the changes in both shape and area–under different map projections with various parameters. The exercise brought forth interesting results. Depending on final ranks worked out based on minimum differences in extent of areas and shape distortion in the case of India, it is suggested to adopt either (i) LCC projection with Everest India-Nepal datum, First Standard Parallel (FSP) 24.50, Second Standard Parallel (SSP) 28.50, Latitude of Origin (LO) 16.253259, Central Meridian (CM) 80.8749 or (ii) LCC projection with WGS 84 datum, FSP 24.50, SSP 28.50, LO 16.253259, CM 80.8749 or (iii) Polyconic with Everest India-Nepal datum, CM 84.50, LO 13.00, for mapping both smaller areas on larger scales and larger areas on smaller scales.

Geocarto International

Need for Pan-India Compatibility of Geospatial Databases in Terms of Map Projections and Parameters

Procedia CIRP

<a href="https://www.sciencedirect.com/science/article/pii/S2212827123002652">A CoBot Reinforcement Framework To Facilitate Assembly line Workers</a>

The promise of artesunate’s (ART) role in anticancer properties steers a focus on drug repurposing. Potential nanocargo heterostructures, with each component having a specific role, are required for effective drug delivery. Here, we have developed a nanoformulation based on nanodiamonds studded with iron oxide that exhibits exceptional biocompatibility and effective cellular delivery of ART. The current drug delivery formulation has proven improved efficiency of the drug in generating higher reactive oxygen species (ROS), resulting in DNA damage and subsequent cellular apoptosis.

ACS Materials Letters

Multifunctional Multicomponent Highly Biocompatible pH-Responsive Iron-Oxide Embedded Nanodiamond Cargo for Artesunate to Inhibit Cancer Growth

The present study discusses the synthesis of yttrium and tin-doped barium titanate using sol–gel synthesis route, and explores their structural, morphological, and ferroelectric properties. Yttrium nitrate and stannic chloride were used to dope barium titanate synthesized using barium acetate and titanium butoxide. The phase of the sol–gel prepared samples with composition Ba1-1.5xYxTi1-zSnzO3; x = 0.0, 0.005, 0.01 for each z = 0.0, and 0.05 was investigated using X-ray diffraction (XRD). The Raman spectra were obtained to investigate their lattice vibrations. The doping-induced variation in the morphology and grain size of these samples was studied using SEM micrographs, which revealed an increase in grain size with tin-doping and grain growth inhibition caused by yttrium incorporation. The DC conductivity of these samples was measured from RT to 350 °C. The samples were also inspected for their ferroelectric properties. The maximum dielectric constant of 9586 (1 kHz) at its Curie temperature and 5611 at 40 °C was observed for 0.5 mol% yttrium and 5 mol% tin co-doped sample which also displayed the highest density. The P-E hysteresis behavior and piezoelectric coefficient (d33) were also studied. [Figure not available: see fulltext.]. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Physics

Journal of Sol-Gel Science and Technology

Cumulative effect of yttrium and tin co-doping on the structural and ferroelectric properties of sol–gel derived barium titanate

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.