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291 results on '"P., Sajitha"'

1. Quantum thermal machine regimes in the transverse-field Ising model

Author

Sajitha, Vishnu Muraleedharan, Santra, Bodhaditya, Davis, Matthew J., and Williamson, L. A.

Subjects
Quantum Physics, Condensed Matter - Quantum Gases, Condensed Matter - Statistical Mechanics
Abstract

We identify and interpret the possible quantum thermal machine regimes with a transverse-field Ising model as the working substance. In general, understanding the emergence of such regimes in a many-body quantum system is challenging due to the dependence on the many energy levels in the system. By considering infinitesimal work strokes, we can understand the operation from equilibrium properties of the system. We find that infinitesimal work strokes enable both heat engine and accelerator operation, with the output and boundaries of operation described by macroscopic properties of the system, in particular the net transverse magnetization. At low temperatures, the regimes of operation and performance can be understood from quasiparticles in the system, while at high temperatures an expansion of the free energy in powers of inverse temperature describes the operation. The understanding generalises to larger work strokes when the temperature difference between the hot and cold reservoirs is large. For hot and cold reservoirs close in temperature, a sufficiently large work stroke can enable refrigerator and heater regimes. Our results and method of analysis will prove useful in understanding the possible regimes of operation of quantum many-body thermal machines more generally., Comment: 9 pages, 6 figures

Published
2024

9. A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network

Author

P. Sajitha, A. Diana Andrushia, N. Anand, M.Z. Naser, and Eva Lubloy

Subjects
Pomegranate, Fruit disease detection, Deep learning, Hybrid OACapsNet, Post-harvest technique, Information technology, T58.5-58.64, Ecology, QH540-549.5
Abstract

In 2022, the production rate of pomegranate is estimated at approximately 4.8 million metric tons. Unfortunately, these fruits are susceptible to many different kinds of diseases caused by bacterial, viral, and fungal infections. Such diseases can have a major negative impact on fruit quality, production, and the profitability of pomegranate cultivation. Nowadays, several machine learning and deep learning methods are used to identify pomegranate fruit diseases automatically and effectively. In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. This can improve disease identification accuracy, enabling more efficient disease control, lower crop losses, and better resource management. The proposed work introduces an intelligent deep learning-based approach for accurately detecting pomegranate diseases, begins with Improved Guided Image Filtering (Improved GIF) and resizing to pre-process fruit images, followed by feature extraction (shape, color, texture) using GLCM and GLRLM to streamline classification. Extracted features are then fed into a novel Hybrid Optimal Attention Capsule Network (Hybrid OACapsNet), which classifies the images as normal or diseased, conditions such as bacterial blight, heart rot, and scab. Our analysis indicates that the proposed classifier has a classification accuracy of 99.19 %, precision of 98.45 %, recall of 98.41 %, F1-score of 98.43 %, and specificity of 99.45 % compared to other techniques. So this approach offers a framework, which is a feasible solution for automated detection of diseases in fruits, thereby benefiting farmers and supporting their farming operations.

Published
2024
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12. Dizzy: Large-Scale Crawling and Analysis of Onion Services

Author

Boshmaf, Yazan, Perera, Isuranga, Kumarasinghe, Udesh, Liyanage, Sajitha, and Jawaheri, Husam Al

Subjects
Computer Science - Cryptography and Security, Computer Science - Social and Information Networks
Abstract

With nearly 2.5m users, onion services have become the prominent part of the darkweb. Over the last five years alone, the number of onion domains has increased 20x, reaching more than 700k unique domains in January 2022. As onion services host various types of illicit content, they have become a valuable resource for darkweb research and an integral part of e-crime investigation and threat intelligence. However, this content is largely un-indexed by today's search engines and researchers have to rely on outdated or manually-collected datasets that are limited in scale, scope, or both. To tackle this problem, we built Dizzy: An open-source crawling and analysis system for onion services. Dizzy implements novel techniques to explore, update, check, and classify onion services at scale, without overwhelming the Tor network. We deployed Dizzy in April 2021 and used it to analyze more than 63.3m crawled onion webpages, focusing on domain operations, web content, cryptocurrency usage, and web graph. Our main findings show that onion services are unreliable due to their high churn rate, have a relatively small number of reachable domains that are often similar and illicit, enjoy a growing underground cryptocurrency economy, and have a graph that is relatively tightly-knit to, but topologically different from, the regular web's graph.

Published
2022

18. Smart farming application using knowledge embedded-graph convolutional neural network (KEGCNN) for banana quality detection

Author

P. Sajitha, A. Diana Andrushia, Nour Mostafa, Ahmed Younes Shdefat, S.S. Suni, and N. Anand

Subjects
Knowledge graph, Graph convolutional neural network, Smart farming, Banana, Quality detection, Agriculture (General), S1-972, Nutrition. Foods and food supply, TX341-641
Abstract

The appearance of fruits is crucial in their quality grading and consumer choices. Colour, texture, size, and shape determine fruit quality. Existing computer vision systems have been implemented for external quality control, relying on observations for fruit grading and classification. Banana quality detection systems, which employ advanced algorithms and sensors to evaluate the ripeness and general quality of bananas throughout their life cycle, are an innovative application of smart farming technology. In this proposed system, Knowledge Embedded-Graph Convolutional Neural Networks (KEGCNNs) are employed to classify and grade banana fruit. The approach aims to detect banana fruit quality by converting banana images into a knowledge graph, applying knowledge embedding to transform them into a continuous vector space, and using Graph Convolution Neural Networks (GCNNs) to analyze the graph structure and make accurate detections. KEGCNNs are especially useful for detecting the quality of banana fruits because they provide a form for capturing the contextual interactions between distinct nodes. KEGCNNs can learn from the data within the graph in an unsupervised manner, allowing them to use the knowledge inherent in the graph structure. KEGCNNs enable more accurate and efficient diagnosis of banana quality as they can discover patterns in data that conventional machine learning algorithms cannot. The suggested technique demonstrates an impressive performance score, indicating its suitability for detecting the quality or grade of banana fruit.

Published
2023
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29. Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer’s disease

Author

T., Premkumar, Katta, Bhavana, Lulu S., Sajitha, and Sundararajan, Vino

Abstract

AbstractAlzheimer’s disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer’s disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of −7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of −7.7 kcal/mol and −6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma

Published
2024
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31. Psychological Approaches to Learner Centered Curriculum in Kerala

Author

Nath, Baiju K. and Sajitha, P. S.

Abstract

The major curricular innovations that have taken place in Kerala secondary education system in recent years is so revolutionary. This paper examines the basic psychological theories of the learner centered curriculum of school education in the state of Kerala. Initially the curriculum was based on the principles of behaviorism. The curriculum then revised in the light of social constructivism, and further incorporated the principles of Critical pedagogy. The Curriculum at this stage is designated as Issue based curriculum, where social issues are the backbone. Eight social issues were identified and curriculum was developed in a critical approach to social issues relevant in a global setup.

Published
2010

37. Research Perspectives in Issue Based Curriculum and Critical Pedagogy

Author

Nath, Baiju K. and P. S., Sajitha

Abstract

The National Policy on Education (NPE, 1986) of India proposed the National Curriculum Framework (NCF) as a means of evolving a national system of education, recommending a core component derived from the vision of national development enshrined in the constitution. The Kerala Curriculum Framework (KCF-2007) draws upon the National Curriculum Framework of 2005 (NCF - 2005) introduced critical pedagogy and issue based approach in education from the academic year 2008-09. While implementing a new approach in educational system, it requires a thorough research base for its success. Even though a lot of works have been conducted in critical pedagogy, majority were in adult learning; and researches in issue based approach are very less. In order to transact the new curricular approach in a successful manner, researches in issue based approach and critical pedagogy is to be encouraged. This paper tries to identify the researchable areas in issue based approach and critical pedagogy. Critical pedagogy should be perceived as pedagogy for existence or survival. Hence what is critical will be changed by age level, experience, subject of study, type of institutions, etc. Access, equity, and quality are the basic dimensions of any type of education. Conceptual analysis and prioritisation of competencies at each level is the basic issue to be resolved. Though access in terms of institutions is realized, access in terms of resources is not even estimated. The critical concepts required and pedagogy to attain that of elementary level child will be entirely different from that of secondary level pupil.

Published
2009

39. A detailed practical and theoretical study of the adsorptive potential of synthesised ceria impregnated activated graphite for aqueous Se(IV) and Te(IV) removal: Process optimisation, characterisation and mechanistic approach

Author

Nair, Sajitha, Kumar, P.Naveen, Bose, Roopa, Durani, Smeer, and Pandurangappa, M.

Abstract

A cost-effective, simple, and earth friendly nanocomposite adsorbent was synthesized by in - situ penetration of ceria nanocrystals on graphite to remove aqueous selenium (IV) and tellurium (IV). Batch experiments optimize parameters such as pH, temperature, sorbent dosage and equilibration time of 30 minutes. The adsorbent exhibited the maximum adsorption capacities of 144.5 mg/g for Te(IV) and 29.3 mg/g for Se(IV). BET analysis revealed a specific surface area of 16.44 m²/g, an average pore diameter of 4.24 nm, and a high pore volume of 0.0519 cm³/g. The adsorbent was characterized before and after adsorption by SEM-EDX, XRD and FTIR. The sorption data could be well interpreted by the Langmuir model for both Se and Te and by the DR isotherm for Te(IV) at ambient temperature. Kinetic empirical data correlated positively with pseudosecond order for both Se and Te and the Elovich model for Te at room temperature. Kinetics and equilibrium isotherms suggested that the adsorption occurred through surface adsorption using electrostatic bonding along with physisorption involving steady diffusion and accommodation of analyte ions into mesopores and defects of the adsorbent. Chemisorption by electron transfer of the oxyanions of Se/Te with Ce3+/Ce4+of the cerium impregnated graphite (CIAG) and ion exchange with functional groups on the activated graphite edges were other probable mechanisms. The adsorbent demonstrated the ability to be regenerated and reused over multiple cycles, with minimal interference from coexisting ions emphasizing its selectivity. All measurements were conducted in triplicate with the relative standard deviation (RSD) below 5 %. The optimised method was successfully applied in natural water samples from various sources, demonstrating strong potential for scaling up to field applications.

Published
2024
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