IMU Institutional Digital Repository
Welcome to the Institutional Digital Repository of IMU. The Central Library of IMU maintains this repository and provides metadata and full-text files of all the IMU Publications. The repository aims to preserve and disseminate access to IMU publications, including Articles, Working Papers, Books, Book Chapters, Project Reports, and more authored by Faculty, Experts, Research Scholars, and Students enrolled in IMU. For access to the repository or any clarifications, do not hesitate to get in touch with the Central Library at:repository@imu.ac.in
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Recent Submissions
Advanced hybrid neural network techniques for minimizing gas turbine emissions
(World Journal of Engineering | Emerald Publishing, 2024-10-08) Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra, Manashi Chakraborty
Purpose Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS). Design/methodology/approach The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies. Findings The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R ² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R ² of 0.887 and a slight overestimation bias of +0.01. Research limitations/implications While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research. Practical implications Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs. Social implications Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment. Originality/value This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.
Thermal and Friction Characteristics of Laminar Flow through Square and Rectangular Ducts with Transverse Ribs and Twisted Tapes with and without Oblique Teeth
(Journal of Enhanced Heat Transfer, 2010-01-01) Parthajit Pal, Sujoy Kumar Saha
Thermal and friction characteristics of a laminar flow through square and rectangular ducts with periodic transverse ribs and different types of twisted tapes with and without oblique teeth have been studied experimentally. Correlations for predicting the friction factor and Nusselt number have been developed and performance has been evaluated. Although both friction factor and Nusselt number are the highest for the case of all types of twisted tapes with oblique teeth in combination with transverse ribs, the performance evaluation has shown that the ducts with transverse ribs and regularly spaced twisted-tape elements with oblique teeth are the best and this is recommended. However, where the pressure drop in a heat exchanger is a small fraction of the total system pressure drop; the heat transfer being higher, full-length and short-length twisted tapes in combination with transverse ribs can be recommended since the heat exchanging surface area requirement will be less.
Probiotics, psychobiotics, and postbiotics: a therapeutic modality for the management of schizophrenia
(Nutritional Neuroscience| Taylor & Francis, 2025-10-01) Sumel Ashique, B. Latha, Biplab Debnath, Dinesh Kumar Chellappan, Joy Das, Utpal Bhui, Mohhammad Ramzan, Neha Sharma, Bimlesh Kumar, Javedh Shareef, Uttam Prasad Panigrahy, Md Sadique Hussain
Schizophrenia is a debilitating, chronic neuropsychiatric disorder, a multifactorial disorder combining genetic, neurodevelopmental, immunological, and environmental factors. Common antipsychotic treatments may be effective against positive symptoms, but still lack when dealing with negative symptoms, cognitive defects, and side effects of medication. Recent innovations show how the gut-brain axis is an important modulator of neuropsychiatric health, identifying microbial dysbiosis as a cause of schizophrenia. This review examines the therapeutic potential of such treatments of probiotics, psychobiots, and postbiotics as an adjunctive or alternative treatment targeting the way of modulating neuroinflammation, neurotransmitter synthesis, experience, and maintenance of blood–brain barrier integrity. Probiotics, which are live beneficial microbes, have immunomodulatory and neuroactive effects; psychobiotics, a subclass that has specific mental effects, modify stress-response systems and neurotrophic factors. Postbiotics, consisting in turn of microbial metabolism like short-chain fatty acids, present improved safety and stability with anti-inflammatory and antioxidant functions. Available clinical and preclinical evidence suggests the ability of these agents to attenuate the symptoms of schizophrenia and cognitive impairment, as well as to increase the tolerability of treatment. Regarding the conclusive presumptions, however, strain-specific variability and inconsistent methodologies confined by the sparse large-scale trials limit them. New technologies of nanocarrier systems, artificial intelligence, and personalized microbiome profiling might provide the best precision of the therapy. In this review, pitfalls in mechanistic insights, progress reports on translational studies, and future research prospects are deconstructively examined to support microbiota-based interventions as promising paradigms of holistic schizophrenia management.
Performance and emission analysis of diesel engine operating with hybrid nanoparticles dispersed Madhuca longifolia biodiesel
(Nanotechnology for Environmental Engineering | Springer, 2025-04-30) Balaji Ashok Kumar Bylapudi, Venkata Subbaiah Kambagowni, Jaikumar Sagari
This study investigates the performance and emissions of a single-cylinder diesel engine fueled with a ternary fuel mixture of hybrid nanoparticles such as ferric chloride (FeCl3) and graphene. The fuel mixture consists of 70% diesel, 20% Madhuca longifolia biodiesel and 10% ethanol. Hybrid nanoparticles at concentrations of 50 and 75 mg/L were added to the ternary fuel mixture, with Cetyltrimethylammonium Bromide (CTAB) and QPAN added in a 1:1 ratio to increase the stability of the nanoparticles. The stability was measured according to the principle of photo spectroscopy and the fuel properties were evaluated according to American Society for Testing and Materials (ASTM) standards. The experimental methodology included varying the injection pressures to 200, 225, and 250 bars, along with different loads of 3, 6, 9, and 12 kgf, in a diesel engine to evaluate its performance and emission characteristics. The results showed that the addition of nanoparticles led to an improvement in brake thermal efficiency (BTE) and a reduction in brake specific fuel consumption (BSFC) as well as a reduction in carbon monoxide (CO), unburnt hydrocarbons (UHC), oxides of nitrogen (NOx), and smoke opacity compared to diesel and ternary fuel blend. Of all the fuel samples, D70B20E10NPs75 mg/L QPAN75 mg/L performed best, achieving a BTE of 33.26% and a BSFC of 0.206 kg/kWh, while CO, UHC, NOx, and smoke opacity at an injection pressure of 250 bar were 0.019%, 21 ppm, 833 ppm, and 36.25% respectively.
Characterization of sustainable natural fiber reinforced geopolymer composites
(Polymer Composites| Society of Plastics Engineers| Wiley, 2022-04-01) M. G. Ranjithkumar P. Chandrasekaran G. Rajeshkumar
This article focuses on the investigation of properties of Phoenix sp. fiber based geopolymer composites. Control samples (0 wt.%) and fiber reinforced samples (1, 2, 3, and 4 wt.%) with different quantities were produced and determined
their physical, mechanical, morphological, ultrasonic pulse velocity, water absorption, thermal conductivity and fracture toughness properties. The outcomes show that the incorporation of Phoenix sp. fibers to geopolymer improved the splitting tensile (1.28–2.35 MPa), compressive (27.85–32.18 MPa) and flexural strengths (3.34–6.53 MPa). By contrast, as the fiber loading increased to 4 wt.%, the workability and bulk density of geopolymer decreased to 86% and 13%, respectively. Furthermore, a linear relationship was evidenced between the bulk density and thermal conductivity, as well as ultrasonic pulse velocity and compressive strength. Due to the hydrophilic character of Phoenix sp. fibers, the water absorption increased as the fiber content increases. Due to the local mechanisms that control the bridging activity, the addition of Phoenix sp. fibers improved the fracture toughness of the composites.