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Prediction of indoor air circulation of residential room with adaptation of solar chimney using numerical technique
With the exponential increase in consumption of electrical power during the summer season by household, there is a great need for households to withhold sustainability. To maintain the temperature of the household a passive heating and cooling system is used i.e. Solar Chimney. Ventilation, through a natural convection process, is gaining a lot of attention to be an alternative technique for mechanical air conditioning ventilation because of its reduced power usage when compared to the external cooling devices used in residential buildings of hot regions. The present study, involve solar chimney of horizontal and vertical designs in comparison with different width and height. The following paper studies the effect of a solar chimney on the indoor thermal behavior using Numerical Technique for a prototype of a residential room. The performance on the ventilation velocity and air temperature operation inside the room with varying air gap width is studied based on multiple numerical analysis solutions. The present study deals with two different architectures of a two dimensional model and results have shown that the ventilation velocity has increased to 0.017626444 kg/s and operative air temperature has been decreased by 7.26C for the vertical model while the horizontal model has shown a mass flow rate of 0.018027636 kg/s and a temperature decrease of 9.15C. The most efficient chimney was found to be model 7 which is horizontal solar chimney 3 with an air gap width of 0.05625m and a height of 0.3175 m, when compared to the other models from model number one to six. BEIESP. -
Indian Emotional Alignment Score (I-EAS): A Pilot Framework for Culturally-Aware AI Evaluation in Multilingual Contexts
The current Emotional Intelligence (EI) standards of large language models (LLMs) are predominantly Western, thereby excluding billions of non-Western users from being judged fairly by AI. I-EAS, a new culture-based evaluation system, is grounded in indigenous epistemology and a computational evaluation framework to address a critical gap in AI Fairness. I-EAS takes into account four weighted factors: Emotional Alignment, Cultural Resonance, Coherence, and Code-Switching. It prioritizes cultural appropriateness and maintains a rigorous methodology. The pilot study uses 150 systematically crafted prompts in Hindi, Tamil, and English in diverse Indian cultural scenarios to test three LLMs: Claude Sonnet 4, GPT-4o-mini, and Krutrim-Base. The results highlight the inherent drawbacks of automated cultural assessments, as the weak relationships between human and automated assessments indicate that current NLP models are unable to respond to cultural nuances. The human evaluation was more discriminative than the automated evaluations, as the automated code-switching evaluations were not successful. The limitations of the pilot study include the presence of only one annotator. However, they prove I-EAS as a preliminary methodological and theoretical framework to equitable AI evaluation in culturally diverse situations and the necessity to use hybrid human-computational measures of AI systems in different cultural contexts. 2025 IEEE. -
Process Development for Mass Production of Cordycepin Using Fermentation Technology
Cordyceps is a rare and exotic medicinal genus that has been utilised for generations in traditional Chinese medicine. China, Bhutan, Nepal, the Tibetan Plateau, and the newlinenortheastern parts of India are the main areas where they may be noticed. Cordycepin newline(C10H13O3N5), an adenosine derivative generated naturally by Cordyceps militaris, has essential pharmacological effects. Cordycepin has been shown to have anti-tumor/antiproliferative, anti-metastatic, apoptosis-inducing, anti-malaria, anti-microbial, antifungal, anti-diabetic/hypoglycemic, and anti-inflammatory qualities, among other things. The scarcity of native Cordyceps spp., as well as illicit commerce and a lack of growing methods in natural habitats, limit the supply of this prized fungus for therapeutic applications. As a result, an attempt was made to standardize the technique for large-scale cordycepin manufacturing in a laboratory setting. To measure the cordycepin, analytical methods by using High performance liquid chromatography (HPLC) and Ultraviolet Visible spectroscopy (UV Spec.) were established. Furthermore, different methods were optimised and established to successfully extract cordycepin from biomass. Solid state fermentation (SSF) was performed to grow the fruiting bodies of C. militaris in jars. A novel technology was developed using cotton in the SSF which improved the cordycepin content by 138.42 %. Different liquid culture methods (static, submerged, and mix) were also studied; the Static mode was chosen for future media optimization studies. Initially, different parameters like the effect of pH, light, media volume, and inoculum percentage were optimised that affect the cordycepin production. Further, numerous media components, including carbon newlinesources, nitrogen sources, metals, salts and also the impact of vitamins, amino acids, newlineand adenosine that can influence the cordycepin production were optimized employing the One Factor at a time (OFAT) method. -
Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. 2013 IEEE. -
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model. 2012 IEEE. -
A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images
The classical self-supervised neural network architectures suffer from slow convergence problem and incorporation of quantum computing in classical self-supervised networks is a potential solution towards it. In this article, a fully self-supervised novel quantum-inspired neural network model referred to as Quantum-Inspired Self-Supervised Network (QIS-Net) is proposed and tailored for fully automatic segmentation of brain MR images to obviate the challenges faced by deeply supervised Convolutional Neural Network (CNN) architectures. The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qbits. The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states, wherein the image pixel intensities (quantum bits) are self-organized in between these two layers without any external supervision or training. Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. The proposed self-supervised quantum-inspired network model has been tailored for and tested on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data sets for detecting complete tumor and reported promising accuracy and reasonable dice similarity scores in comparison with the unsupervised Fuzzy C-Means clustering, self-trained QIBDS Net, Opti-QIBDS Net, deeply supervised U-Net and Fully Convolutional Neural Networks (FCNNs). 2020 Elsevier B.V. -
An Efficient Copper-Catalyzed Regioselective One-Pot Synthesis of Pyrido[1,2-a]benzimidazole and Its Derivatives
A facile and effectual regioselective one-pot synthesis protocol has been developed for the construction of pyrido[1,2-a]benzimidazole and its derivatives using Copper(I) bromide as the catalyst, 1,10-phenanthroline as ligand, and K3PO4 (Tripotassium phosphate) as the base in Dimethyl sulfoxide as solvent at 110 C for 12 h. The reaction conditions were optimized by screening various copper catalysts, ligands, solvents, and bases. The substrate scope of the reaction was also carried out with electron-withdrawing and donating functional groups to prepare novel functionalized regioselective benzimidazole compounds in good to excellent yields. All the isolated compounds were characterized by 1H, 13C, and 19F NMR. 2023 Wiley-VCH GmbH. -
Metal and Ligand-Free Approach Towards the Efficient One-Pot Synthesis of Dipyridopyrimidinimine Derivatives
We report a facile, expeditious, user-friendly, and convenient metal-free synthesis employing base catalysis in a one-pot procedure to construct 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives. This protocol involves a domino process leading to the formation of double C?N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron-withdrawing and electron-donating functional groups, yielding novel functionalized stable 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis. 2024 Wiley-VCH GmbH. -
A Simple and Efficient Ligand-Free Copper-Catalyzed C-N Bond Formation of Aryl (Hetero) Halides and N-Heteroaryl Amines
In this protocol, we report a simple, inexpensive, and user-friendly conventional method for C-N cross coupling between aryl/heteroaryl halides and hetero aryl amines using copper iodide as a catalyst in DMSO as a solvent to prepare pyrimidines and pyrazines derivatives. The reaction conditions were optimized by screening in various copper catalysts and bases. The substrate scope of the reaction was also carried out to prepare novel functionalized N-arylated compounds in good yields. 2021 Taylor & Francis Group, LLC. -
Diverse Morphologies of Nb2O5 Nanomaterials: A Comparative Study for the Growth Optimization of Elongated Spiky Nb2O5 and Carbon Nanosphere Composite
Controlled synthesis and design of nanomaterials with intricate morphologies and active phases offer new prospects in harnessing their unique chemical and physical properties for various applications. Herein, a facile and efficient hydrothermal approach is reported for obtaining various complex Nb2O5 nanostructures, including thin sheets, thick flakes, spiky and elongated spiky sea urchin morphologies using urotropin as a growth-directing and hydrolyzing agent in various mixed and pure solvents. The detailed structural and chemical composition, surface morphology and crystallinity of as-synthesized Nb2O5 nanostructures are presented. The urotropin concentration, reaction time, and water-ethanol solvent mixture play a critical role for obtaining the elongated spiky sea urchin morphologies. The spiky Nb2O5 structures show a pseudohexagonal phase with less urotropin content, while thin sheets are obtained with a higher urotropin concentration and are primarily amorphous. These structures undergo transformation in their crystal phase and morphologies during calcination at higher temperatures revealing the active role of urotropin in stabilizing them. A composite of spiky sea urchin Nb2O5-carbon nanospheres (suNb2O5-CNS) is achieved by in-situ growth of Nb2O5 in the presence of CNS without compromising on morphology, phase, and crystallinity. suNb2O5-CNS composite is shown to possess higher charge storage capacity compared to its constituents for supercapacitor applications. 2023 Wiley-VCH GmbH. -
The complexities of home and belonging in the Gulf-Malayalee experience: a close reading of Salim Ahameds Pathemari (2015)
This paper explores the interaction between home, belonging, and migration by closely reading Salim Ahameds 2015 Malayalam film, Pathemari. The paper briefly traces migration history from Kerala to the Gulf and its impact on Keralas housing boom, influencing its socioeconomic and cultural landscape. Through this, the paper examines how Gulf Malayalees navigate the multifaceted and contested concept of home despite being physically and emotionally displacedthe paradox of belonging and unbelonging, in their attempts to secure a material home while working as blue-collared Malayalee migrants in the Gulf. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Data privacy in blockchain management scheme with Nudge Theory for banking sector
Blockchain is an emerging digital transformation technique for processing and storing information. The study explores how blockchain technology can transform the banking sector by improving efficiency, transparency, and security. The main goal is to understand how blockchain can modernize traditional banking operations and address key challenges such as fraud, high transaction costs, and slow processing times. The study uses a qualitative approach, drawing insights from existing research, real-world examples, and current trends in financial technology. Findings show that blockchain offers clear advantages, including faster and more secure transactions, reduced operational costs, and improved record-keeping. It holds strong potential in areas like payments, trade finance, and compliance. However, the paper also highlights significant obstacles such as unclear regulations, difficulties in integrating with existing systems, and technical limitations related to scalability and interoperability. Blockchain is seen as a promising solution for many of the inefficiencies in current banking practices. Still, successful implementation will require careful planning, regulatory support, and collaboration across the financial ecosystem. The study offers practical insights for banks, technology developers, and regulators, recommending a gradual and strategic approach to blockchain adoption to ensure long-term value and sustainability. 2025 by the authors; licensee Learning Gate. -
A scalable scheduling and resource management framework for cloud-native B2B applications
In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioningparticularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 510% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability. The Author(s) 2025. -
Sibling Bereavement Among Young Indian Adults
This qualitative study explores the bereavement experiences of 12 surviving siblings in India, focusing on familial, societal, and cultural influences. Six themes emerged: The Demanding Familial Role, Isolation That Accompanies the Grief, Damaging Impact of Society, Positive Role of Friends and Family, Support Systems, and Continuing Bonds. Participants often felt the burden of supporting their parents, leading to personal grief suppression and isolation, exacerbated by societal stigmas. Conversely, empathetic friends, supportive extended family, and professional resources like therapy provided crucial coping mechanisms. Continuing bonds with the deceased offered comfort and connection. The study highlights the need for comprehensive support systems tailored to cultural and societal contexts. It emphasizes the importance of public awareness and education to foster a supportive response to bereavement. Further research with larger, more diverse samples is recommended. The Author(s) 2024. -
QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning
Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. This work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. The quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. These results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems. 2025 IEEE. -
Investigation of corrosion behavior of Cenosphere reinforced iron based composite coatings
In the present study cenopshere was reinforced with FeCrNiC (Metco 42C) as matrix material and prepared four different feedstock powders such as FeCrNiC+0%Cenosphere, FeCrNiC+5%Cenosphere, FeCrNiC+10%Cenosphere and FeCrNiC+15%Cenosphere were coated by plasma spray technique on T22 substrate. Evaluation of the substrate and coatings potential under salt spray test was performed. Dense fog of 5% NaCl salt water was used to create a corrosive atmosphere within the chamber. The salt water's pH was kept constant at 6.57. The materials that underwent corrosion were examined using X-ray diffraction (XRD), and scanning electron microscopy (SEM). The FeCrNiC+15%Cenosphere and FeCrNiC+10%Cenosphere coatings exhibited reduced weight loss during a 168-h corrosion test compared to the FeCrNiC+5%Cenosphere, FeCrNiC coatings, and substrate. The excellent chemical stability and corrosion resistance of Cr23C6, SiO2, NiO, and Cr2O particles contribute to gradually avoid the formation of red rust on Fe-based coated samples with exposure approaches to 52 and 130 h. 2024 The Authors -
Lateral Load Behavior of Unreinforced Masonry Spandrels
Spandrels, are usually classified as secondary elements and even though their behaviour has not received adequate focus unlike piers, they significantly affect the seismic capacity of the structure. Masonry spandrels are often damaged and the first structural components that crack within Unreinforced Masonry structures. Despite this, existing analytical methods typically consider a limit case in which the strength of spandrels is either neglected, considered to be infinitely rigid and strong or treated as rotated piers. It is clearly evident that such an assumption is not plausible. Hence, reliable predictive strength models are required. This thesis attempts to re-examine the flexural behaviour of spandrels and proposes an analytical model. The model is based on the interlocking phenomena of the joints at the end-sections of the spandrel and the contiguous masonry. The proposed analytical model is incorporated within a simplified approach to account for the influence of spandrel response on global capacity estimate of URM buildings. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Curvature Ductility of Reinforced Masonry Walls and Reinforced Concrete Walls
Research conducted in this work proposes an equation to evaluate and compares the curvature ductility of reinforced masonry (RM) and reinforced concrete (RC) walls. The curvature ductilities are measured at varying levels of axial stresses for walls for aspect ratio (l/h) of 0.5, 1.0 and 1.5. The percentage of reinforcement is increased from 0.25% (minimum reinforcement for RC walls as per IS-13920) to 1.00%. The curvature ductilities are evaluated by plotting flexural strength (M) versus curvature (?) for the walls. The stressstrain curves of masonry, concrete and reinforcing steel are all adopted from existing literature. The compressive strength of masonry and concrete has been chosen as 10MPa and 25MPa, respectively. The yield strength of the steel is fixed as 415MPa. The height and thickness of the wall are 3000 and 230mm, respectively, and the length of the wall is varied to obtain different aspect ratios. Results obtained from this paper imply due to increase curvature ductility, RM walls provide a better alternative for the construction of structural walls compared to RC walls in regions of significant seismicity. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Idealised Bilinear Moment-Curvature Curves of Reinforced Masonry (RM) Walls
In this paper, an analytical investigation of the axial loadflexural strength interaction of reinforced masonry walls is carried. The curvature ductility of masonry walls is evaluated for walls with different modes of reinforcement configurations under different levels of axial loads. An analytical expression for evaluating the curvature ductility of masonry walls at varying axial loads is proposed in this paper. Value of curvature ductility obtained from the proposed expression is compared with existing methods. Results indicate the proposed model can be used to determine the ductility of reinforced masonry walls. 2020, Springer Nature Singapore Pte Ltd. -
Traditional Wisdom in Water Harvesting: A Comparative Review of Ahar Pynes and Tank Systems in India
The Indian subcontinent has historically relied on rainwater harvesting for agricultural and domestic water purposes. Traditional water management practices were crucial in the development of settlements and growth of villages and towns in Ancient India. Numerous indigenous water management practices have evolved across the diverse geography of this region to capture rain and manage surface water runoff. These systems have sustained the agrarian economy over centuries by contributing to irrigation and water security. This research paper focuses on two diverse water management systems in two geographically distinct areas in India. The Ahar Pyne system is practiced in the flood prone alluvial plains of Bihar, while the Tank system of irrigation is prevalent in the arid Deccan region. Both these systems were managed by the local communities living in their vicinity. These systems promote flood mitigation and drought resilience. These systems have become increasingly neglected in the recent years with development and advent of piped water supply. As the world grapples with problems escalated by climate change and the ensuing issue of water scarcity, there is an increasing interest in traditional systems and how they can be adapted to current needs. The comparative study of these two systems accentuates the adaptability of indigenous water management practices in different climatic and topographic conditions. The study also underlines the significance of integrating these traditional systems into the current water management processes. The paper also highlights the relevance of these systems in the current scenario and the need for revival and sustainable management of these systems towards building a resilient future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.