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Laccase mediated electrosynthesis of heliotropin on mango-kernel derived carbon nanosphere composite: A sustainable approach
Facile fabrication of enzyme immobilized carbon nanospheres (CNS) based catalysts with high electrical conductivity and catalytic efficiency are of decisive importance for their electrocatalysis. A novel, green and highly efficient synthesis route for the development of an electrode surface with enhanced electrical conductivity and better catalytic activity for the electrochemical synthesis of heliotropin. The obtained biowaste (mango seed kernels) was pyrolyzed and subjected to acid treatment to form functionalized CNS (f-CNS). The functionalized carbon fiber paper (CFP) electrode was employed as a template for laccase immobilization which was further treated with free laccase resulting in the formation of Lac-fCNS/CFP electrode. The developed electrode exhibited excellent electrooxidation of piperonyl alcohol in the presence of 2,2,6,6-Tetramethyl-1-piperidinyloxy (TEMPO), which served as the mediator. A high yield (78%) of heliotropin was achieved during the electrooxidation at 0.78 V via bulk electrolysis. The obtained product (heliotropin aka piperonal) was confirmed via 1H NMR and 13C NMR. Additionally, computational molecular docking analysis of f-CNS:laccase composite showed strong binding affinity (?6.2 kcal/mol) with TEMPO in comparison with free laccase (?5.1 kcal/mol). The excellent selectivity and efficiency of the developed electrocatalyst aim to surpass all other reported laccase-TEMPO mediated based electrocatalytic oxidation reactions. 2022 Vietnam National University, Hanoi -
Laccase mediated electrosynthesis of heliotropin on mango-kernel derived carbon nanosphere composite: A sustainable approach /
Journal of Science: Advanced Materials and Devices, Vol.7, Issue 4, pp.1-11, ISSN No: 2468-2179.
Facile fabrication of enzyme immobilized carbon nanospheres (CNS) based catalysts with high electrical conductivity and catalytic efficiency are of decisive importance for their electrocatalysis. A novel, green and highly efficient synthesis route is reported here for the development of an electrode surface with enhanced electrical conductivity and better catalytic activity for the electrochemical synthesis of heliotropin. The obtained biowaste (mango seed kernels) was pyrolyzed and subjected to acid treatment to form functionalized CNS (f-CNS). The functionalized carbon fiber paper (CFP) electrode was employed as a template for laccase immobilization which was further treated with free laccase resulting in the formation of Lac-fCNS/CFP electrode. -
Laccase-immobilized biochar as a unique host matrix for electrochemical detection of gallic acid: a sustainable engineering approach
An electrochemical platform for the detection of organic persistent polyphenolic pollutant, gallic acid (GA), was fabricated using an enzyme-immobilization approach over acid-functionalized biochar (f-BC) modified carbon fiber electrode (CFP) electrode. The f-BC was synthesized from dried pineapple leaves and was characterised using X-ray diffraction analysis (XRD), Raman spectroscopy, scanning electron microscopy (SEM), Fourier Transform-Infrared Spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS) for analysing the physicochemical characteristics. The SEM image of Lac/f-BC/CFP confirmed the presence of a porous and granulated surface upon laccase immobilization, while the FTIR spectrum confirmed the presence of C 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 O stretch and N-H bend, indicating amide bond formation. The acid treatment of biochar introduced -OH and -COOH groups that further aided in the successful immobilization of laccase via covalent bonding. The fabricated electrode could demonstrate a linear response within the concentration range of 0.012-40 ?M and a low detection limit (LOD) of 9 nM with high selectivity. The fabricated electrode also showcased high practical utility as it could attain high recovery percentage during real sample analysis in tap, pond, sewage and industrial effluent samples. 2025 The Royal Society of Chemistry. -
Laguerre polynomial-based operational matrix of integration for solving fractional differential equations with non-singular kernel
The Atangana-Baleanu derivative and the Laguerre polynomial are used in this analysis to define a new computational technique for solving fractional differential equations. To serve this purpose, we have derived the operational matrices of fractional integration and fractional integro-differentiation via Laguerre polynomials. Using the derived operational matrices and collocation points, we reduce the fractional differential equations to a system of linear or nonlinear algebraic equations. For the error of the operational matrix of the fractional integration, an error bound is derived. To illustrate the accuracy and the reliability of the projected algorithm, numerical simulation is presented, and the nature of attained results is captured in diverse order. Finally, the achieved consequences enlighten that the solutions obtained by the proposed scheme give better convergence to the actual solution than the results available in the literature. 2021 The Author(s). -
Lalasa Quantum Computing Method: A Unique Quantum Convolutional Neural Network Architecture
A novel Lalasa Quantum Computing Method architecture is presented in this paper for classification of medical images, aiming to enable efficient early detection of cancer. The proposed framework integrates a custom preprocessing pipeline that removes specular reflections using a SWIN Transformer and segments regions of interest via an Enhanced Gaussian Mixture Model. The quantum classification module employs amplitude encoding to map classical image data into quantum states, enabling structured feature extraction through a sequence of quantum convolutional layers with trainable variational circuits. The model was implemented using IBM Qiskit and trained on the publicly available Intel & Mobile ODT Cervical Cancer Screening dataset. Experimental results show a high overall classification accuracy of 98.58%, with moderate performance on class-specific F1-scores, recall, and precision. These results demonstrate the feasibility and effectiveness of quantum-classical hybrid models for medical image analysis, particularly in high-dimensional, low-sample scenarios. The study sets stage for future advancements in quantum machine learning applications in healthcare, with potential extensions involving real quantum hardware deployment and multiclass classification improvements. 2025 IEEE. -
Laminating techniques for luxury textiles
In this paper, we delved into the transformative aspects of lamination techniques in luxury textiles. We started by explaining luxury textiles and what makes them premium: craftsmanship, exclusivity, superior ingredients, silk, cashmere, fine wool etc. They presented the lamination procedures that are considered pre-processing steps to improve the functionality, lifespan, and appearance of such textiles. -
Land Use/Land Cover (LULC) Change Classification for Change Detection Analysis of Remotely Sensed Data Using Machine Learning-Based Random Forest Classifier
Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data. 2025 by the authors. -
Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment
Keerthikumara SM, Saikia B, Hiremath C. 2025. Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment. Asian J Agric 9: 377-390. Indian farmers continue to face structural distress driven by low income, high indebtedness, and inadequate risk protection. This study investigates the interrelationship between landholding size, agricultural credit, indebtedness, and crop insurance uptake using state-level secondary data from 2016 to 2023, drawn from Agricultural Statistics at a Glance, PMFBY/RWBCIS dashboards, and NCRB reports. Using descriptive statistics, linear regression, and paired t-tests, we identify key macro-level trends across 20 major Indian states. Results show that marginal and small farmers (less than 2 hectares) account for over 62.7% of all indebted farm households, but receive only 38.5% of total institutional agricultural credit. A bivariate regression reveals that a ?1,000 increase in monthly farm income is associated with a reduction of 1,314 indebted households (?=-0.445, p=0.016). Despite substantial credit disbursal in high-debt states like Andhra Pradesh and Telangana, farmer debt remains elevated, underscoring that credit alone does not reduce vulnerability. Crop insurance enrollment increased after the 2020 policy shift from mandatory to optional participation among loanee farmers, yet the change was not statistically significant (p=0.099). Actuarial analysis reveals that in several states, claim settlement ratios remain below 50%, with high premiums and delayed payouts fueling distrust. The study recommends fully subsidized premiums for marginal farmers, region-specific pricing, improved claim transparency, and financial literacy integration with agricultural extension. Effective risk mitigation in agriculture must go beyond insurance and integrate income and credit reforms to ensure equitable protection for Indias most vulnerable farmers. 2025, Smujo International. All rights reserved. -
Lane Detection using Kalman Filtering
Autonomous vehicles are the future of transportation. Modern high-tech vehicles use a sequence of cameras and sensors and in order to assess their atmosphere and aid to the driver by generating various alerts. While driving, it is always a challenging task for drivers to notice lane lines on the road, especially at night time, it becomes more difficult. This research proposes a novel way to recognize lanes in a variety of environments, including day and night. First various pre-processing techniques are used to improve and filter out the noise present in the video frames. Then, a sequence of procedure with respect to lane detection is performed. This stable lane detection is achieved by Kalman filter, by removing offset errors and predict future lane lines. 2023 Elsevier B.V.. All rights reserved. -
Language and identity formations of second-generation migrants in Deepak Unnikrishnan's temporary people
Decades of migration to the Gulf nations have led to the existence of second-generation migrants who were born and raised in migrant lands. The chapter uses the novel Temporary People (2017) by Deepak Unnikrishnan as a primary text to explore the role language plays in second-generation migrant identity formations and the assimilation process. The national language, Arabic, is situated in the specific socio-political context, a site where ideologies and power relations are reproduced. By identifying a gap in the language education policy, it reveals how migrant's inability to communicate in the Arabic language has complex implications on their identities and notions of belongingness. The chapter explores language's power to naturalize norms and hierarchical structures within society that can hinder the assimilation process and highlights the migrant-citizen divide. It shows how notions of temporariness and Othering in migrants are inherent within the language politics of the land. The chapter reaffirms language-identity relations and points to revaluating migrant language policies. 2023, IGI Global. All rights reserved. -
LANGUAGE CONTACT AND CHANGE: AN ANALYSIS OF HERITAGE KONKANI IN KERALA; [CONTACTE LINGSTIC I CANVI: UNA ANISI DEL PATRIMONI KONKANI A KERALA]; [CONTACTO LINGSTICO Y CAMBIO: UN ANISIS DEL PATRIMONIO KONKANI EN KERALA]
This paper analyses the extent of language contact induced change in the sound system of heritage Konkani community of Kerala, India. Heritage Konkanis, belonging to different castes namely Gowda Saraswat Brahmins (GSB), Saraswat non-brahmins, Konkan Sonars, Vaishya Vaniyar and Kudumbi, started migrating to Kerala in the late 13th century. The upper caste GSBs remained as a closed community until 20th century, which helped in the maintenance of their language. Linguistic data for the study was collected from 20 GSB youths of the age group 18-35 using Snowball sampling method. The analysis of the data shows loss of features such as aspiration and nasalization, which are inherent to Konkani. This indicates the vulnerable linguistic situation of the GSB community whose mother tongue exhibits linguistic variations, as a result of prolonged coexistence with the majority language, Malayalam. 2024 University of Barcelona. All rights reserved. -
Language, exempted?
Arent languages the most important, for, how else will students grasp, express, and practise the knowledge of the so-called core subjects? -
Lanthanide-based coordination polymers: a fluorometric Frontier in explosive sensing
In the pursuit of public safety, environmental protection, and counter-terrorism, significant advancements have been made in explosive detection techniques. However, challenges such as limited sensitivity, poor selectivity, and high operational costs remain, particularly for trace-level detection. In this study, we present a simple and scalable synthesis of lanthanide-based coordination polymers (Ln-COPs), denoted as Ho(DAB) and Tb(DAB), formed through the coordination of Ho(iii) and Tb(iii) ions, respectively, with the organic linker 3,3?-diaminobenzidine (DAB). Spectroscopic and electron microscopic analyses confirm their two-dimensional planar structure, resulting from the self-assembly of infinitely long polymeric strands. These luminescent Ln-COPs demonstrate exceptional performance as sensors for detecting both nitroaromatic and non-nitroaromatic explosives via fluorescence quenching. Notably, Tb(DAB) exhibits a remarkable limit of detection of 7.7 M for TNP. Furthermore, mechanistic insights into the quenching process are explored. These results underscore the sensitivity and practical applicability of Ln-COPs in advanced explosive detection systems. This journal is The Royal Society of Chemistry, 2025 -
Large Language Models in Economic Forecasting: A Comprehensive Analysis of Predictive Performance and Benchmarking Against Traditional Methods for India FY 2025-26
This study presents a comprehensive systematic evaluation of the performance of Large Language Models (LLMs) in economic forecasting, specifically examining their ability to predict key Indian macroeconomic indicators for the fiscal year 2025-26. Through a comparative analysis of ten prominent LLMs against traditional econometric models and expert forecasts from leading institutions, we assess the forecasting accuracy, reliability, and practical limitations of these models using a rigorous multistage validation framework. We validate predictions using actual quarterly data from Q1 and Q2 of FY 2025-26, providing a real-time assessment of forecasting capabilities with bootstrap confidence intervals and time series cross-validation techniques. Results reveal significant variations in LLM performance, with validation against Q1 2025-26 actual GDP growth of 6.7 per cent showing that several LLMs achieved superior accuracy (MAPE less than 3 per cent) compared to traditional ARIMA models (MAPE 13.58 per cent). Top-performing LLMs demonstrate forecasting capabilities that approach expert-level accuracy while maintaining computational efficiency and scalability. Statistical significance tests using the Diebold-Mariano framework confirm the superiority of ensemble LLM approaches over individual traditional methods. The findings demonstrate that leading LLMs can serve as valuable supplementary forecasting tools, positioning between conventional statistical methods and expert analysis in terms of accuracy, while offering advantages in processing qualitative information and adaptation to structural changes. 2025 IEEE. -
Large power factors in wide band gap semiconducting rFeO3 materials for high-temperature thermoelectric applications
While most of the thermoelectric materials work well only at low and mid temperatures, high-temperature thermoelectric materials (T > 900 K) are equally important for the operation of deep-spacecraft missions, nuclear reactors, and high-temperature industrial reactors. To accomplish this demand, this work provides insights into wide band gap semiconducting RFeO3 (rare-earth orthoferrites) for high-temperature thermoelectric applications. Using the first-principles density functional theory calculations, we have demonstrated the coexistence of extremely flat and corrugated flat bands near the Fermi region in a wide band gap material. The presence of such features enhances and sustains the thermopower, electrical conductivity, and power factor, which are the crucial factors for the efficiency of thermoelectric materials. Semiclassical Boltzmann formalism was then employed to study the transport properties of four orthorhombic RFeO3 materials (R = Pr, Nd, Sm, and Gd). Our results reveal high Seebeck coefficients (thermopower) along with the large electrical conductivities over the high hole doping carrier concentration and in the high-temperature region (T > 900 K). Furthermore, significantly large power factors are obtained with very low theoretical minimum lattice thermal conductivity in the range 1.41?1.51 W m?1 K?1. These huge power factors directly suggest the maximum power output in RFeO3, which we believe is a more appropriate performance index than the figure of merit, especially for high-temperature thermoelectric applications. We also emphasize that the outcomes of our work would be certainly useful for experimentalists in designing high-temperature thermoelectric materials. 2020 American Chemical Society -
Large scale extinction maps with UVIT
Astrophysics and Space Science Vol.343, No.2 ISSN No. 0004-640X -
Large scale extinction maps with UVIT
The Ultraviolet Imaging Telescope (UVIT) is scheduled to be launched as a part of the ASTROSAT satellite. As part of the mission planning for the instrument we have studied the efficacy of UVIT observations for interstellar extinction measurements. We find that in the best case scenario, the UVIT can measure the reddening to an accuracy of about 0. 02 magnitudes, which combined with the derived distances to the stars, will enable us to model the three-dimensional distribution of extinction in our Galaxy. The knowledge of the distribution of the ISM will then be used to study distant objects, affected by it. This work points the way to further refining the UVIT mission plan to best satisfy different science studies. 2013 Springer Science+Business Media Dordrecht. -
Large Scale Transportation Data Analysis and Distributed Computational Pipeline for Optimal Metro Passenger Flow Prediction
Transportation has a signifcant impact on controlling traffc around a busy city. Among the transport system, metro rails became the backbone by operating above the traffc. For this reason, we have to take special consideration of the passenger and#64258;ow in the transport system and, by understanding the needs, take timely actions for smooth running. Every metro system stores information about the and#64258;ow of passengers in the form of transactions known as Automatic Fare Collection (AFC) data. For this research, AFC data is taken as the primary newlinesource of information to identify the passenger and#64258;ow within the metro rail platform. Each metro system generates massive data throughout its running period and stores data within the system and considering the size of data generated, the analytic platform has to process them in a distributed paradigm to handle quotBig Dataquot. Artifcial Intelligence (AI) algorithms can derives information, insights, and patterns from this data. The patterns in time series can be identifed from the passenger and#64258;ow data using exploratory analysis. The step is an essential step in data science for understanding the underlying properties of the raw data. The research uses a data platform with a distributed computing and storage mechanism called the JP-DAP. The research leverages the above mentioned platform to extract passenger and#64258;ow data from AFC Ticketing data. After the data engineering, the results of passenger and#64258;ow information underwent further visualization and trend analysis. Based on the facts or patterns identifed from the passenger and#64258;ow information, a decision is taken for forecasting. The initial study will reveal the characteristics of metro usage and practices within the system and fnally derive a solution with machine learning-based forecasting method. The passenger and#64258;ow newlineforecasts based on the above patterns depend on factors like seasonality, trends, cyclicity, location, events, and random effects. -
Large-Scale Proteomics Reveals New Candidate Biomarkers for Late-Onset Preeclampsia
BACKGROUND: Preeclampsia is classified as either a more severe early onset or a more prevalent late-onset form. Lower PlGF (placental growth factor) and increased sFlt-1 (fms-like tyrosine kinase-1) in maternal circulation are promising biomarkers, yet they lack specificity for preeclampsia. METHODS: We quantified ?7000 proteins in 673 samples collected from 89 patients with late-onset preeclampsia and 91 controls at T1 (1522), T2 (2230), and T3 (3042) weeks. Elastic net and random forest models were fitted and evaluated by cross-validation. Differential abundance analysis followed by functional profiling, was used to identify and interpret protein changes. RESULTS: An increase in protein differential abundance in late-onset preeclampsia was observed with advancing gestation, reaching 806 proteins at T3 related to angiogenesis, cell adhesion, and extracellular matrix remodeling. FAAH2 (fatty acid amide hydrolase 2), SIGLEC6 (sialic acid-binding Ig-like lectin-6), IL17RC (interleukin-17 receptor C), HTRA1 (serine protease), sFlt-1, and 47 other proteins dysregulated at T3 were validated in a reanalysis of a ?5000 protein Norwegian data set. Random forest models with 20 proteins showed high accuracy at T3 (area under the curve [AUC], 0.83 [0.770.89], sensitivity 59%) even in cases not yet diagnosed at sampling (n=31, AUC, 0.80 [0.710.90], sensitivity 58%), outperforming sFlt-1 and PlGF. Moderate accuracy was obtained at T1 (AUC, 0.63 [0.540.72], sensitivity 33%) and T2 (AUC, 0.59 [0.500.68], sensitivity 17%). Combining maternal characteristics and obstetric history with proteomics data increased accuracy at T1 (AUC, 0.68 [0.590.77], sensitivity 28%), T2 (AUC, 0.68 [0.600.77], sensitivity 31%), and T3 (AUC, 0.87 [0.810.92], sensitivity 69%). CONCLUSIONS: The findings confirm the involvement of abnormal trophoblast invasion, angiogenesis, and extracellular matrix remodeling in late-onset preeclampsia, while highlighting new protein alterations consistent across diverse cohorts. 2025 American Heart Association, Inc. -
Larval descriptions and natural history of two endemic frogs (Amphibia: Anura) from the Western Ghats, India
Western Ghats of India is known for its high anuran diversity; however, the larvae of many anurans are still unknown. Studies on anuran larvae can provide insights into their natural history and evolution, help identify cryptic species and aid in amphibian conservation. In this study, we describe the tadpoles of two poorly known species Indirana bhadrai and Micrixalus candidus from the Western Ghats, India using morphology and molecular techniques and provide details on their natural history. The morphology of the tadpoles reflected their habitats. The tadpole of Indirana bhadrai was semiterrestrial, adapted to wet rocky slopes while the tadpole of Micrixalus candidus was fossorial, found under small rocks and sand in slow-flowing streams. Molecular analysis using the 16S rRNA gene showed 100% identity between tadpoles of Indirana bhadrai, and Micrixalus candidus with their adults respectively. The larval descriptions provided in this study can help understand the ecology of the frogs from the Western Ghats. Copyright 2025 Magnolia Press.