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A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors
The major VLS I circuits like sequential circuits, linear chips and op amps are very important elements to provide many logic functions. Today's competitive devices like cell phone, tabs and note pads are most prominent and those are used to get function the 5G related operations. In this work lower built-in self-test (LBIS T) mechanism is used to designing a microprocessor. The proposed methodology is giving performance measure like power efficiency 97.5%, improvement of delay is 2.5% and 32% development of area had been attained. This methodology attains more out performance and compete with present technology. The proposed equipment and execution for our approach requiring a constrained range overhead (lower than 3% power) over conventional LBIS T. 2022 IEEE -
Bioprospecting Soil Bacteria for Protease Production Using Agro-Waste: Toward Sustainable Detergent Formulations
Purpose: Microbial proteases, particularly from soil-dwelling Bacillus species, are preferred over plant- and animal-derived enzymes due to their high yield, stability, and cost-effectiveness for large-scale production in industries. This study aimed to isolate and characterize potent protease-producing bacteria from soil and explore their application in developing a sustainable, bio-based stain remover. The formulation incorporates waste (citrus fruit peel and flower), promoting the valorization of agro-waste as part of a sustainable waste management strategy. Methods and Results: Soil samples collected from market waste disposal sites in Madurai, Tamil Nadu, yielded eight distinct bacterial isolates, among which strain S-5 showed the highest proteolytic activity on skimmed milk agar. Molecular identification confirmed the isolate as Bacillus aerius based on 16S rRNA sequencing.The crude enzyme extract obtained after 48h of incubation exhibited maximum proteolytic activity at pH 11, with an activity of 0.928U/mL. This confirms that enzyme production improves at higher pH levels. A biodegradable stain remover was prepared by combining the crude protease extract with citrus peel extract in water and ethanol formulations. The prepared formulation effectively removed oil, paint, and dye stains from cotton cloth within 20min of treatment without mechanical rubbing, whereas control samples showed minimal stain removal. Ethanol-based formulations demonstrated higher cleaning efficiency compared to water-based extracts, showing extensive stain removal in all replicates, while control treatments showed only minor or minimal removal. Conclusion: The integration of microbial proteases from soil-derived bacteria with agro-waste components produced an eco-friendly stain remover, offering a sustainable alternative to chemical detergents and promoting waste valorization in circular economy-based green product development. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
GC-MS profiling of metabolites in blue and white varieties of heirloom butterfly pea (Clitoria ternatea L.) seeds
The Butterfly Pea is a tropical legume, a perennial herbaceous plant commonly found in Southeast Asia. The plant and its products are rich in bioactive ingredients, attracting the industrial and biopharmaceutical sectors due to their various applications. In this study, the blue and white flowered variety seeds of Butterfly Pea methanolic extract were comprehensively screened to identify the bioactive compounds and their drug-like properties. The methanolic extract was prepared by the cold maceration method, and the crude dried extract was subjected to GC-MS analysis for seed metabolite profiling. The chromatogram analysis revealed 39 abundant phytoconstituents, demonstrating the diverse chemical composition of the Butterfly Pea seeds. Among the identified compounds, the relatively abundant bioactive components in the blue variety seeds were stearic acid (64.6%), methyl stearate (54.0%), hexadecanoic acid, methyl ester (48.2%), and ethriol (35.9%). the white variety seeds primarily included palmitic acid (71.0%), hexadecanoic acid, methyl ester (53.4%), methyl stearate (42.0%), and hydrocinnamic acid (30.5%). Additionally, both varieties exhibited a diverse array of shared compounds reflecting their phylogenetic proximity. These metabolites are associated with key bioactivities in plant signaling and defense, playing vital roles in growth regulation, stress adaptation, and exhibiting potential antidiabetic properties. The research highlights the potential of the butterfly pea seeds as a valuable resource of active metabolites for vast research and therapeutic applications. 2025, Indian journals. All rights reserved. -
Effect of Magneto Convection Nanofluid Flow in a Vertical Channel
An analytical study of the effect of the magneto-convective flow of immiscible fluids through a vertical channel has been investigated in the presence of a chemical reaction. One region is saturated by electrically conducting incompressible fluid, and the other is saturated by nanofluid in a vertical channel with constant transport properties. The coupled nonlinear governing equations are solved by the regular perturbation method, with the Brinkman number as a perturbation parameter since its value is always less than unity. The results are discussed in detail using plots to analyze the flow phenomena. The increase in thermal and mass Grashof numbers enhances the fluid velocity and temperature profile, whereas Hartman number, solid volume fraction, and chemical reaction parameters exhibit the opposite effect. The effect of an increase in the nanoparticle volume fraction opposes the fluid flow and diminishes the temperature distribution due to the enhanced viscosity of the nanofluid. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
Social Media Addiction and ParentPeer Attachment in Telangana Adolescents: A Cross-sectional Investigation
Background: The ubiquity of social media in contemporary life has raised concerns about its potential negative impacts, particularly among adolescents. While the impact of attachment on adolescents social media use has been studied in Western and South Asian contexts, there is a paucity of research on this relationship in the Indian context. Aims: This study aimed to find the relationship between sociodemographic factors, attachment to parents and peers, and social media addiction among adolescents in Telangana, India. Methods: A random cluster sampling method was used to survey 264 6th to 12th grade students in two schools. Data was collected using the Parent and Peer Attachment Inventory and the Social Media Disorder Scale. Chi-square analysis, Pearsons correlation, and multiple regression analysis were done to achieve the research objective. Results: The study found no association between sociodemographic factors (age, gender, socioeconomic status, family type, and number of siblings) and social media addiction. However, significant negative correlations were found between social media addiction and dimensions of attachment to parents and peers, except for communication with friends. Multiple regression revealed that attachment dimensions explained 15.7% of the total variance. The variables, Trust in the father and Alienation from the mother independently and significantly predicted social media addiction. Conclusion: The findings underscore the importance of attachment relationships in understanding social media addiction among Indian adolescents. The results reveal that fathers and mothers attachments to adolescents predict adolescent social media addiction differentially. Further research, especially longitudinal studies, is needed to explore these relationships in greater depth. 2025 Indian Journal of Social Psychiatry. -
Determination of heavy metals in various tissues of locally reared (country) chicken in major districts of Karnataka, India: Assessment of potential health risks
Food is one of the most prevalent ways that humans are exposed to metals. Heavy metals including cadmium, iron, zinc, lead, and mercury are harmful to humans and have a detrimental impact on health because they accumulate in biological organs. The concentration levels of these heavy metals were tested in different edible parts of the country (locally raised) chicken from various districts in Karnataka, India, namely Bengaluru, Tumakuru, Mangaluru, and Udupi, using an Atomic-Absorption Spectrophotometer (AAS). Heavy metal concentrations in various chicken parts were found to be below detectable limits (BDL)-0.0062, 0.027-3.178, and 0.262-2.103 ppm for Cd, Fe, and Zn, respectively, whereas Hg and Pb were BDL. The content of Zn was found to be significantly higher in all chicken samples from all examined districts, followed by Fe and Cd. Hg and Pb concentrations, on the other hand, were below the detection level in all samples. The estimated daily intakes (EDIs) of the observed metals from country chicken consumption were found to be lower than their respective FAO/WHO reference oral doses (RfD). The non- carcinogenic health hazards posed by the tested metals to the target population were estimated using the Hazard Quotient (HQ) and Hazard Index (HI) values. The HQ and HI values observed in this estimation were less than one, indicating that exposure to these heavy metals through the consumption of country chicken is unlikely to provide possible health concerns to the examined regions human population. 2023, Universidade Federal do Parana. All rights reserved. -
Psychological Problems Among Children Three Years After the Earthquake in Nepal
Background: Frequent disasters and weak mental health system pose a risk to psychological health in Nepal. In 2015, a massive earthquake of 7.6 magnitude occurred in Nepal, which caused large scale destruction to human life and property. Limited research in children after disasters in Nepal prevent health professionals from implementing new evidence-based trauma treatments. Aim: The study aimed to identify the long term emotional problems experienced by earthquake-affected children in Nepal. The role of gender, severity of exposure, socioeconomic status and type of family in relation to emotional problems were also examined in the selected group. Methods: A purposive sampling was used to select 454 children (4th and 5th standard) from two highly affected wards in Kathmandu Metropolitan City. Information about exposure to the earthquake was collected from children using the Level of Exposure Scale while the parents completed the Nepali version of the Strengths and Difficulties Questionnaire (SDQ/ 4-17). Results: The effect of exposure to the earthquake was identified in the children even after three years. Boys had higher conduct, hyperactivity-inattention and peer problems while girls had high pro-social behaviour. Emotional problems were greater for those belonging to a lower socio-economic status. Among the variables, gender was a better predictor of emotional problems in earthquake-affected children. Conclusions: Emotional problems such as conduct problems, hyperactivity-inattention, peer problems are present in the earthquake-affected children in Kathmandu. Future researchers and clinicians need to monitor the children affected by the earthquake to recognise vulnerable groups and implement appropriate trauma-focused interventions. 2021, Indian Association for Child and Adolescent Mental Health. All rights reserved. -
Efficacy of Art Based Interventions for Emotional Problems among Children Affected by Earthquake in Nepal
The earthquake of April 2015 left Nepal in a vulnerable state. Children represent an estimated 3.2 million of the 8 million people affected by the earthquake. The aim of the study was to examine the role of art in dealing with the long-term impact of earthquake on emotional problems in children in Nepal. A purposive sampling was adopted to select 454 children studying in 4th and 5th standard from four schools in Kathmandu Metropolitan City. Children completed the Level of Exposure Scale while the parents provided information about the emotional and behavioural difficulties of children using the Nepali version of Strengths and Difficulties Questionnaire (SDQ/ 4-17). The influence of gender, severity of exposure, socio-economic status and type of family in relation to emotional problems were also examined in the selected group. The results of Phase 1 show that conduct, hyperactivity-inattention and peer problems were higher in boys while girls had higher pro-social behaviour. Children belonging to lower socio-economic status were found to be at risk for emotional problems. Gender and exposure were also identified as predictors of emotional problems in children. For the second phase of the study, those children with high emotional problems (N=60) were selected for an art-based intervention consisting of nine sessions. Both the treatment (N=30) and control group (N=30) completed the pre- and post- treatment measure of SDQ. The results show that the children in the treatment group reported lower levels of emotional problems, hyperactivity-inattention and peer problems compared to the control group (Cohen's d: 0.50-0.80). In the final phase of the study, 12 children from the treatment group were interviewed to identify the elements of art that contributed to a change in the emotional problems. A thematic vii analysis revealed six global themes: a new schema, an expression space, drawing the trauma, reappraisal of trauma narrative, protective factors and future benefits. The responses of the children show that the inherent properties such as regulation and social connection promoted by an engagement in arts needs to be adopted as an effective mode of trauma care. The findings also point to the possibility of using art-based therapy to overcome stigma which hinder the mental health professionals when implementing evidence-based treatments in the country. -
Forest Fire Prediction Using Machine Learning and Deep Learning Techniques
Forests are considered synonyms for abundance on our planet. They uphold the lifecycle of a diversity of creatures, including mankind. Destruction of such forests due to environmental hazards like forest fires is disastrous and leads to loss of economy, wildlife, property, and people. It endangers everything in its vicinity. Sadly, the presence of flora and fauna only increase the fire spread capability and speed. Early detection of these forest fires can help control the spread and protect the nearby areas from the damage caused. This research paper aims at predicting the occurrence of forest fires using machine learning and deep learning techniques. The idea is to apply multiple algorithms to the data and perform comparative analysis to find the best-performing model. The best performance is obtained by the decision tree model for this work. It gave an accuracy of 79.6% and a recall score of 0.90. This model was then implemented on front-end WebUI using the flask and pickle modules in Python. The front-end Website returns the probability that a forest fire occurs for a set of inputs given by the user. This implementation is done using the PyCharm IDE. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sexual violence in cyberspace: breaking the silence of international law
The increasing dependence of the world on digital technology and the internet has consequently led to the juxtaposition of the problematic social structure in online transactions and communications. This has resulted in increasing cases of cybersexual violence against women. The article argues that the effects of cybercrimes are transnational and, therefore, the traditional domestic criminal law is rather inept in preventing crime and punishing offenders. This increases the obligation of international law, which has so far remained silent on the issue. The articles conclusion suggests that the proposed World Convention on Cybercrime should include cybersexual violence as a core crime. This would serve as a beginning for addressing the threat, effect, and extent of the crime of cybersexual violence. The article concludes that the masculinist normative structure of international law is to blame for its culture of silence. Copyright 2024 Inderscience Enterprises Ltd. -
Characterization of signed paths and cycles admitting minus dominating function
Let G = (V, E, ?) be a finite signed graph. A function f: V ? {?1, 0, 1} is a minus dominating function (MDF) of G if f(u) + Pv?N(u) ?(uv)f(v) ? 1 for all u ? V. In this paper we characterize signed paths and cycles admitting an MDF. 2020 Azarbaijan Shahid Madani University -
All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems
An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817nm with a field enhancement of approximately 28x with gap dimensions of 10nm long. Fabricated devices attained resonance of 823nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 10? and overall shift of the resonance at 51nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Hi line analysis of Herbig Ae/Be stars using X-Shooter spectra
Herbig Ae/Be stars are intermediate-mass pre-main sequence stars undergoing accretion through their circumstellar disk. The optical and infrared (IR) spectra of HAeBe stars show Hi emission lines belonging to Balmer, Paschen and Brackett series. We used the archival X-Shooter spectra available for 109 HAeBe stars from Vioque et al. (2018) and analysed the various Hi lines present in them. We segregated the stars into different classes based on the presence of higher-order lines in different Hi series. We discussed the dependence of the appearance of higher-order lines on the stellar parameters. We found that most massive and younger stars show all the higher-order lines in emission. The stars showing only lower-order lines have Teff< 12 , 000 K and an age range of 510 Myr. We performed a case B line ratio analysis for a sub-sample of stars showing most of the Hi lines in emission. We noted that all but four stars belonging to the sub-sample show lower Hi line ratios than theoretical values, owing to the emitting medium being optically thick. The Hiline flux ratios do not depend on the stars spectral type. Further, from the line ratios of lower-order lines and Paschen higher-order lines, we note that line ratios of most HAeBe stars match with electron density value in the range of 10 9 10 11 cm - 3 . The electron temperature, however, could not be ascertained with confidence using the line ratios studied in this work. 2023, Indian Academy of Sciences. -
Disentangling the two sub-populations of early Herbig Be stars using VLT/X-shooter spectra
Context. Early Herbig Be (HBe) stars are massive, young stars accreting through the boundary layer mechanism. However, given the rapid (<2 Myr) evolution of early Herbig stars to the main-sequence phase, studying the evolution of the circumstellar medium around these stars can be a cumbersome exercise. Aims. In this work, we study the sample of early (B0-B5) HBe stars using the correlation between H? emission strength and near-infrared excess, complemented by the analysis of various emission features in the X-shooter spectra. Methods. We segregate the sample of 37 early HBe stars based on the median values of H? equivalent width (EW) and near-infrared index (n(J-H)) distributions. The stars with |H? EW| > 50and n(J-H) > -2 are classified as intense HBe stars and stars with |H? EW| < 50and n(J-H) < -2 as weak HBe stars. Using the VLT/X-shooter spectra of five intense and eight weak HBe stars, we visually checked for the differences in intensity and profiles of various HI and metallic emission lines commonly observed in Herbig stars. Results. We propose that the intense HBe stars possess an inner disk close to the star (as apparent from the high near-infrared excess) and an active circumstellar environment (as seen from the high H? EW value and presence of emission lines belonging to FeII, CaII, OI, and [OI]). However, for weak HBe stars, the inner disk has cleared, and the circumstellar environment appears more evolved than for intense HBe stars. Furthermore, we compiled a sample of ~58 000 emission-line stars published in Gaia DR3 to identify more intense HBe candidates. Further spectroscopic studies of these candidates will help us to understand the evolution of the inner (approximately a few au) disk in early HBe stars. The Authors 2023. -
Emission line star catalogues post- Gaia DR3: A validation of Gaia DR3 data using the LAMOST OBA emission catalogue
Aims.Gaia Data Release 3 (DR3) and further releases have the potential to identify and categorise new emission-line stars in the Galaxy. We perform a comprehensive validation of astrophysical parameters from Gaia DR3 with the spectroscopically estimated emission-line star parameters from the LAMOST OBA emission catalogue. Method. We compare different astrophysical parameters provided by Gaia DR3 with those estimated using LAMOST spectra. By using a larger sample of emission-line stars, we performed a global polynomial and piece-wise linear fit to update the empirical relation to convert the Gaia DR3 pseudo-equivalent width to the observed equivalent width, after removing the weak emitters from the analysis. Results. We find that the emission-line source classifications given by DR3 is in reasonable agreement with the classification from the LAMOST OBA emission catalogue. The astrophysical parameters estimated by the esphs module from Gaia DR3 provides a better estimate when compared to gspphot and gspspec. A second degree polynomial relation is provided along with piece-wise linear fit parameters for the equivalent width conversion. We notice that the LAMOST stars with weak H? emission are not identified to be in emission from BP/RP spectra. This suggests that emission-line sources identified by Gaia DR3 are incomplete. In addition, Gaia DR3 provides valuable information about the binary and variable nature of a sample of emission-line stars. 2022 EDP Sciences. All rights reserved. -
Discovery of 2716 hot emission-line stars from LAMOST DR5
We present a catalog of 3339 hot emission-line stars (ELSs) identified from 451 695 O, B and A type spectra, provided by LAMOST Data Release 5 (DR5). We developed an automated Python routine that identified 5437 spectra having a peak between 6561 and 6568 False detections and bad spectra were removed, leaving 4138 good emission-line spectra of 3339 unique ELSs. We re-estimated the spectral types of 3307 spectra as the LAMOST Stellar Parameter Pipeline (LASP) did not provide accurate spectral types for these emission-line spectra. As Herbig Ae/Be stars exhibit higher excess in near-infrared and mid-infrared wavelengths than classical Ae/Be stars, we relied on 2MASS and WISE photometry to distinguish them. Finally, we report 1089 classical Be, 233 classical Ae and 56 Herbig Ae/Be stars identified from LAMOST DR5. In addition, 928 B[em]/A[em] stars and 240 CAe/CBe potential candidates are identified. From our sample of 3339 hot ELSs, 2716 ELSs identified in this work do not have any record in the SIMBAD database and they can be considered as new detections. Identification of such a large homogeneous set of emission-line spectra will help the community study the emission phenomenon in detail without worrying about the inherent biases when compiling from various sources. 2021 National Astronomical Observatories, CAS and IOP Publishing Ltd.. -
Insights into the Publication Trends of Pharmaceutical Reverse Supply Chain Using Data Mining Approach
Due to the non-profitable nature of reverse supply chain of pharmaceutical products, researchers and companies have not shown much interest in this field. Due to stringent regulatory compliances pharmaceutical companies and hospitals are mandated for proper disposal of pharmaceutical wastes. This research aims to highlight the publication trends of pharmaceutical reverse supply chain using data mining approach. The metadata of published literature was extracted from Scopus and analysis was done for the title and abstracts of the articles. It was found that there is limited published literature on this topic. Co-occurrence map of text-based data, time graph of co-occurrence map of text, trigrams word cloud, keywords plus word cloud and unigrams word cloud were formed to get insights into the publication trend. A model had been proposed from the consumers end for pharmaceutical reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Development of an AI Based Framework for Reverse Supply Chain of Pharmaceutical Products
The pharmaceutical reverse supply chain is an integral part of pharmaceutical industry. Due to the complex nature of the process and strict government regulations, it is important to use different AI technologies to increase the efficiency of the reverse supply chain. This research aims to design an AI driven framework for reverse supply chain of pharmaceutical products which would increase efficiency, speed, automate processes and enhance trust among the stakeholders. The framework consists of five modules namely Collection and Sorting Centre, Return Management, Real-time Inventory Management, Disposal Centre, and Data Analytics. In each module different AI technologies have been embedded to increase the efficiency of the system. The proposed framework offers a holistic approach that not only aligns with stringent pharmaceutical standards but also contributes to a more robust, transparent, and environmentally sustainable reverse supply chain. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Geochemical Data Exploration using Machine Learning Methods
This study introduces a novel ensemble model combining Support Vector Machine (SVM) and Gradient Boosting algorithm (GBC). The model's performance is compared with the two single layered model namely K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) on a publicly available dataset. Further, Performance is measured using standard metrics such as accuracy, precision, and recall. To have the excellence in detection of types of rocks based on its properties this research explores the stacking approach, contributing in the field of geological studies and also for future exploration making it effective and efficient in identification of mineral deposits. 2025 IEEE. -
Segregation and researcher's positionality: Challenges of conducting policy ethnography in Southern polarized settings
Researchers conducting policy ethnography in conflict environments are faced with a valuable ethical dilemma is there an ethical standard to determine how a dataset should be pursued in the field? What if the method of pursuing data carries the potential of possibly disrupting one's rapport with the community and being perceived as a partisan ideologically driven researcher with ulterior motives? This question becomes more pronounced in socio-legal, conflict and public policy research in spatially polarized settings of the South. In these settings, knowledge is co-produced through one's own positionality and the nuances of grey areas that do not often feature in aggregated datasets. Scholarship on positionality has questioned whether scholars should explicate their position on the field by pointing towards the intentional or unintentional perpetuation of hierarchies. This paper situates itself in the positionality debate with reference to castelessness in socio-legal research through nine months of ethnographic fieldwork in a Southern spatially polarized setting. It grapples with an emerging contrasting view of whether researchers should at all engage in explicating their positionality. The paper argues that data is a socio-spatial product. It is to suggest that the production of data in conflict settings is informed by the spatial dynamics of social relations that emerge in the co-production of knowledge, and the researcher's reflexive positionality that itself impacts the outcome of data that emerges. 2025 The Author(s). Journal of Law and Society 2025 Cardiff University (CU).