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Gas Kinematics and Dynamics of Carina Pillars: A Case Study of G287.76-0.87
We study the kinematics of a pillar, namely G287.76-0.87, using three rotational lines of 12CO(5-4), 12CO(8-7), 12CO(11-10), and a fine structure line of [O i] 63 ?m in southern Carina observed by SOFIA/GREAT. This pillar is irradiated by the associated massive star cluster Trumpler 16, which includes ? Carina. Our analysis shows that the relative velocity of the pillar with respect to this ionization source is small, ?1 km s?1, and the gas motion in the tail is more turbulent than in the head. We also performed analytical calculations to estimate the gas column density in local thermal equilibrium (LTE) conditions, which yields N CO as (?0.2-5) 1017 cm?2. We further constrain the gass physical properties in non-LTE conditions using RADEX. The non-LTE estimations result in n H 2 ? 10 5 cm ? 3 and N CO ? 1016 cm?2. We found that the thermal pressure within the G287.76-0.87 pillar is sufficiently high to make it stable for the surrounding hot gas and radiation feedback if the winds are not active. While they are active, stellar winds from the clustered stars sculpt the surrounding molecular cloud into pillars within the giant bubble around ? Carina. 2024. The Author(s). Published by the American Astronomical Society. -
GASP XVIII: Star formation quenching due to AGN feedback in the central region of a jellyfish galaxy
We report evidence for star formation quenching in the central 8.6 kpc region of the jellyfish galaxy JO201 that hosts an active galactic nucleus (AGN), while undergoing strong ram-pressure stripping. The ultraviolet imaging data of the galaxy disc reveal a region with reduced flux around the centre of the galaxy and a horse-shoe-shaped region with enhanced flux in the outer disc. The characterization of the ionization regions based on emission line diagnostic diagrams shows that the region of reduced flux seen in the ultraviolet is within the AGN-dominated area. The CO J2-1 map of the galaxy disc reveals a cavity in the central region. The image of the galaxy disc at redder wavelengths (9050-9250 reveals the presence of a stellar bar. The star formation rate map of the galaxy disc shows that the star formation suppression in the cavity occurred in the last few 108 yr. We present several lines of evidence supporting the scenario that suppression of star formation in the central region of the disc is most likely due to the feedback from the AGN. The observations reported here make JO201 a unique case of AGN feedback and environmental effects suppressing star formation in a spiral galaxy. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
GASP XXIII: A Jellyfish Galaxy as an Astrophysical Laboratory of the Baryonic Cycle
With MUSE, Chandra, VLA, ALMA, and UVIT data from the GASP program, we study the multiphase baryonic components in a jellyfish galaxy (JW100) with a stellar mass 3.2 1011 M o hosting an active galactic nucleus (AGN). We present its spectacular extraplanar tails of ionized and molecular gas, UV stellar light, and X-ray and radio continuum emission. This galaxy represents an excellent laboratory to study the interplay between different gas phases and star formation and the influence of gas stripping, gas heating, and AGNs. We analyze the physical origin of the emission at different wavelengths in the tail, in particular in situ star formation (related to H?, CO, and UV emission), synchrotron emission from relativistic electrons (producing the radio continuum), and heating of the stripped interstellar medium (ISM; responsible for the X-ray emission). We show the similarities and differences of the spatial distributions of ionized gas, molecular gas, and UV light and argue that the mismatch on small scales (1 kpc) is due to different stages of the star formation process. We present the relation H?-X-ray surface brightness, which is steeper for star-forming regions than for diffuse ionized gas regions with a high [O i]/H? ratio. We propose that ISM heating due to interaction with the intracluster medium (either for mixing, thermal conduction, or shocks) is responsible for the X-ray tail, observed [O i] excess, and lack of star formation in the northern part of the tail. We also report the tentative discovery in the tail of the most distant (and among the brightest) currently known ULX, a pointlike ultraluminous X-ray source commonly originating in a binary stellar system powered by either an intermediate-mass black hole or a magnetized neutron star. 2019. The American Astronomical Society. All rights reserved. -
GASP. XV. A MUSE view of extreme ram-pressure stripping along the line of sight: Physical properties of the jellyfish galaxy JO201
We present a study of the physical properties of JO201, a unique disc galaxy with extended tails undergoing extreme ram-pressure stripping (RPS) as it moves through the massive cluster Abell 85 at supersonic speeds mostly along the line of sight. JO201 was observed with multi-unit spectroscopic explorer as part of the GASP programme. In a previous paper (GASP II) we studied the stellar and gas kinematics. In this paper we present emission-line ratios, gas-phase metallicities, and ages of the stellar populations across the galaxy disc and tails. We find that while the emission at the core of the galaxy is dominated by an active galactic nucleus (AGN), the disc is composed of star-forming knots surrounded by excited diffuse gas. The collection of star-forming knots presents a metallicity gradient steadily decreasing from the centre of the galaxy outwards, and the ages of the stars across the galaxy show that the tails formed ? 109 yr ago. This result is consistent with an estimate of the stripping time-scale (?1 Gyr), obtained from a toy orbital model. Overall, our results independently and consistently support a scenario in which a recent or ongoing event of intense RPS acting from the outer disc inwards, causes removal and compression of gas, thus altering the AGN and star formation activity within and around the galaxy. 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Gastronomic delights for community growth: unravelling the impact of sustainable tourism in Sikkim, India
Community development (CD) and sustainable gastronomy tourism development (SGTD) are mutually beneficial. Therefore, this study calls for a thorough investigation and interpretation of the phenomenon. In the context of Sikkim, India, this study examines the effects of SGTD on CD. The research question of whether sustainable gastronomy tourism (SGT) influences CD is addressed through a narrative analysis. The stories of ten local food vendors are gathered and examined using the categorical-content approach. However, the narratives indicate that local food vendors do believe that SGTD can act as a catalyst for local development, and that using traditional food as an alternative source of income can offer them a number of benefits. Other facets of gastronomic tourism are identified in the stories that may have unfavourable effects. Several ways to promote SGTD are suggested. The paper concludes that, to endorse gastronomic tourism (GT), local community involvement and strict policies are crucial. Copyright 2024 Inderscience Enterprises Ltd. -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. -
Gaze and Queer Autonomy? Representations and Possibilities on New Visual Media Landscapes in the Indian Context
Representations of sexuality in Indian mainstream cinema tend to reinforce sexual differences, imbalances, and certain stereotypes that put queer identities in a disadvantageous position. The shift and transition from the monopoly of the state to an era of popular forms of entertainment enabled the centrality of debates on representations and sexuality. The study examines the representations of queerness that flourished on new visual media landscapes such as the OTT platforms Netflix and Amazon Prime Video that engaged in a new dialogue on queer representations and possibilities. The study attempts to analyze Super Deluxe (2019), Made in Heaven (2019) and Ajeeb Daastans (2021), as they become the representative form of cinema that marked the shift and engaged in a new dialogue on queer representations and possibilities. The study reads queer representation in light of the frameworks of gaze and homophyly to understand why they might offer different opportunities for representation and gaze. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
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. -
GCMS Analysis and zebrafish studiesr reveal presence of antilipidemic phytochemicals in methanolic seed extracts of phaseolus vulgaris /
International Journal of Pharmacy And Biological Sciences, Vol.9, Issue 2, pp.796-802, ISSN No: 2230-7605. -
GCMS analysis, anthelmintic, antibacterial and antifungal properties of unripe fruit peel extract of Musa paradisiaca L.
Endoparasites, namely, Ascaris sp., Taenia sp., Haemonchus contortus, Ancylostoma duodenale etc. are of serious concern since they can lead to financial loss if farm animals are attacked by these parasites. Finding out cost-effective natural remedies for these infections is an area of research in the field of veterinary sciences. The current study was undertaken with a view to explore the anthelmintic property of banana peel along with its other bioactive properties. The fruit is available abundantly throughout the year and the peel is often discarded as waste. This study has shown that the extracts of the Nendran variety of Musa paradisiaca L. fruit peel have potent anthelmintic, antibacterial and antifungal activities. Preliminary cytotoxicity studies have also given positive results. GCMS analysis has revealed the major phytochemicals responsible for the bioactive properties of the peel. Since the United Nations has urged countries to align research and development with a thrust on sustainable development, these kinds of natural alternatives are best suited in place of synthetic drugs. 2023 World Research Association. All rights reserved. -
Ge-GaAs-Ge Heterojunction MOSFETs for Mixed-Signal Applications
A lattice matched heterojunction intraband tunnel (HJIBT) FET is proposed. The performance dependence of the device on conduction band (CB) discontinuity at source-channel and drain-channel interface is addressed using numerical simulation. Various mechanisms governing transport phenomena in the HJIBT FET are investigated in detail for different CB offsets (CBOs). For low gate to source voltage ( ${V}_{\text {GS}}$ ), thermionic emission is found to be the most significant transport mechanism. For moderate ${V}_{\text {GS}}$ , intraband tunneling phenomenon dominates over thermionic emission and continues to remain so. At high ${V}_{\text {GS}}$ , band-to-band tunneling occurs in HJIBT FETs. The proposed device shows improved figures of merit such as drain-induced barrier lowering (DIBL), ON-current ( ${I}_{ \mathrm{\scriptscriptstyle ON}}$ ) to OFF-current ( ${I}_{ \mathrm{\scriptscriptstyle OFF}}$ ) ratio ( ${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$ ), subthreshold slope (SS), gate capacitance ( ${C}_{\text {G}}$ ), ${g}_{m}$ (transconductance), and ${f}_{T}$ (cut-off frequency), with respect to conventional MOSFET. Also, the design of a high-performance hybrid 6T-static random access memory (SRAM) is proposed. 1963-2012 IEEE. -
Gems of Prediction: From Clarity to Carats - Unveiling Diamond Prices with Machine Learning in Waikato Environment for Knowledge Analysis
Background: This research focuses on using Weka's toolkit to test machine learning models for predicting diamond prices. The complexity of diamond value characteristics, such as carat, cut, color, and clarity, motivates the study to find the most accurate models. The goal is to promote fairer market processes and customer education. Methods used: The research rigorously preprocesses a diamond attributes dataset using Weka for analysis. Various machine learning algorithms are examined, including simple algorithms like Decision Stump and ZeroR, sophisticated models like M5P and REP Tree, and advanced ensemble approaches like Bagging with REP Tree. Model performance is evaluated using train/test splits (80-70-60%) and cross-validation (5-fold and 10-fold) with metrics such as Correlation Coefficient, MAE, and RMSE. Results achieved: The research finds that ensemble approaches, particularly Bagging with REP Tree, outperform simple and sophisticated models in diamond price prediction. These techniques demonstrate higher accuracy and lower error rates, highlighting the need for multiple models to capture the complexity of diamond valuation. Simple models provide benchmarks and insights into dataset trends but are less precise. Concluding remarks: This study contributes to the understanding of machine learning algorithms for diamond price prediction, an important economic valuation subject. It demonstrates the effectiveness of complex data analysis methods using Weka. The research also highlights the accessibility and sophistication of machine learning at the crossroads, with Weka's cutting-edge algorithms making complicated analytical methods more accessible for practical, everyday use. This work adds to the knowledge of the dynamics of diamond prices and the role of machine learning in economic research. 2024 IEEE. -
GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification
The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 37% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. 2026 by the authors. -
Gen AI Gen Z: understanding Gen Zs emotional responses and brand experiences with Gen AI-driven, hyper-personalized advertising
Introduction: Gen Z, a tech-savvy consumer group, has highly evolved in its approach to new-age advertising. The rise of Generative Artificial Intelligence (Gen AI) has revolutionized advertising by enabling hyper-personalized content, making it essential to understand its influence on Generation Z (Gen Z) population. This study explores the responses of Gen Z participants in India to Generative Artificial Intelligence based, hyper-personalized advertisements, with a specific focus on emotional responses and brand interactions which are significant predictors of advertisement success. Methods: Using qualitative research methods, semi-structured interviews were conducted with 40 Gen Z participants. Thematic analysis of the data was performed to understand the major themes pertaining to emotional responses and brand interactions to this form of Gen AI-driven advertising. Results: Two major themes and five sub-themes were revealed through thematic analysis. The first theme, diverse emotional responses, encompassed two sub-themes, curiosity and interest as well as fear and suspicion. The second major theme, enhanced brand experience, encompassed three sub-themes of advanced targeted marketing; initial attraction and brand engagement; and brand connection and loyalty, as perceived by the participants. Discussion: Findings imply that brands can harness Gen AI-driven, hyper-personalized advertisements to evoke meaningful emotions, enhancing consumer loyalty and building stronger, more personal connections with their audience. Copyright 2025 Peter, Roshith, Lawrence, Mona, Narayanan and Yusaira. -
Gen Z and the Road to Electric Vehicles: Navigating Practical Barriers
Electric Vehicles (EVs) are regarded as a solution to lowering a countrys carbon emissions in numerous countries worldwide. When compared to traditional fueled vehicles, also known as Internal combustion engine vehicles (ICEVs), EVs emit significantly less carbon. Numerous countries including India, are looking to increase EV adoption as part of their plan to curb their overall carbon output. However, transforming a substantial portion of a countrys vehicles to EVs, comes with its own set of challenges in areas like infrastructure required to maintain EVs as well as meeting the increased demand in energy in a sustainable and environment friendly manner. The present study dives into the exploration of perception among Gen Z towards adoption of EVs particularly related to factors such as cost, infrastructure, functionality and environment. The study signifies the importance of functionality features in electric vehicles such as facilities, technological features and benefits in the long run that have significant effect in effecting the perception of Gen Z in adopting electric vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Gen z student expectations towards employer attractiveness: An indian perspective
A study was undertaken to determine Generation Z management students' perceptions as to what factors would attract them to seek a career in an organization, and their expectations in this context. Over 330 students from both first- and second- year management programs participated in the self- administered printed questionnaire based on 25- item employer attractiveness scale and included additional items like choosing the top five items from the 25 list. A focus group discussion was also undertaken to determine what the perceived benefit to the students was for each of the 25 items would deliver to them. The study also compared the findings with published research and notes the common factors in terms of employer branding, employee engagement industry studies on work and links to other models like Herzberg's hygiene factors. The results were compared to similar studies conducted in Sri Lanka and Australia and substantive similarities were reported. 2024, IGI Global. All rights reserved. -
Gender and Ethnicity Recognition System Based on Convolutional Neural Networks
The classification of Gender and Ethnicity has been utilized in diverse scenarios, specifically in the realm of human-computer interaction, visual surveillance, and electronic customer services. Predicting the gender and ethnicity of individuals presents a significant obstacle due to its complex characteristics. The escalating prevalence of social media has emphasized the utmost importance of independently predicting gender and race. In this research endeavor, a framework is utilized which utilizes a Convolutional Neural Network to forecast gender and ethnicity by utilizing various outputs starting from the initial stage. The models performance was evaluated using different metrics, including the F1-score, accuracy, precision, recall, and accuracy. The methodology is evaluated using the UTKFace dataset for predicting gender and ethnicity, and compared the model with previous study to understand which model is giving better accuracy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Gender and Ontological Friction in Endometriosis Diagnosis
Endometriosis often struggles to consolidate as a recognizable condition during biomedical encounters; in India, this unfolds at the interstice of biomedicine and gender-caste-class persistence. We contribute to contemporary endometriosis discourse by considering how, given the ontological politics of Indian womens reproductive health care, symptoms move across porous configurations without consolidating as disease. Through reflexive thematic analysis, informed by feminist new materialism, of interviews with nine cisgender women in urban India, we trace ontological friction and its influence on patient care practices. We identify this friction as the condition through which diagnostic delay continually emerges. 2026 Society for Menstrual Cycle Research. -
Gender as a Predictor in the Perception of Sexual Harassment Definition
Sexual harassment is a pervasive problem across the globe and it is generally viewed subjectively. The review of the literature suggests that individual perception, history of past sexual harassment and other personal factors influence beliefs concerning the seriousness of the problem. The present study aims to explore the role of socio demographic variables in the definition of sexual harassment. One hundred and sixty-one college students volunteered for this study. Personal profile sheet and Sexual Harassment Definition Questionnaire were used to collect the data. The results of the chi-square test suggested that girls and students who already experienced sexual harassment found larger social incidents as harassment. However, the results of logistic regression found gender as a strong predictor of sexual harassment definition and the history of past harassment was failed to provide a statistical significance. Educating men on male privilege, violence against women and identifying behaviours in them that are not acceptable by women will be helpful. The Electrochemical Society -
Gender Differences in Social Capital and Job Search Methods in the Information Technology Industry in Bangalore
The Indian Journal of Economics Vol. 55, No. 3, pp 501-917, ISSN No. 0971-7927