Browse Items (16481 total)
Sort by:
-
Genetics Analysis and Identification of SSR Makers Linked to Downy Mildew Resistance Through BSA in Cucumber (Cucumis sativus L.)
The research was carried out to divulge the genetics of inheritance, nature of gene action, correspondingly to identify the SSR markers linked to downy mildew resistance in cucumber. The parents Swarna Agethi and IIHR-438 were used and developed six generations (P1, P2, F1, F2, BC1 and BC2), the parents, filial generations and backcross populations were deployed for genetic studies. The Mendelian segregation suggested that the downy mildew resistance was governed by two pairs of recessive genes with inhibitory recessive epistasis (3 resistant: 13 susceptible) gene interaction. IIHR-438 possessed a higher degree of resistance; and epistatic interaction (additive dominance) was of greater importance than the main effect. Bulk segregant analysis (BSA) aided in identifying the two SSR markers (SSR 35 and SSR 413), which had polymorphism between resistant and susceptible bulk, and the markers among backcross population segregated in an equal (1:1) ratio. Genetic distance between identified markers was found to be 16.6 cM from the QTL IciMapping V3.2 software, which depicted two new quantitative trait loci (QTLs) on chromosome number 3. These findings on genetics of downy mildew resistance provide an implication to advance the reciprocal recurrent selection followed by pedigree selection to aid in the development of resistant varieties in cucumber. 2025 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd. -
Genome analysis for precision agriculture using artificial intelligence: a survey
Precision agriculture is a farm management technique which uses the help with the help of information technology to ensure that the crops and soil receive exactly what is required for optimum health and productivity. Genome analysis in plants helps to identify the plant structure and physiological traits. The identification of the right plant genome and the resulting traits help to optimize the cultivation of the plant for better productivity and adaptability. Genome analysis helps the biologist edit the plant genetic makeup structure to make the plant to adapt to the current conditions and thereby reducing the use of fertilizers. For precision agriculture, artificial intelligence techniques help to understand the relationships between plant genome and soil nutrient conditions that help in precision farming effectively reducing the usage of fertilizers by modifying the plants to adapt with the current soil characteristics. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Genotoxic repercussion of high-intensity radiation (x-rays) on hospital radiographers
Recent technological advances in the medical field have increased the plausibility of exposing humans to high-intensity wavelength radiations like x-rays and gamma rays while diagnosing or treating specific medical maladies. These radiations induce nucleotide changes and chromosomal alterations in the exposed population, intentionally or accidentally. A radiological investigation is regularly used in identifying the disease, especially by the technicians working in intensive care units. The current study observes the genetic damages like chromosomal abnormalities (CA) in clinicians who are occupationally exposed to high-intensity radiations (x-rays) at their workplaces using universal cytogenetic tools like micronucleus assay (MN), sister chromatid exchange and comet assay. The study was conducted between 100 exposed practitioners from the abdominal scanning, chest scanning, cranial and orthopedic or bone scanning department and age-matched healthy controls. We observed a slightly higher rate of MN and CA (p <.05) in orthopedic and chest department practitioners than in other departments concerning increasing age and duration of exposure at work. Our results emphasize taking extra precautionary measures in clinical and hospital radiation laboratories to protect the practitioners. 2022 The Authors. Environmental and Molecular Mutagenesis published by Wiley Periodicals LLC on behalf of Environmental Mutagen Society. -
Genuine handwriting variations in 10 years: a pilot study
Background: The present study aims to examine the extent of variation in genuine handwriting characteristics across 10years. One hundred samples (one admitted handwriting and three exemplars) were collected from 25 subjects (male and female, age ranging from 30 to 55) using purposive sampling technique. The admitted handwriting sample included documents like notebooks, wills, diaries, and record books that had been written 10years earlier, and 3 exemplars with the same information, written now in a similar kind of material. Both individual and class characteristics were analyzed in admitted as well as three exemplars which includes size of letters, slant, i-dot, t-bar (diacritics), humped letters (m, n, h), and formation of rounded letters (o, a, d, b, g, p, q). Results: Cohens kappa showed that there is a significant agreement between admitted and exemplars in the characteristics except for size. Conclusion: The results imply that once an adult has acquired a particular handwriting pattern, the master pattern of each letter, as well as both class and individual characteristics, remain unchanged. The size of the letters may change across age. 2019, The Author(s). -
Geo-spatial crime analysis using newsfeed data in indian context
Social media is the platforms where users communicate, interact, share ideas, career interest, pictures, video, etc. Social media gives an opportunity to analyze the human behavior. Crime analysis using data from social media such as Newsfeeds, Facebook, Twitter, etc., is becoming one of the emerging areas of research for law enforcement organizations across the world. The intelligence gathered through data is used for identifying future attacks and plan for reinforcements. This article focuses on the implementation of textual data analytics by collecting the data from different newsfeeds and provides an optimized visualization. This article establishes a framework for better prediction of 16 types of crime in India and the Bangalore area by providing the coordinates of the crime area, along with the crime which might happen there. 2019, IGI Global. -
Geo-spatial crime density attribution using optimized machine learning algorithms
Law enforcement agencies use various crime analysis tools. A large amount of crime data has enabled crime analysis. In this paper, the proposed research methodology uses Kernel Density Estimation (KDE) in a Geographical Information System (GIS) to analyze crime-type data. Bangalore and India newsfeeds are considered for experimental purposes. The paper introduces an optimized KDE machine learning algorithm that detects hotspots, estimates a locations crime rate, and identifies point pattern lows and highs. As a result of the experiment, the proposed methodology identified that the bandwidth of the Geographical information system influences the visualization of crime density. The paper also aids in visually determining the appropriate bandwidth for the problem using an optimized KDE algorithm. We had identified a significant correlation between Newsfeed data and Official Government data, both overall Crime and by crime type. The proposed KDE model achieved a predictive performance of 77.49%. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
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. -
Geodetic sets in n-inordinate invariant intersection graphs
Algebraic graph theory is a rapidly growing research area in which several graphs based on algebraic structures are introduced and investigated. The algebraic intersection graphs, called the n-inordinate invariant intersection graphs, and the n-inordinate invariant non-intersection graphs, have been constructed on the symmetric group and various properties of these graphs are studied, in the literature. In this article, we analyse the structure of these graphs by examining different types of geodetic sets in them. 2025, Ankara University Faculty of Science. All rights reserved. -
Geographical and Gender Disparities in Financial Inclusion Diffusion in India
Financial inclusion is providing an opportunity to use essential banking and financial services to the less-privileged people and their businesses in order to accomplish an inclusive society and the inclusive economy. The efforts of policy makers towards achieving financial inclusion in India yielded fruitful results. Numbers of savings accounts, numbers of credit accounts, numbers of deposits, numbers of ATMs, and loan distribution to the micro and small enterprises have significantly improved in recent times. This study intends to provide answer to the question raised by examining the penetration of financial inclusion area wise, region wise and based on gender. This study has employed descriptive research design and has used secondary data for analysis. The study has found that there are geographical and gender disparities in financial inclusion penetration and financial inclusion penetration varies in terms of gender as well in India. Indian Institute of Finance. -
Geographical Approaches to Global Sustainable Development Goals in BRICS Countries
Geographical factors play a key role in the realization of SDG's across all countries of the world. Present study is an endeavor in this direction and attempts to evaluate the impact of urbanization, agriculture, and water resource availability on the achievement of sustainable development of BRICS nations. The study employs Panel data analysis using Fixed Effects Model (FEM) and Random Effects Model (REM) along with the application of Hausman Test to determine the appropriate model selection. The results reveal that urbanization significantly enhances SDG progress, agriculture shows an insignificant effect, and water resource availability negatively impacts sustainability, indicating the urgent need for strategic urban planning, agricultural reforms, and efficient water management. The study also highlights that country-specific factors play a critical part in shaping sustainability outcomes, reinforcing the necessity for BRICS policymakers to adopt geographically tailored approaches that align economic growth with environmental sustainability. 2026, IGI Global Scientific Publishing. -
Geographies of Gender and Leadership: Regional Inequalities and Women in Omans Oil & Gas Sector
Introduction / Main Objective: This research explores Omani women's spatial and cultural barriers to leadership in the Oil & Gas industry. It looks at geographic location and regional differences and how these affect women's access to and experience of leadership. Background of the Problem: While significant advancements have been made in women's participation in the labor force in Oman, spatial disparities still exist whereby women in urban areas have more chances of leadership compared to women in rural or rural-remote towns. Cultural expectations and infrastructural constraints add to these geographical disparities. Novelty: This study innovatively combines a geographic perspective in gender and leadership research in Oman's Oil & Gas sector with an emphasis on regional disparities influencing women's career development. Research Methods: A qualitative method involving questionnaires was conducted among thirty women managers within various regions of Oman. Responses were processed to determine spatially connected obstacles and coping mechanisms. Findings: Women in urban areas like Muscat enjoy better access to education, professional networks, and organizational support, while women in rural areas are confronted by cultural conservatism, poor infrastructure, and lower promotion opportunities. There was an urban-rural leadership training and family support divide. Conclusion: Geography profoundly influences women's leadership paths in Oman's Oil & Gas industry. For policies to enhance gender equity, leadership development policy needs to take regional disparities into consideration and adapt interventions to local contexts. 2025, Green Publication. All rights reserved. -
Geometric aspects of noncommutative wormholes with conformal symmetry
This paper investigates traversable wormhole solutions within the framework of (Formula presented) f(Q,T) gravity by incorporating conformal symmetry and employing a Lorentzian distribution to model the matter sources. The study considers different equations of state, such as traceless and barotropic forms, to explore their impact on the viability of wormhole solutions. A comprehensive analysis of the effects of the model parameters on the wormhole geometry and its physical properties is carried out. The findings reveal that the resulting shape function satisfies all the necessary wormhole conditions. Importantly, certain scenarios are identified where the wormhole can be supported by non-exotic matter, highlighting the physical plausibility of such solutions within modified gravity. 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the https://publishingsupport.iopscience.iop.org/iop-standard/v1. -
Geometry of generalized Ricci-type solitons on a class of Riemannian manifolds
In this paper, the notion of generalized Ricci-type soliton is introduced and its geometrical relevance on Riemannian CR-manifold is established. Particularly, it is shown that a Riemannian CR-manifold is Einstein when its metric is a generalized Ricci-type soliton. Next, it has been proved that a Riemannian CR-manifold is Einstein-like, when its metric is a generalized gradient Ricci-type almost soliton (or generalized Ricci-type almost soliton for which the soliton vector field is collinear to the CR-vector field). Finally, we present an example of generalized Ricci-type solitons which illustrate our results. 2022 Elsevier B.V. -
Geometry of Variably Inclined Inviscid MHD Flows
A steady plane variably inclined magnetohydrodynamic flow of an inviscid incompressible fluid of infinite electrical conductivity studied. Introducing the vorticity, magnetic flux density, and energy functions along with the variable angle between magnetic field and velocity vector, governing equations are reformulated. The resulting equations are solved to analyze the geometry of the fluid flow. Considering streamlines to be parallel, stream function approach is applied to obtain the pattern for magnetic lines and the complete solution to the flow variables. Next considering parallel magnetic lines, magnetic flux function approach is applied to obtain streamlines and the complete solution of the flow. A graphical analysis of pressure variation is made in all the cases. 2020, Springer Nature Singapore Pte Ltd. -
Geopolitical Risk, Variability of Oil Price, and the Global Trade Uncertainty: An Economic Perspective
Oil prices are the outcome of a highly integrated, dynamic global system. With geopolitical risk and trade policy uncertainty contribute to the volatile world markets, the study analyses the impact of geopolitical risk, trade policy uncertainty on crude oil price globally. It considers data from 2000 to 2023 and finds out systematic link between the three variables. The data proved a long run impact of trade policy uncertainty and geopolitical risk on the crude oil. This enhances the influence of the variables and leads to implementation of policy measures in global markets. The data are tested through Auto Regressive Distributed Lag (ARDL) model and checked for long run cointegration and short run association. The policy, thus, has been suggested as to improve the transition towards sustainability globally. Though the impact of geopolitical risk and trade policy uncertainty is not strong on crude oil price in world market, it can affect the short run volatility. Thus, mitigation, diversification and transparency are the key factors to strengthen the existing situation in world. 2026, IGI Global Scientific Publishing. All rights reserved. -
Geopolitical shockwaves: the Russia-Ukraine wars impact on BRICS financial markets
The Russia-Ukraine War triggered global financial market turmoil and disrupted the global supply chain, including agriculture and energy. This study explores the impact of the Russia-Ukraine war on BRICS nations stock markets, highlighting varying degrees of volatility and contagion effects. It examines the extent of contagion in the BRICS stock markets and their financial linkages by employing the multivariate DCC-GARCH model. The study reveals sensitive turbulence in Russian markets post-crisis, influenced by its direct involvement in the conflict. Brazil and China experienced higher market volatility after the event, and Brazil shifted its financial linkages with the global market. Conversely, the Indian market experienced eased overall volatility, but its financial linkage with Russia has increased due to its trade partnership. In the post-event period, China and South African markets indicate structural market decoupling. The long-term volatility persists over the short-term volatility of BRICS market dynamics. This study underscores the implications for investors and policymakers, emphasising the need for adjustments in monetary and fiscal policies to stabilise financial markets amid geopolitical uncertainties. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Geopolymer concrete paving blocks made with Recycled Asphalt Pavement (RAP) aggregates towards sustainable urban mobility development
Policy makers in India have realized the importance of facility for pedestrians and non- motorized vehicles in an urban infrastructure setup. This has resulted in increased utilization of construction materials like Portland cement and crushed stone, which are not environmentally friendly and sustainable. The current study presents the development of paver blocks for pedestrian facility using different wastes. Geopolymer concrete was synthesized by fly ash and recycled asphalt pavement aggregates for making of paver blocks. Paver blocks were produced in laboratory with recycled asphalt pavement aggregate replacement levels of 0%, 20%, 40%, 60% and 80% by weight of virgin coarse and fine aggregates. The developed paver blocks were tested for dimensions and tolerances, water absorption, compressive strength and abrasion resistance as per IS15658:2006 standard. The results of the laboratory study show that recycled asphalt pavement aggregates can be introduced into geopolymer matrix to produce paver blocks of desirable quality. Furthermore, its use in pedestrian facilities provides a new avenue for managing the excessive waste, which otherwise goes in landfills, incurring loss to the paving industry. Therefore, the proposed method can help decision makers to effectively utilize recycled asphalt pavement in paving industry with environment-friendly approach. 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Geospatial Analysis of Groundwater Recharge Zones in Bengaluru
Urban flooding in cities like Bengaluru results from excessive rainfall overwhelming drainage systems, worsened by rapid urbanisation and the expansion of impervious surfaces. This study investigates the causes and consequences of urban flooding in Bengaluru, highlighting the decline in natural drainage and the encroachment of water bodies. Using QGIS, a geographic information system tool, spatial data from sources like NRSCs Bhuvan portal and USGS were analysed to identify flood-prone areas, drainage networks, and land use changes. The analysis revealed critical flooding zones such as Bellandur, Bommanahalli, and Mahadevapura. The study also emphasises the importance of implementing Best Management Practices (BMPs) and Rainwater Harvesting (RWH) strategies. Land Use and Land Cover (LULC) mapping, soil infiltration data, and rainfall patterns were assessed to understand urban hydrology. The findings stress the need for climate-resilient infrastructure, lake rejuvenation, and improved public awareness to mitigate future urban flood risks in Bengaluru. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Geospatial crime analysis and forecasting with machine learning techniques
People use social media to engage, connect, and exchange ideas, for professional interests, and for sharing images, videos, and other contents. According to the investigation, social media allows researchers to examine individual behavior features and geographic and temporal interactions. According to studies, criminology has become a prominent subject of study globally, using data gathered from online social media sites such as Facebook, News feed articles, Twitter, and other sources. It is possible to obtain useful information for the analysis of criminal activity by using spatiotemporal linkages in user-generated content. The study refers to the application of text-based data science by gathering data from several news sources and visualizing it. This research is motivated by the abovementioned work from various social media crimes and government crime statistics. This chapter looks at 68 various crime keywords to help you figure out what kind of crime you are dealing with concerning geographical and temporal data. For categorizing crime into subgroups of categories with geographical and time aspects using news feeds, the Naive Bayes classification algorithm is used. For retrieving keywords from news feeds, the Mallet package is used. The hotspots in crime hotspots are identified using the K-means method. The KDE approach is utilized to address crime density and this methodology has solved the difficulties that the current KDE algorithm has. The study results demonstrated equivalence between the suggested crimes forecasting model as well as the ARIMA model. 2022 Elsevier Inc. All rights reserved. -
Geospatial crime analysis to determine crime density using kernel density estimation for the indian context
Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime. 2020 American Scientific Publishers.