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BRICS VS. G7: A COMPARATIVE ANALYSIS OF ECONOMIC AND POLITICAL EFFICIENCY IN SHAPING GLOBAL ORDER
The global distribution of power is increasingly shaped by the competing influences of two major blocs: BRICS (Brazil, Russia, India, China, and South Africa) and the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This paper investigates how BRICS and the G7 shape the emerging multipolar global order. Using comparative analysis of key indicators: GDP, trade flows, investment patterns, diplomatic engagement, and strategic alliances. The paper examines each blocs structure and internal cohesion. The analysis underscores the G7's historical supremacy, which stems from its economic strength and political unity, in contrast to BRICS rising role as a representative for the Global South and a platform for alternative governance models. Important metrics include trade flows, investment trends, diplomatic efforts, and strategic alliances. The research also assesses the internal dynamics within each bloc, including challenges to cohesion and the effectiveness of decision-making. By comparing the advantages and drawbacks of BRICS and G7, this paper provides insights into their respective functions in a multipolar world order, evaluating their ability to promote transformative global agendas. Lastly, the paper concludes that both alliances embody divergent approaches to global governance, reflecting deeper shifts in international collaboration, competition, and the balance of power. 2025, Observare. All rights reserved. -
Breeding Potential of Crosses Derived from Parents Differing in Overall GCA Status for Productivity per se Traits and Powdery Mildew Disease Response in Blackgram [Vigna mungo (L.) Hepper]
Background: Predicting the breeding potential of crosses in terms traits means, genetic variability and frequency of desirable transgressive segregants in early segregating generations is crucial in breeding programme. Therefore, an experiment was carried out to assess breeding potential of crosses involved parents with varying overall GCA status and contrasting responses to powdery mildew disease (PMD) in blackgram. Methods: Total of 40 F1 s developed by following Line Tester design; among, nine crosses were selected based on gca status of parents and responses to PMD. F1, F2 and F3 along with parents of six and three crosses were evaluated for 10 productivity per se traits and responses to PMD separately during kharif, 2016 and rabi, 2016-17 respectively. The traits means, absolute and standardized range, PCV and frequency of transgressive segregants in F2 and F3 were compared to assess the breeding potential of the crosses. Result: F2 and F3 generations derived from six crosses (for productivity traits) and three crosses (for PDI) were differed for means, absolute and standardized range, PCV and the frequency of transgressive segregants. This is may be due to the contribution of diverse genes from female and male parent. Though considerable number of transgressive segregants were also identified in F2 and F3 of all the crosses, high frequency of desirable transgressive segregants was observed in crosses involved parents with overall high GCA status. 2024, Agricultural Research Communication Centre. All rights reserved. -
Breeding distrust during artificial intelligence (AI) era: howtechnological advancements, jobinsecurity and job stress fuel organizational cynicism?
Purpose: This study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees views on their work future and its probable influence on their job behaviors in this era of technology. Design/methodology/approach: A quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling. Findings: The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employees cynicism. Practical implications: The current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress. Originality/value: The study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements. 2024, Emerald Publishing Limited. -
Breast Cancer Survival Prediction using Gene Expression Data
Breast cancer is one of the most common forms of cancer in the world.[1]. Breast, skin, colon, pancreatic, and other 100 types of cancer have founded globally. An accurate breast cancer prognosis can save many patients from having unnecessary treatment and the huge medical costs that come with it. Multiple gene mutations can possibly transform a normal cell into a cancerous one. Genomic variations and traits have a significant effect on cancer. Genetic abnormalities caused by various circumstances drive numerous efforts to find biomarkers of breast cancer advancement. Early Detection of Cancer types is the only way to recover the patients from this acute disease. In this paper, a proposed Deep learning algorithm and Machine learning algorithms are used to predict the survival of cancer patients using clinical data and gene expression data. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset is split into clinical and gene data for detailed preprocessing. This proposed method gives a better understanding of the condition and assesses how effective treatment methods are by using Deep Learning and Machine Learning models on gene data. Logistic Regression is the most accurate method identified. Grenze Scientific Society, 2022. -
Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning
Breast cancer remains one of the major reasons for cancer deaths in women, which is why it is key to develop and improve diagnostic systems for accurate predictions. Currently, the advent of Machine learning has helped in providing powerful algorithms to achieve advancements in cancer detection. However, the main motivation of this research is to focus on building more complex ensemble architectures, as they are known for significantly improving predictive accuracy, robustness, and generalisation, especially in performing complex tasks such as medical diagnosis. In this research, a Hybrid stacking ensemble was built using two gradient boosting techniques, XGBoost and LightGBM, with a Logistic Regression meta-learner to predict breast cancer and compare their performance with standard classifiers. The Breast Cancer Wisconsin (Diagnostic) dataset, which consists of 569 patient records, was utilised for model training and analysis. The data was preprocessed using Z-score normalisation and stratified 5-fold cross-validation. The machine learning algorithms, such as Decision Tree, Logistic Regression, and Random Forest, were compared with the hybrid model, and the metrics used for comparison were accuracy, precision, recall, F1-score, and ROC-AUC. The proposed hybrid model performed well, achieving a high accuracy rate of 97.37% and a recall rate of 93.00% for malignant cases. McNemar's test (p > 0.05) confirms that this accuracy rate is statistically equivalent to the Random Forest classifier. These findings proved that the proposed model can perform optimally in predicting complex data with the same degree of precision as the standard models. Therefore, the hybrid model can be considered a robust and reliable new alternative for breast cancer prediction. 2026 IEEE. -
Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning
Breast cancer diagnosis requires accurate diagnostic tools that are both efficient and interpretable for clinical deployment. This study presents an integrated pipeline combining Recursive Feature Elimination with Cross-Validation (RFECV), Synthetic Minority Over-sampling Technique (SMOTE), and ensemble learning methods applied to the Wisconsin Breast Cancer Diagnostic dataset. RFECV achieved dimensionality reduction from 30 to 17 features, representing a 43% reduction while maintaining predictive performance. SMOTE transformed the class imbalance ratio from 1.68:1 to a perfect 1:1 balance. A comprehensive evaluation of twelve machine learning models revealed that LightGBM attained an F1-score of 0.9722, accuracy of 96.5%, and ROC-AUC of 0.9914 with strong cross-validation stability (0.9681 0.0179). Feature importance analysis identified worst perimeter, area, and concave points as the most discriminative features for differentiating malignant from benign tumors. The proposed approach achieved a 35% reduction in training time compared to full-featured models without sacrificing performance. This reproducible pipeline demonstrates practical clinical relevance for automated breast cancer diagnosis with improved computational efficiency and model interpretability. 2025 IEEE. -
Breast cancer detection: A comparative review on passive and active thermography
Breast cancer is the main cause of death among women due to cancer. Early detection is crucial in controlling the disease. Thermography is a non-invasive imaging method that uses temperature differences on the breast surface to identify tumors. This paper focuses on the various aspects of thermography as a diagnostic tool for detecting breast cancer. It includes a review of the currently existing active thermography approaches used to energize the tumor cell to enhance the thermal contrast on the surface. The comparison of passive and active thermography showed that active thermography was more effective, increasing depth-dependent performance from 3 mm to 9 mm for 1.5 mm sized tumors and accuracy from 54% to 82% without a rise in false positive rates. The contrast between malignant and benign tissue also improved from 0.6 C to 0.9 C, indicating that active thermography increases the performance of passive thermography in various aspects. A comparative study of active thermography reveals that healthy tissues are likely to be damaged if the input parameters are not regulated properly. A comprehensive comparison of various tumor estimation algorithms in the paper concludes that the dynamic analysis using an active approach outperforms static analysis due to a significant decrease in error percentage. 2023 Elsevier B.V. -
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors. -
Breast Cancer Classification Using Machine Learning A Study
Nowadays, breast cancer is the most common disease found in women. Although many researchers and experts have aimed to discover the solution to this widespread disease, they have not determined it. In this study, the techniques that are used to find the early signs of breast cancer with the use of machine learning (ML) are discussed. ML is an emerging technology in the field of computer science and information technology, especially in disclosing medical diagnoses. ML is also used, for example, in image recognition, speech recognition, traffic prediction, virtual personal assistants, and online fraud detection. There are plenty of algorithms and techniques that are used in ML. Some of the most popular techniques are discussed in this study. 2025 selection and editorial matter, A. Malini, Surbhi Bhatia Khan, S. Kayalvizhi, and Mohammed Saraee; individual chapters, the contributors. -
Breaking the Taboo: Addressing Menstrual Health Challenges in India
Although, menstrual hygiene is a topic as ancient as mankind, it has recently garnered attention because society is more willing to face its difficulties. Adolescents seldom talk about issues related to menstruation, menstruation disorders, menstrual cleanliness, and customs of their culture. There is little data on the hardships that teenage females bear from menstruation and their social norms. Adolescent health education must include information about menstruation. Menstrual behaviors are often greatly influenced by culture, awareness, and social condition. However, periods, behaviors, and problems are seldom included in health education programs for the younger girls in impoverished nations. International health organizations such as WHO and UNICEF have advised developing culturally responsive menstrual health management (MHM) as well as water, sanitation and hygiene (WASH) programs for the adolescent girls. Without an awareness of the preconceived notions and prejudices that teenage girls in poor nations currently have about menstruation, these programs cannot be implemented. The goal of this review from India was to record the myths that are currently in circulation concerning menstruation, menarche, and other understudied menstrual constraints. Our goal in conducting this review was to characterize and assess the effectiveness of menstruation education programs designed to provide early teenage girls the information and abilities they need to support menstrual health. RJPT All right reserved. -
Breaking the Glass Ceiling: Will the Role of Organizational Workplace Policies Perpetuate or Mitigate Gender Bias?
Despite significant global progress in narrowing gender gaps, inequality persists across many countries. Organizations like the Global Gender Gap Index and the European Institute for Gender Equality monitor improvements in political leadership, economic opportunities, health, and education. However, women continue to face challenges, including unequal pay, limited career advancement, and imbalanced household labor. The "glass ceiling" refers to invisible barriers that prevent women from achieving top positions despite equal qualifications. Long-term effects include temporary employment and lower retirement savings. True gender equality requires more than quotas-it demands equitable opportunities, flexible work policies, pay transparency, and mentorship programs. Tackling unconscious bias and fostering inclusive environments is essential for sustainable change and women's holistic success. 2026, IGI Global Scientific Publishing. All rights reserved. -
Breaking the Cycle of Child Labor in Dehradun: A Multidimensional Study on Causes and Challenges
Child labor remains a critical issue, particularly in developing nations like India, where millions of children are deprived of their right to education and safe childhood due to poverty, marginalization, and inadequate policy implementation. This study explores the underlying causes, occupational patterns, and harmful consequences of child labor in Dehradun, Uttarakhand. It highlights that children are primarily engaged in agriculture, domestic work, and small industries, often facing physical and mental exploitation. Despite existing legal frameworks and initiatives like the Child Labor (Prohibition and Regulation) Act and National Child Labor Projects, enforcement challenges persist. The study uses cross-sectional and triangulation methods to analyze data from 42 child laborers and suggests a holistic approach, including poverty alleviation, quality education, community empowerment, and rehabilitation services, as key to combating child labor. It emphasizes the need for multi-stakeholder participation to ensure child rights and reduce labor dependency among vulnerable families. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Breaking the Bias: Assessing Nudge Effectiveness in Overcoming Decision-Making Prejudices
Influencing consumer decision-making processes in the digital age is vital as E-commerce continues dominating the market. This research work explores the application of nudging to e-commerce or any digital environment involving consumer choices, investigating its efficacy in mitigating cognitive biases that affect online purchasing decisions. We employed a 2 x 2 within-group experimental design to examine how different nudges influence e-commerce choices. Data collected from 88 participants reveal that status-quo nudges can influence decision-making more than social proof nudges or salience nudges in online shopping. These results significantly impact digital marketing strategies, suggesting that carefully designed nudges can guide consumer choices by overcoming ingrained prejudices. This research also provides practical insights into consumer behaviour through subtle interventions in e-commerce settings. While nudging has been studied in offline contexts, applying it to e-commerce and digital consumer choices represents a growing area of study. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Breaking News Recognition Using OCR
Identifying and recognition of breaking news in most of the TV channels in different backgrounds with varying positions from a static image plays a significant role in journalism and multimedia image processing. Now a days its very challenging to isolate only breaking news from headlines due to overlapping of many categories of news, keeping all this in mind, a novel methodology is proposed in this paper for detecting specific text as a breaking news from a given multimedia image. Basic digital image processing techniques are used to detect text from the images. The methods like MSER (Maximally Stable Extremal Regions) and SWT (Stroke Width Transform) are used for text detection. The proposed work focuses on extraction of text in breaking news images also discusses the different methods to overcome existing challenges in text detection along with different types of breaking news datasets collected from various news channels are used to identify text from images and comparative study of different text detection methods. The comparative study proves that MSER and SWT is a better technique to detect text in images. Finally using OCR (Optical Character Recognition) technique to extract the breaking news text from the detected regions will help in easy indexing and analysis for journalism and common people. Extensive experiments are carried out to demonstrate the effectiveness of the proposed approach. 2019, Springer Nature Singapore Pte Ltd. -
Breaking down Vicarious Trauma: Supporting Trauma Workers Who Work among Survivors of Sex Trafficking in India
Trauma workers who work at the grassroots levels of sex trafficking rehabilitation are face to face with the survivors struggle. Their roles are essential to lay the foundations of recovery and reintegration for the survivors. This article focuses on what vicarious trauma means and how acknowledging its presence as an occupational hazard will help shape organization policies and structures in a way that empowers trauma workers to continue to bring quality work from an intentional, supported, and grounded space. 2025 Indian Journal of Social Psychiatry. -
Breaking down the barrier: exploring queer young adults experience with counselling
This study explores the impact of counselling on the mental health of queer young adults in India. The research uses qualitative methods, including semi-structured interviews, to understand how counselling impacts their identities and well-being. The study involved 12 participants aged 1825, representing diverse genders and sexual orientations across different regions of India. Thematic analysis revealed three main themes: Queer Identity: Recognized, Misrecognized, and Negotiated in Therapy, Queer recommendations for inclusive care, and Empowering queer mental health journey. Each main theme revealed three sub-themes. The study emphasises the importance of therapists competence, empathy, and affirmative practices in creating a supportive therapeutic environment for queer young adults. The participant narratives highlight the complex character of the therapeutic process, going beyond professional interventions to include concepts like belonging, empowerment, and validation. 2026 College of Sexual and Relationship Therapists. -
Brand Value: Nexus with Profitability and Value Relevance Indian Evidence
The paper studied the association between brand value and financial profitability metrics, value relevance, and excess market returns. The study used the dollar value data of BrandZ Top Indian Brands as the proxy for brand value and used 221 firm years for a sample of 72 companies that owned the top brands for 5 years, from 2014 2018. The study deployed the fixed effects model to find the association between profitability, firm value, and brand value and the Fama French four-factor model for the risk-return performance of high-brand value stocks. The findings indicated a strong association between the brand values of firms and profitability and firm value. The portfolio returns of high-brand value companies produced higher risk-adjusted returns over market returns offered by BSE 100 stocks. This is Indias first and most comprehensive study to provide empirical evidence on the nexus between brand value, profitability, and value relevance. The results gave a concrete conclusion that building brand value offers both customer satisfaction as well as shareholder value creation. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Brand together: How co-creation generates innovation and re-energizes brands /
Vels Management Journal, Vol-1 (2), pp. 98-100. ISBN-978-0-7494-6325-0 -
Brand review scale for brand management /
Patent Number: 202211039578, Applicant: Dr. Mahesh Chandra Joshi.
Big firms have enough resources for various activities such as branding, market research, innovation, product development etc. which are very important for survival and growth of an organization. Small firms also wish to execute these activities but many times resource constraints refrain them form activities like market research which either requires inhouse research team or hiring of external agency for the task.