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Patriarchal Constraints in Everyday Lives: Gender Roles, Matrilineality, and the Status of Contemporary Khasi Women
The Khasi tribe from Meghalaya in northeast India practices a matrilineal system, which is believed to be more egalitarian than patrilineal systems. The women of the Khasi tribe are often regarded as having a higher status than other women in India. However, despite belonging to a matrilineal society, Khasi women still face challenges in their social lives stemming from patriarchal constructs. This qualitative study examines the social status and subsequent challenges faced by Khasi women in contemporary India. Using in-depth interviews and observations of thirty urban and rural Khasi women in the East Khasi Hills District of Meghalaya, the study reveals how Khasi women experience contradictory and challenging roles, relationship dynamics, and gender stereotypes in their lives. More studies should examine the problems and challenges that Khasi women face in their society despite the benefits of a matrilineal system. 2026 Bridgewater State College. All rights reserved. -
HEART FAILURE DETECTION USING OPTIMIZATION ALGORITHMS
Heart failure (HF) remains a significant global health challenge, requiring early and precise detection to improve clinical outcomes and reduce mortality rates. Traditional diagnostic approaches often fail to capture the complexity of HF pathophysiology, necessitating advanced computational methods for accurate prediction. In this study, we propose a novel optimized Stacked Support Vector Machine (S-SVM) framework, integrating multiple SVM classifiers with diverse kernel functions to enhance predictive accuracy. A genetic algorithm (GA) is employed to fine-tune hyperparameters, ensuring model robustness and generalizability across patient populations. The model is rigorously evaluated on the UCI Heart Failure Clinical Records Dataset and the Framingham Heart Study Dataset, demonstrating superior performance in accuracy (95.7%), precision (0.90), recall (0.87), and AUC (0.96) compared to conventional machine learning techniques. The proposed system effectively balances computational efficiency with clinical interpretability, making it a promising tool for early-stage HF detection and risk stratification. This research advances the intersection of machine learning and cardiovascular diagnostics, offering a scalable and adaptive solution for real-world healthcare applications. Little Lion Scientific. -
Building a sustainable brand in textile industry
The textile industry is at a pivotal moment where integrating sustainability into brand strategies is not merely an ethical obligation but a competitive necessity. This study highlights three key approaches-adoption of circular economy principles, implementation of blockchain technology for supply chain transparency, and the use of innovative, sustainable materials-that are shaping the future of sustainable brand development in the textile sector. -
The DREAMS Outbound Leadership Training (DREAMS OLT) for Psychosocial Empowerment Among Youth and Sustainable Development Goals (SDGs)
DREAMS Intervention Program is a three-year curriculum program for schools and Higher Educational Institutions (HEIs), aimed at developing psycho-social leadership skills through mentoring intervention. Its triangulated model connects Schools (under-served students), Universities (youth mentors), and Community (senior leaders) to transfer knowledge, experiences, and leadership skills as tools for social change. Outbound Leadership Training (OLT) is a key instrument in achieving DREAMS' goals. DREAMS OLT is an experiential program to develop higher leadership skills and build effective teams by mentoring children and teens in various DREAMS chapters. Mentor training is ongoing, developing psychosocial competencies through project execution. DREAMS OLT refines these skills with structured, activitybased experiential learning. Integrating sustainability into education advances a holistic approach to developing professional skills and leadership among youth. Such skills help the youth to deal with complex worldly challenges. Initiatives such as DREAMS OLT enable the transformation of SDG frameworks into meaningful action by nurturing responsibility, innovation, collaboration and community engagement. Education and youth leadership are critical drivers of long-term social transformation. DREAMS OLT supports SDGs: 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), 17 (Partnership for the Goals), 3 (Good Health and Well-Being), and 11 (Sustainable Cities and Communities). This paper explores how DREAMS OLT applies these SDGs through experiential, mentoring-based leadership development and psycho-social empowerment of youth. 2026 RESTORATIVE JUSTICE FOR ALL. -
STRUCTURAL PROPERTIES OF SIGNED GRAPHS ADMITTING ROMAN DOMINATING FUNCTION
A Roman dominating function(RDF) on a signed graph S = (G, ?) is a function f: V (S) ? {0, 1, 2} such that (i) (Formula presented) for every vertex (Formula presented) and (ii) for any vertex v with f(v) = 0 there exists a vertex (Formula presented) having f(u) = 2. In this article we explore structural properties of signed graphs admitting an RDF. Further, signed graphs with 3-regular graph as their underlying graph are examined and characterisation of one of its subclasses, net-regular signed graphs admitting an RDF is obtained. I??k University, Department of Mathematics, 2025; all rights reserved. -
EDGE INCIDENT 2-EDGE COLORING SUM OF GRAPHS
The edge incident 2-edge coloring number, ?ein2(G), of a graph G is the highest coloring number used in an edge coloring of a graph G such that the edges incident to an edge e = uv in G is colored with at most two distinct colors. The edge incident 2-edge coloring sum of a graph G, denoted as (Formula presented.), is the greatest sum among all the edge incident 2-edge coloring of graph G which receives maximum ?ein2(G) colors. The main objective of this paper is to study the edge incident 2-edge coloring sum of graphs and find the exact values of this parameter for some known graphs. I??k University, Department of Mathematics, 2025; all rights reserved. -
SHRINKAGE BASED ESTIMATION FOR STRESS STRENGTH RELIABILITY P[Y < X < Z] FOR THE EXPONENTIAL DISTRIBUTION
Estimating stress-strength reliability of the form P(Y < X < Z) is a pivotal concern in reliability analysis, particularly when systems are subjected to both lower and upper stress limits. This paper investigates the reliability measure under the assumption that the stress variables Y and Z, as well as the strength variable X, follow exponential distributions. The analysis is conducted using both complete samples and right-censored samples to reflect realistic data collection scenarios. To improve estimation efficiency, we propose several shrinkage estimators based on distinct strategies: a constant shrinkage weight factor, a modified Thompson-type shrinkage weight, and the formulation introduced by Mehta and Srinivasan (1971). The performance of these estimators is evaluated via extensive Monte Carlo simulations and compared against the conventional maximum likelihood estimator, demonstrating the relative merits and limitations of each approach. ARF India, Gurgaon, India. -
Cognitive and Motor profiles of Preterm Infants with Hypoxic Ischemic Encephalopathy (HIE): A Case Report
Hypoxic Ischemic Encephalopathy (HIE) significantly impacts infants' cognitive and motor development, necessitating tailored evaluation tools for early identification and intervention. To address this need, this study aimed to build new normative data for the Bayley Scales of Infant and Toddler DevelopmentTM, Fourth Edition (BayleyTM-4), specific to HIE. A cross-sectional analysis of 54 infants across three age groups (6, 12, and 18 months), consisting of 18 each, revealed significant deficits in the cognitive and motor domains compared with those of matched controls. These findings highlight the need for HIE-specific benchmarks to improve diagnostic accuracy and inform targeted interventions. Establishing these norms can enable clinicians to design personalized strategies to enhance developmental outcomes. 2025 EL-MED-Pub. All rights reserved. -
Laminating techniques for luxury textiles
In this paper, we delved into the transformative aspects of lamination techniques in luxury textiles. We started by explaining luxury textiles and what makes them premium: craftsmanship, exclusivity, superior ingredients, silk, cashmere, fine wool etc. They presented the lamination procedures that are considered pre-processing steps to improve the functionality, lifespan, and appearance of such textiles. -
The Middle Class in India Trends and Patterns
The middle class in India is an economically significant group, the composition of which is very diverse. Researchers often use income as an indicator to identify or measure the middle class. This article uses consumption as an indicator based on the purchasing power parity to identify the middle class. Further, it categorises the middle class into different groups based on their economic standing within its class, from the emerging middle class to the higher middle class. 2025 Economic and Political Weekly. All rights reserved. -
The Fusion of CNN and MLP Algorithm as High-Performance Classification for Identification of Healthy and Unhealthy Leaves
In this study, it encapsulates the results of the work carried out with the Convolutional Neural Network, Multi-layer Perceptron, and hybrid of CNN and MLP classifier for the recognition of a tea leaf. The leaves are categorized into distinct classes by analysis and identification and recognition, which benefits both the buyer and the farmer by enabling the seller to sell tea leaves based on the quality of the leaf. Nowadays there is more advancement in the field of agriculture. But it is always the latest subject to study in the field of agriculture for the analysis and to identify quality of leaves. Many AI methodologies can be used for identification and recognition and further their fusion with different techniques or methods that can be applied to address the issue and to acquire the better accuracy. In this CNN, MLP and the hybrid of CNN-MLP are employed for determining the accuracy and this can further help in classifying the leaf in different grades like best quality, average quality, and worst quality, as for the future scope. Then the feature selection algorithms are implemented based on the different selection methods such as ANOVA, information gain, feature importance, and the random forest, which will reduce the number of parameters, at the end classification is carried out for identification of the leaf. 2026, Greater Mekong Subregion Academic and Research Network, Asian Institute of Technology. All rights reserved. -
A COMPUTATIONAL MODEL FOR TEA LEAF PRICE PREDICTION BASED ON QUALITY FACTORS USING HYBRID MACHINE LEARNING TECHNIQUES
This document reflects the effort made to calculate and identify the grade of the tea leaves based on the assessment of the leaves' size and color. The leaves were classified based on their severity with the help of HSV. The leaves were further classified using the k prototypes clustering once their length and width were established. The leaves were then further categorized in line with that. Light, medium, and dark are the three-color categories into which it belongs. The leaves were further sorted according to their quality so that the farmer could sell the produce at a better price. With the machine learning method for the categorization part, we were able to show its values. All of the healthy leaves were considered in a different dataset, and the images were obtained using the feature selection method. The length and width of each individual leaf, along with its color and shape, were then measured using those leaves. We were able to differentiate between the various leaf grades based on the findings. The healthy leaves were separated from the diseased leaves using the textual features. Additionally, we were able to use the other criteria to obtain higher-grade leaves. Little Lion Scientific. -
Compressive Sensing Based Compression Algorithm for the Audio Signal
Nyquist sampling is used in the conventional digital converter to convert the analog audio signal to digital audio data. Traditional approaches, such as Nyquist sampling, require high sampling rates, leading to large datasets. After sampling, the digital audio data is often compressed using algorithms like Moving Picture Experts Group Layer-3 Audio Coding (MP3), Advanced Audio Coding (AAC), or other codecs to re-duce the file size for storage or transmission. Recently, Compressive Sensing (CS) has been used instead of Nyquist Sampling. Instead of sampling at the Nyquist rate, Com-pressive Sensing samples the signal at a much lower rate. The recovery of the original signal from these fewer samples is possible through minimization techniques. Hence, in the proposed algorithm, we use Compressive Sensing for audio compression. The basis matrix for Compressive Sensing is generated by exploring different transform matrices. The proposed algorithm leverages the principles of Compressive Sensing to enhance audio compression by reducing the number of samples needed and using efficient recovery techniques, resulting in a high compression rate suitable for modern audio applications. 2025. -
On Proper Diameter of Certain Classes of Graphs
An edge coloring of a graph is said to be proper edge coloring if no two adjacent edges receive the same color. A graph G is said to be properly connected if there exists a properly edge colored path between every pair of vertices. For a properly connected graph G with a k-edge coloring c, the proper diameter of a graph, pdiamk (G) is the maximum proper distance between any distinct pair of vertices in G. We investigate the proper diameter of various classes of graphs that are 2-colored and provide bounds on the values of pdiam2(G) for these graphs. Palestine Polytechnic University-PPU 2025. -
Assessing the Implementation of Climate-Smart Agricultural Practices: Key Findings and Policy Strategies
Climate change poses a significant threat to global agriculture, jeopardizing food security and the livelihoods of millions. The agricultural sector's vulnerability is evident in altered precipitation patterns, rising temperatures and an increase in extreme weather events. This study examines climate smart agriculture (CSA) as an adaptive approach to boost productivity, enhance resilience and reduce greenhouse gas emissions. Focusing on Chhattisgarh, India a region reliant on rain-fed agriculture this research assesses farmers' attitudes toward climate change and CSA adoption, identifying both challenges and opportunities. The findings reveal a mixed adoption of CSA practices, with preferences for water-smart, knowledge-smart and weather-smart technologies. Key barriers include economic constraints, insufficient awareness and limited government support. However, a high adoption rate (72.5%) of knowledge-smart practices, such as improved seed varieties, underscores the importance of agricultural institutions and information dissemination. The study recommends enhancing support for energy and carbon-smart technologies, improving access to agricultural insurance and bolstering knowledge-sharing initiatives. These insights offer valuable guidance for policymakers, agricultural extension services and farmers in promoting climate-resilient and sustainability in Chhattisgarh. 2025 - Kalpana Corporation. -
Level Up or Log Out? Exploring the Multifaceted Effects of Internet Gaming on Youths Life: Emotional Intelligence, Coping Behavior, Aggression, Procrastination & Quality of Life
Emotional Intelligence, coping mechanisms, aggression, procrastination, the quality of life are the psychological factors of students well-being examined in this study, which also emphasizes the multifaceted impacts of online gaming on youths. It reveals subtle discrepancies in results through analysis of variance involving gamers and non-gamers. Concerns continue to exist regarding the potential adverse effects of gaming despite its extensive implementation among college students. The findings indicate that individuals who engage in gaming demonstrate diminished levels of emotional intelligence, as evidenced by challenges in proficiently comprehending and regulating emotions. Moreover, excessive discontentment and procrastination result from the tendency of gamers to utilize fewer adaptive coping mechanisms when confronted with stressors. These results highlight the complex relationship between internet gaming and quality of life, which indicates that gamers generally encounter less favorable consequences than those who do not engage in gaming. By illuminating these inconsistencies, the study enhances the comprehension of the varied impacts of online gaming on young adults lives. This highlights the criticality of developing adaptive coping mechanisms and fostering positive gaming behaviors to minimize negative consequences. The research highlights the necessity for interventions that target the well-being of young gamers, with implications that transcend the realm of academia and the real world. Furthermore, it emphasizes the substantial societal ramifications associated with the increasing prevalence of online gaming and its influence on young individuals. This study provides novel perspectives on the intricate nature of online gaming and its consequential effects on diverse facets of the student experience. 2025 RESTORATIVE JUSTICE FOR ALL. -
Balancing Innovation and Tradition: An Analytical Study of the Interface between Intellectual Property and Cultural Appropriation in India
India, with its rich and diverse cultural heritage, is vulnerable to practices which may undermine its traditional knowledge. Intellectual Property (IP) rights, while designed to protect innovation and creativity, can also play a crucial role in safeguarding traditional knowledge. Cultural appropriation, which essentially refers to the unauthorized use of indigenous knowledge and practices by foreign entities who exploit these lesser-known art forms, expression or traditional knowledge relating to lifestyle and well being with an objective of commercial exploitation, has increasingly become a concern in the global south, including India. There have been several instances of cultural appropriation like patent claims on Indian traditional knowledge which have been part of local customs since time immemorial by misrepresenting the origin of the product or diluting the traditional knowledge and portraying it as a scientific breakthrough. Therefore, this paper explores the intersection of cultural misappropriation and protection under IP laws for appropriators in the context of India's heritage. This paper examines the challenges faced by indigenous communities in protecting their traditional knowledge such as loss of traditional knowledge, economic disadvantages and disrespect to the community. This paper highlights the importance of balancing the rights of indigenous communities with the broader public interest in accessing and enjoying cultural heritage. The study aims to focus on bottlenecks in the current IP system that fail to adequately protect traditional knowledge from misappropriation. It also explores the ongoing debates around fair benefit-sharing mechanisms, the importance of maintaining a traditional digital library that is accessible to all and the need for a sui generis system that aligns with the unique characteristics of traditional knowledge. The study advocates for stronger legal protections, ethical considerations and raising awareness across nations about the value of traditional knowledge which are crucial for fostering respect and preventing further exploitation. Moreover, recommendations are explored to suggest safeguarding the cultural heritage of indigenous communities in India against the backdrop of global intellectual property regimes by implementing robust IPR protection for traditional knowledge that can safeguard against unauthorized use and exploitation. 2025 Department of Law, University of North Bengal. All rights reserved. -
British Rule and Environmentalism in Wayanad The Curious Case of Kerala Varma Pazhassi Raja
[No abstract available] -
Smart-Beta Strategies in India: Analysis of Performance and Exposure of BSE Strategy Indices
The study examines the performance and exposure of BSE strategy indices and also compares them between the pre and post-covid periods. Though the monthly returns of some of the indices were significant, no indices outperformed the Sensex across the sample periods. On the risk-adjusted terms, quality, momentum, and low-volatility indices offered a significant alpha in the pre-covid, but post covid, all indices failed. The indices, except momentum, performed better in post covid compared to pre-covid but are insignificant due to the post covid high volatility. Momentum and value indices offered a predominant performance in the pre and post covid periods respectively, while the dividend index failed in both periods. While the market factor is the prominent driver of return for the indices, the size effect on their performance is insignificant across the sample periods. The indices, irrespective of their pre-covid exposure, gained significant exposure to loser stocks post covid. Though the exposure varied between pre and post covid, the change in the exposure was not significant except for the quality and dividend indices. They offer limited intended factor exposure and some extent of unintended exposure, as in the case of the momentum index in the pre-covid period. Indian Institute of Finance.. -
Connecting Inner Competencies with Workplace Behavior: Influence of Emotional and Spiritual Intelligence on OCB in IT and Non-IT
Emotional and spiritual intelligence are recognized as key resources that impact workplace behavior. This study explores their relationship with organizational citizenship behavior (OCB), the voluntary actions that improve organizational effectiveness. Using Self-Determination Theory and Social Exchange Theory, the research examines how these intelligences vary across demographic groups and their roles in OCB. Data were collected from 450 employees (272 males and 178 females) in IT and non-IT sectors using simple random sampling. Participants had experience levels from under 5 to over 11 years in their organizations. Standardized self-report measures were used, and the data were analyzed with t-tests, ANOVA, correlation, regression, and mediation analysis. The study found that females scored higher in spiritual intelligence and OCB, while emotional intelligence showed no gender differences. Married and more experienced employees exhibited higher emotional intelligence and OCB, whereas unmarried and less experienced individuals had increased spiritual intelligence. No significant differences were noted between IT and non-IT sectors. Emotional intelligence predicted OCB, but spiritual intelligence did not. Limitations included a cross-sectional design and reliance on self-reports. The findings suggest that organizations should prioritize emotional intelligence training and adapt engagement strategies based on employee demographics for a more effective workplace. NAJP.