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Maximizing Efficiency: Unveiling thePotential ofKubernetes Metrics
In the realm of Kubernetes cluster management, the importance of metrics cannot be overstated. Metrics serve as a powerful lens, providing a quantitative perspective into a clusters performance, behavior, and resource utilization. In the ever-evolving landscape of cloud-native computing, metrics are the key to informed decision-making. They empower administrators to navigate scaling, resource allocation, and the holistic optimization of Kubernetes clusters with a data-driven confidence. This paper stands as a vital contribution, placing metrics at the forefront of the discussion. It underscores their transformative potential by shedding light on how they drive administrators decisions, enable the identification of performance bottlenecks, and enhance application responsiveness. Moreover, metrics play a pivotal role in proactive capacity planning, ensuring resources are allocated with precision to meet both current and future workload demands. In essence, this papers core contribution lies in providing a comprehensive overview of Kubernetes metrics and highlighting their profound impact on Autoscaling strategies. By revealing the constraints that metrics may impose on the efficient scaling of application resources, it equips administrators with a navigational tool for building dynamic and resilient computing environments within Kubernetes clusters. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Mayfly Algorithm for Optimal Integration of Hybrid Photovoltaic / Battery Energy Storage / D-STATCOM System for Islanding Operation
In today's power system design studies, autonomous and self-healing capabilities are becoming increasingly important. Renewable energy (RE) integration, on the other hand, is geared at long-term sustainability. In this regard, a hybrid energy system consisting of a photovoltaic (PV) source, battery energy storage (BESS), and distribution-static synchronous compensator (D-STATCOM) is proposed for optimal design and integration in the electrical distribution network (EDN) when short-term islanding operational requirements are taken into account. When considering grid-connected mode, the PV system is initially optimally allocated towards loss minimization. Following that, the capacities of BESS and D-STATCOM are assessed in the context of a short-term islanding scenario. The optimization problem is tackled utilising a recent meta-heuristic mayfly optimization algorithm (MOA) in both stages. The simulations are run on an IEEE 33-bus EDN network. By having optimal PV system in grid-connected mode, it is observed that real power losses are reduced to 111.03 kW from 210.998 kW and reactive power losses are reduced to 81.684 kVAr from 143.033 kVAr. In addition, the minimum voltage in the network is raised to 0.9424 p.u. from 0.9038 p.u. On the other hand, by designing hybrid energy systems using PV, BESS, and D-STATCOM, the network is able to serve the entire load even under islanding conditions. MOA's competitiveness in solving difficult non-linear multivariable optimization problems was demonstrated in comparative research with literature publications. In addition, the proposed hybrid energy system can cope with the uncertainties and other requirements of current grids. 2022. All Rights Reserved. -
MCCLDP: Multi Class Cotton Leaf Diseases Prediction and Classification using Deep Learning Model
Cotton plant disease detection is critical for sustainable agriculture and reducing crop losses. This paper proposes a novel Multi-Stream Attention-Guided Hybrid CNN (MAH-CNN) for accurate classification of cotton leaf diseases. The model leverages pre-trained ResNet152v2 and DenseNet-121 backbones for hierarchical feature extraction, complemented by a shallow CNN for localized texture analysis. A spatial attention mechanism enhances focus on disease-relevant regions, mitigating background noise. Features from the global and local streams are fused and passed through a lightweight classification head. The model achieves superior performance in terms of accuracy 97.32%, F1 score 98%, and specificity 100% on benchmark datasets which are available in open access, outperforming existing state-of-the-art methods. The integration of Grad-CAM provides interpretability, fostering trust in automated disease detection systems. 2025 IEEE. -
Meaning in life buffers mental health risks in South Indian transgender (Hijra) women
Purpose This study aims to investigate the mediating role of meaning in life (MIL) in the relationships among depression, anxiety, stress (DAS) and quality of life (QOL) among transgender (Hijra) women in South India. The concept of QOL extends beyond the absence of negative mental states such as DAS; it includes overall mental health, well-being and personal evaluations of life circumstances. Design/methodology/approach This study is based on a sample survey of 302 transgender women selected via convenience sampling from five states in South India. The MIL scale, Depression, Anxiety and Stress Scale (DASS-21) and QOL tools were culturally validated and tested, with reliability confirmed through a pilot study (n = 15). Correlation, regression and mediation analyses were conducted to explore the relationships. Findings A strong positive correlation between MIL and QOL was found. The DAS score has a significant negative correlation with QOL. The Presence of Meaning (PoM) subscale emerged as a crucial predictor for overall QOL, whereas the Search for Meaning (SoM) subscale showed predictive value for psychological health. MIL negatively mediates the relationship between DAS, and QOL, suggesting that it buffers mental health risks. Originality/value These findings reinforce the notion that meaning-making is an active life-affirming process, particularly for transgender women navigating adversity. The POM enriches the QOL and acts as a buffer against existential despair. This highlights the need for interventions that foster meaning-making as a pathway to resilience, emphasizing agency, authenticity and purpose pursuit in the face of existential anxiety. 2025 Emerald Publishing Limited -
Meaning over metrics
By focusing on rankings, our current education model equates visibility with value and reputation with reality. -
Meaning-making, identity, and community: Cultural and religious influences on the mental health of transgender (Hijra) women in South India
Background: Hijra women in South India navigate mental health within contexts marked by structural exclusion, religious ambivalence, and complex kinship networks. While minority stress theory explains the psychological impact of stigma and discrimination, less attention has been paid to how culturally embedded meaning-making processes shape resilience and well-being in this population. Methods: This qualitative study draws on semi-structured, in-depth interviews with 13 self-identified Hijra women from Karnataka, Kerala, and Tamil Nadu. Interviews were conducted in regional languages and analyzed using Braun and Clarkes thematic analysis. The study integrates minority stress theory with a meaning-making framework to examine how cultural and religious contexts influence mental health, identity negotiation, and community relationships. Results: Three interconnected themes emerged: (1) Minority Stress and Systemic Oppression, characterized by family rejection, religious exclusion, public stigma, and economic marginalization; (2) Living in Ambivalence, reflecting both solidarity and constraint within Hijra kinship networks; and (3) Meaning-Making through Identity, Work, Community, and Spirituality. Educational attainment did not consistently translate into improved livelihoods or reduced distress. Participants constructed dignity and purpose through gender affirmation, creative and ethical livelihoods, contribution to others, and selective engagement with spiritual or ethical frameworks. Conclusions: Mental health among Hijra women is shaped by the interaction of structural exclusion and culturally embedded resources for meaning-making. Findings extend minority stress research by demonstrating how resilience emerges relationally within ambivalent social and religious environments. Culturally grounded mental health interventions must therefore address structural injustice while supporting community-based and meaning-oriented coping processes. 2026 Taylor & Francis Group, LLC. -
Meaningfulness and Experience of Perimenopausal Women During the Pandemic in India: A Mixed-Method Study
The perimenopausal transition marks a significant phase in womens lives, and the unforeseen challenges posed by the COVID-19 pandemic added complexity to this already intricate period. We investigated the mediating role of satisfaction with life between meaning in life and quality of life and adaptive strategies employed by perimenopausal women during the pandemic in India. Findings showed satisfaction with life as a strong mediator between meaning in life and quality of life. Thematic analysis identified a global theme of meaning in uncertainty and organizing themes: finding solace in self and actionable insights. The study emphasizes the need for understanding the meaning-making processes and adaptive strategies employed by women navigating perimenopause in the face of unprecedented global challenges. 2024 Society for Menstrual Cycle Research. -
Measurement Model of CO-PO Attainment in Higher Education: A Simplified Approach
The educational system in most countries are moving toward Outcome-Based Education (OBE) which is a student-centric teaching and learning methodology. The basic idea behind the adoption of OBE model is that the graduates should possess a sound knowledge in their respective disciplines and also have global mobility and acceptance. The Outcome-Based Education (OBE) should be based on the vision and mission of the institution. The institutions should clearly spell out the learning objectives of the program and course. The Course Outcome (CO), Program Outcome (PO), Program Specific Outcome (PSO) and Program Educational Objectives (PEO) determine clearly what the students are expected to accomplish, post their course or program respectively. This study aims to provide the simplified approach on assessment, evaluation and calculating the attainment levels of students through COs and POs in a management program. To assess the CO attainment for management courses, the authors have identified the subject Entrepreneurship Development offered in the first semester from the 2018-2020 batch of 60 students from the MBA program of an autonomous institute. The Course Outcome (CO) and Program Outcome (PO) are mapped with the Continuous Internal Assessments (CIA) and Semester Exam End (SEE) and thus the attainment levels of each CO are measured. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Measurement of Corporate Social Responsibility of Financial Companies in the Indian Context
Purpose: This study aimed to measure the corporate social responsibility (CSR) activities of financial companies (banks and NBFCs) in India. This paper used content analysis to quantify the CSR performance of financial companies. Financial institutions loan money to businesses utilizing assets owned by saving bank account holders, thereby heightening their societal responsibility. Methodology: Data from 63 financial companies listed on the National Stock Exchange from 20142015 to 20202021 were considered. Cronbachs alpha, descriptive statistics, and z-score test were used in this research to check the reliability and normality of the CSR scores. Findings: The results showed that private sector banks proactively participated in social activities compared to public sector banks (PSB) and non-banking financial institutions (NBFCs). Scores of CSR improved during the study period. Practical Implications: The study will allow future research because every issue and factor considered in the scale needs attention to benefit humankinds future generations. Calculated CSR scores could be used to measure the impact on the financial performance of companies. Originality: Unlike prior research on corporate social responsibility, the current work built a model to measure the CSR of financial companies. 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Measurement of financial inclusion status of India
Financial inclusion provides access to formal financial system for all members of the society. Financial inclusion leads to inclusive financial system which has several merits. Financial inclusion facilitates rational allocation of productive resources and thus can potentially reduce the cost of capital. Efforts towards financial inclusion have been undertaken in India for several years. These financial inclusion initiatives have yielded fruitful results for the people to access and use formal financial system. Further, these initiatives led to better penetration of banking system, credit penetration and savings penetration. However, financial exclusion is common phenomena in India among poor and weaker sections. This article has made an attempt to measure state of financial inclusion in India using access indicators, usage indicators and quality indicators of financial inclusion. IAEME Publication. -
Measurement of financial inclusion status of India /
International Journal of Mechanical Engineering And Technology, Vol.9, Issue 7, pp.354-364, ISSN Print: 0976-6340. ISSN Online: 0976-6359. -
Measures of superstitious beliefs: A meta-analytic review of research
Superstition is a term which is widely used across the globe but, is understood differently by people from different cultures. Superstitious beliefs are challenged by emerging scientific knowledge, and they continue to persist even among advanced societies. In recent years, superstitions are viewed as a belief in luck. The instruments that are available to assess this phenomenon are few and have insufficient psychometric properties. There is a need for developing new standardised measures which explore the complex, conceptual nature of superstitions. A meta-analysis of existing literature was done to explore the existing measures of superstitious beliefs and to examine the relationship between reliability of scales and the various attributes of scales. A literature search was conducted in relevant databases. Suitable transformation procedures for coefficient alpha were used. Meta-regression analysis was done to explore the heterogeneity of data. 41 scales measuring superstitions were analysed. Results indicate that reliability coefficients were from heterogeneous samples. Regression analysis revealed that few characteristics of scales predicted reliability. Journal of the Indian Academy of Applied Psychology. -
Measuring and monitoring Indias progress toward environmental SDGs: variables influencing the advancement
Purpose: The purpose of this study is the progressive assessment of Indian companies performance on environment-related sustainable development goals (SDGs). The study also aims to analyze the impact of a wide range of governance and firm-level determinants such as board size, gender diversity, board independence, top management commitment, presence of a sustainability committee and experience in nonfinancial reporting on environment-related SDGs performance. Design/methodology/approach: Using a mixed-method approach, this paper developed a framework for assessing business progress on environment-related SDGs by mapping targets of SDGs with indicators of global reporting initiative (GRI) standards. The framework is applied to evaluate the sustainability report of 46 Indian companies for six years for environment-related SDG performance. Multivariate linear regression analysis examines the impact of various governance and firm characteristics on this performance. Findings: The assessment framework identified improved reporting in all categories but significantly increased socio-environmental and socio-economic-environmental categories. Companies in the utility sector performed well, while those in the healthcare industry did not meet expectations. The pooled OLS panel regression results revealed a positive and significant impact of top management commitments, experience in nonfinancial reporting, board gender diversity and sustainability committee on the environment-related SDG disclosure scores. Research limitations/implications: Geographical limitations may limit the generalization in developed countries. The developed SDG assessment framework may help firms choose a business strategy for greater contribution and a governance structure to match this strategy. Investors can receive insight from measured performance, and regulators can lay policies, targets and incentives for different industries based on the interpretation of performance. Originality/value: This study developed an assessment framework to capture the positive business contributions to environment-related SDG and its trends. It sheds light on the critical interaction between corporate governance, management committees, experience in financial reporting and environmental SDG performance to increase the understanding of the determinants of SDG performance. 2025, Emerald Publishing Limited. -
Measuring autonomy in hybrid work: scale development
Background: Autonomy is a core element in many established management theories, consistently linked to positive employee outcomes. However, the COVID-19 pandemic and rapid technological advancements have transformed workplace dynamics, particularly in the information technology (IT) sector in India, where hybrid work models have gained prominence. Despite this shift, no standardized measure exists to assess the autonomy experienced by employees in hybrid work environments, hindering deeper analysis and understanding. Objective: This study aims to contextualize, develop, and validate the Autonomy in Hybrid Work Scale (AHWS) for the Indian context, providing a tool for researchers and practitioners to systematically examine the impact of autonomy in hybrid work. Methods: A descriptive two-phase study was conducted following DeVelliss scale development framework. Phase 1 focused on conceptualizing and developing the construct through a comprehensive literature review, item generation, and assessment of content and face validity by experts, followed by a pilot test. Phase 2 encompassed the scale validation process, which included Exploratory Factor Analysis (EFA) to identify the underlying factor structure and Confirmatory Factor Analysis (CFA) to validate the model and assess its fit. Results: The data collected from 313 IT employees working in Bengaluru, India, was analyzed to confirm data normality (below 2.58). The items showed a strong and positive correlation (r =.734) with the Work Design Questionnaire which indicated convergent validity. Discriminant validity was confirmed through Fornell-Larcker and Heterotrait-Monotrait (HTMT) criteria, with HTMT values below 0.90. The final analysis yielded an 18-item scale with a Cronbachs alpha of 0.825, comprising four distinct dimensions: (a) work location autonomy, (b) work time autonomy, (c) work scheduling autonomy, and (d) work decision autonomy. Implications: The AHWS offers a valuable tool for both managers and academics to assess how different forms of autonomy influence employee well-being and productivity in hybrid work settings. It also addresses a gap in the literature, providing a foundation for further empirical research on autonomy in hybrid work models. The Author(s) 2025. -
Measuring Consumer Perception for P2P Platform: NLP Approach
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik's wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower's., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear. 2022 IEEE. -
Measuring Critical Thinking Skills with the R-BiLSTM-C Model using a Logical Approach
Critical thinking is essential for making informed judgements; it necessitates careful evaluation of pertinent evidence and the application of reasoning. While some individuals possess a more constrained perspective on critical thinking, this elucidation encompasses the predominant views held by the majority. Note-taking, formulating enquiries, and designing experiments exemplify practical actions that may be applied to other creative pursuits, rendering it a valuable skill across diverse domains. This work employed a systematic approach for data preparation, model training, and feature extraction. The primary phase in training unified R-BiLSTM-C models was identified as feature extraction. Standardisation and normalisation, two crucial preprocessing techniques, were employed to ensure uniform and dependable data handling. Furthermore, as the quantity of dependent observations escalated, the study evaluated the efficacy of Reduced Kernel PCA. The proposed solution achieved a 96.82% accuracy rate, surpassing advanced techniques such as CNN and BiLSTM. The findings indicate that the systematic approach enhances the model's performance. A systematic approach is essential for enhancing precision, and analytical reasoning skills are crucial for developing effective machine learning models. The study reinforces the significance of critical thinking in effective decision-making and problem resolution. 2025 IEEE. -
Measuring Customer Perception on Promotion of Tourism Destinations Using AR and VR Applications: Model Testing and Validation
The study aims to propose and develop a model to measure the customer perception toward promotional videos created using Augmented Reality (AR) and Virtual Reality (VR) technologies to promote tourism places. Using judgment sampling, 400 tourists were chosen, all of whom had visited various tourist spots in Visakhapatnam and had seen at least two promotional movies highlighting various tourist attractions using AR/VR technology. A properly written questionnaire was produced ahead of time to gather visitors perception for the qualities of augmented and virtual reality advertising attempts. The study revealed that passengers expect full information and appropriate motivation from digital marketing efforts that promote specific tourist locations using Augmented Reality and Virtual Reality. Furthermore, visitors anticipate high-quality visual and audio features in digital advertising materials for tourist destinations, with the goal of improving the entire customer experience and inspiring future visits. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Measuring employee attrition intention in an auto-component manufacturing organisation
Orientation: The auto-component manufacturing sector, a critical contributor to industrial growth, faces persistent challenges related to employee attrition, affecting operational efficiency and workforce stability. This study examines the influence of job satisfaction, work-life balance, and job stress on attrition intention among employees in Indian auto-component manufacturing organisations. Research purpose: To identify the key factors contributing to employee turnover and evaluate their relative impact on attrition intention. Motivation for the study: Amid rising concerns over attrition in the manufacturing industry, this research aims to explore how work-life balance and job stress influence employees intentions to leave their organisations. Research approach/design and method: Data were collected from 192 employees across 10 auto-component manufacturing companies in Pune, Maharashtra, India, using a structured questionnaire. The responses were analysed through structural equation modelling (SEM) using SPSS and AMOS. Main findings: The study reveals that work-life balance and job stress significantly impact attrition intention. Employees with poor work-life balance and high job stress are more likely to consider leaving. However, job satisfaction does not have a direct effect on attrition intention. Practical/managerial implications: Organisations should prioritise improving work-life balance and managing job stress by implementing flexible work policies, wellness programmes, and realistic workload distribution. Contribution/value-add: This study underscores the importance of addressing work-life balance and job stress in retention strategies, offering actionable insights for HR managers to mitigate attrition in the auto-component manufacturing sector. 2025. The Authors.