Volume 14 | Issue 7 |

 Abstract

The water samples from Jangamhatti Dam and Tilari Dam in Chandgad Tehsil, Kolhapur (Maharashtra), were collected during the pre-monsoon (April 2025) and post-monsoon (November 2025) seasons. Physicochemical parameters such as pH, temperature, salinity, chlorides, total hardness, turbidity, electrical conductivity, calcium, magnesium, dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) were analyzed and compared with the standard values recommended by the World Health Organization (WHO). The results indicated noticeable variations in these parameters between the two dams.

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Jangamhatti Dam, Tilari Dam, Physicochemical analysis

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There is an influx of digitally generated content and user opinions over several decades and has brought major changes to the information environment. Online platforms such as movie review sites, e-commerce portals, and social networking services produce a large amount of user-generated content every year. This includes customer feedback, reviews of products and services, and general social discussions. Together, this information forms an important source of sentiment data that supports business decisions, political communication, and social research. This paper proposes a model SentiBERT with sentiment intelligence framework that uses three transformer architectures--BERT, RoBERTa, and DistilBERT--against classical TF-IDF-based baselines on the IMDB Large Movie Review Dataset, while evaluating across Amazon product and Yelp restaurant review datasets considering cross-domain transfer protocol. SentiBERT generates three remarkable outputs which distinctively elevates this model from rest. First, we design a multi-method Explainable AI (XAI) pipeline that unifies Local Interpretable Model-agnostic Explanations (LIME), BertViz attention-weight visualization, and GPT-2-grounded for natural language explanations-- In this approach, the generated explanation is based on measurable evidence obtained from LIME rather than relying only on unrestricted language model outputs, which helps reduce the chances of imagined results. Second, we implement a hybrid aspect-based sentiment analysis (ABSA) module that integrates spaCy noun-phrase extraction with BERT context-window scoring across six film-specific aspect categories. Third, vocabulary overlap is examined to understand transfer difficulty. For this purpose, Jaccard similarity is used as a reference measure. Additionally, words associated with model failure are identified across target domains using LIME. The experimental results are validated against reproducible JSON metric files which demonstrates RoBERTa achieves the highest test-set accuracy of 91.70% and AUC-ROC of 0.972, representing a 32% reduction in error rate relative to the best classical baseline (Linear SVM: 87.62%). Interestingly, the accuracy drop for zero-shot cross-domain transfer is only 0.60% and 0.40% on Amazon and Yelp, respectively, and contradicts the notion of domain fragility for transformer-based sentiment classifiers. Under text-casing perturbation, BERT achieves 90.0% accuracy, whereas it drops to 70.0% when the test set is heavily corrupted with 20% of typing errors (typo injection). The biggest challenge BERT faces is the identification of subword tokenisation boundaries. The results shed light on the advantages and disadvantages of applying sentiment analysis based on transformers to real-world applications.

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Sentimental Analysis, DistilBERT, BERT, RoBERTa, LIME, GPT-2, Aspect-Based Sentiment Analysis (ABSA), Cross-Domain Transfer Learning, Explainable AI, Natural Language Processing

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This paper argues that shadow economies are not peripheral anomalies or symptoms of underdevelopment but adaptive institutions that emerge when formal economic systems externalise health risk onto workers without adequate protection. Drawing on nineteenth-century literary evidence from Dickens, Hugo, and Dostoevsky -- read through Elaine Freedgood's method of strong metonymic reading -- alongside Hernando de Soto's theory of informal capital, Rosenblat and Stark's empirical study of Uber drivers, the Fairwork 2021 global report on platform labour during COVID-19, and Mia Moore's legal critique of algorithmic governance, the paper identifies a four-stage recurring institutional mechanism: health risk privatisation, income volatility amplification, vulnerability accumulation, and adaptive informality emergence. It demonstrates that this mechanism operates similarly across industrial capitalism and contemporary platform labour, despite significant differences in technology and scale. The paper concludes by proposing three institutional design interventions -- (i) health-indexed algorithmic constraints, (ii) portable risk contributions, and (iii) mandatory health transparency audits -- that would interrupt the cycle by making health economically visible within platform governance. The contribution is both theoretical, in reframing shadow economies as predictable institutional responses to risk externalisation, and practical, in identifying actionable policy directions for platform labour reform.

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Shadow Economy, Risk Externalisation, Algorithmic Governance, Platform Labour, Health Invisibility, Institutional Economics, Behavioural Economics, Gig Economy, Informal Institutions.

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Mobile banking has emerged as one of the most significant innovations in the modern banking sector, transforming the way customers access and utilize banking services. The increasing penetration of smartphones, internet connectivity, and digital payment systems has encouraged. customers to perform financial transactions through mobile applications without visiting physical bank branches. Mobile banking offers various facilities such as fund transfers, balance inquiries, bill payments, account monitoring, and other banking services, thereby enhancing convenience and accessibility for users. The present study examines the impact of mobile banking on customer satisfaction in public and private sector banks. The primary objective of the study is to analyse customer perceptions regarding mobile banking services and to compare satisfaction levels among users of public and private sector banking applications. The study also investigates the influence of factors such as ease of use, security, transaction speed, service quality, accessibility, reliability, and customer trust on overall satisfaction. The research is based on both primary and secondary data. Primary data were collected through a structured questionnaire administered to customers who regularly use mobile banking services, while secondary data were obtained from books, journals, research articles, banking reports, and online sources. The collected data were analysed using descriptive statistical techniques. The findings reveal that mobile banking has positively influenced customer satisfaction by making banking services faster, more convenient, and easily accessible. Customers of private sector banks generally reported higher satisfaction levels due to better application performance, faster transaction processing, and user-friendly interfaces. In contrast, customers of public sector banks expressed greater trust and confidence in the security and reliability of their banking institutions. Despite the overall positive perception, certain challenges such as technical issues, security concerns, and occasional service interruptions continue to affect customer experiences. The study concludes that mobile banking significantly contributes to customer satisfaction in both public and private sector banks. Banks should focus on improving digital infrastructure, strengthening cybersecurity measures, enhancing customer support, and increasing awareness regarding digital banking services. Continuous innovation and service improvement will be essential for sustaining customer satisfaction and ensuring the long term success of mobile banking platforms.

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Keywords: Mobile Banking, Customer Satisfaction, Public Sector Banks, Private Sector Banks, Digital Banking, Service Quality, Customer Trust, Banking Technology.

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ABSTRACT Background Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases and remains one of the leading causes of cancer-related mortality worldwide. Recent advances in immune checkpoint inhibitor therapy targeting PD-1/PD-L1 pathways have improved survival outcomes; however, only a subset of patients demonstrates durable clinical responses. Emerging evidence indicates that lung microbiome dysbiosis plays an important role in tumor progression, immune regulation, inflammatory pathways, and response to immunotherapy. Aim To assess the role of lung microbiome dysbiosis in NSCLC progression and its association with immunotherapy response among patients receiving immune checkpoint inhibitor therapy. Methods A prospective observational study was conducted among 100 diagnosed NSCLC patients receiving immunotherapy. Bronchoalveolar lavage (BAL) samples were collected for microbial profiling using 16S rRNA sequencing. Clinical characteristics, tumor stage, PD-L1 expression, inflammatory biomarkers, and immunotherapy response were assessed. Treatment response was evaluated according to RECIST 1.1 criteria. Data were analyzed using SPSS version 26.0. Results Among 100 NSCLC patients, lung microbiome dysbiosis was identified in 62% of cases. Advanced tumor stage (III-IV) was significantly associated with reduced microbial diversity (p<0.01). Patients with balanced lung microbiota showed a higher immunotherapy response rate (63.2%) compared with dysbiosis patients (32.3%). Increased abundance of Proteobacteria and reduced Firmicutes were associated with poor progression-free survival and resistance to immune checkpoint inhibitors. Conclusion Lung microbiome dysbiosis significantly influences NSCLC progression and immunotherapy response. Microbial profiling may serve as a potential biomarker for predicting treatment outcomes and developing microbiome-targeted therapeutic approaches.

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Keywords: Non-small cell lung cancer, Lung microbiome, Dysbiosis, Immunotherapy, Immune checkpoint inhibitors, PD-1, PD-L1

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Colon-targeted drug delivery has effective approach to address the clinical need for localized treatment of colonic diseases while reducing systemic side effects and improving therapeutic outcomes. Advances in understanding colonic anatomy, physiology, gastrointestinal transit, and the role of microbiota have enabled the development of site-specific delivery strategies such as pH-dependent, time-controlled, enzyme-responsive, and microbiota-activated systems. The use of advanced carrier systems, including polymers, micro- and nanoparticles, lipid-based carriers, and hybrid platforms, has further enhanced drug stability, controlled release, and targeting precision. These technologies have shown significant therapeutic benefits in the management of inflammatory bowel disease, Crohn's disease, colorectal cancer, and microbiome-related and systemic applications. Despite promising preclinical and clinical outcomes, challenges such as formulation stability, physiological variability, regulatory complexity, and scalable manufacturing remain. Emerging trends focusing on smart multi-responsive systems, integration with microbiome-based therapies, and personalized medicine are expected to overcome existing limitations and drive the future clinical translation of colon-specific drug delivery systems.

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Colon-Targeted Drug Delivery, pH-Dependent, Microbiota-Activated, Crohn's Disease, Colorectal Cancer, Inflammatory Bowel Disease (IBD).

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This research paper evaluates and compares the portfolio performance of selected large-cap and mid-cap mutual fund schemes in India. The study uses secondary data for the period 2021-2025 and compares five large-cap schemes with five mid-cap schemes. Performance is examined through return measures, risk measures and risk-adjusted measures such as portfolio return, compound annual growth rate, average return, standard deviation, coefficient of variation, beta, Sharpe ratio, Treynor ratio, Jensen alpha, Sortino ratio, correlation, regression, CAPM and the Fama-French three factor model. The results indicate that mid-cap schemes generally generated higher returns than large-cap schemes, but they were also exposed to higher volatility. Invesco India Mid Cap Fund, Quant Mid Cap Fund and Motilal Oswal Midcap Fund showed strong performance on several risk-adjusted indicators. Among large-cap schemes, Nippon India Large Cap Fund and HDFC Large Cap Fund performed better than the other selected large-cap funds. The study concludes that investors should not select funds only on the basis of return, but should also consider risk, market sensitivity and downside protection before making investment decisions.

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Mutual Funds, Large-Cap Funds, Mid-Cap Funds, Portfolio Performance, Risk-Return Analysis, Sharpe Ratio, Jensen Alpha, CAPM, Fama-French Model.

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Keywords: Biomedical waste management, digital learning, compliance audit, housekeeping personnel, infection prevention, healthcare waste, hospital training, quasi-experimental study.

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ABSTRACT Background Lung cancer development is a complex process influenced by genetic, environmental, inflammatory, and immunological factors. Chronic allergic airway inflammation, characterized by increased Th2 immune responses, eosinophilic activity, and altered cytokine expression, may influence the lung microenvironment and contribute to cancer initiation. Recent evidence suggests that lung microbiome dysbiosis can modify immune surveillance, inflammatory pathways, and tumor-promoting mechanisms. Understanding the relationship between allergic airway inflammation, lung microbiota alterations, and immune crosstalk may help identify early biomarkers for lung cancer prevention and targeted therapy. Objective To assess the impact of allergic airway inflammation on lung cancer initiation and evaluate the association between lung microbiome alterations and immune response among selected participants. Methods A quantitative observational comparative study was conducted among 100 participants divided into two groups: 50 participants with allergic airway inflammation (asthma/allergic airway disease) and 50 healthy controls. Participants were selected using purposive sampling. Clinical history, inflammatory biomarkers, microbiome profiles, and immune response parameters were assessed. Airway inflammation was evaluated using serum IgE levels, eosinophil count, and inflammatory cytokines (IL-4, IL-5, IL-13). Lung microbiome composition was analyzed using 16S rRNA sequencing of respiratory samples. Immune activity was assessed through inflammatory markers and immune regulatory pathways. Data were analyzed using SPSS version 26.0. Descriptive statistics, chi-square test, independent t-test, and correlation analysis were applied. Results Among participants with allergic airway inflammation, significant changes in airway microbiome composition were observed compared with controls. Reduced microbial diversity and increased abundance of inflammatory-associated bacterial species were identified. Higher levels of Th2-associated cytokines were found among allergic participants. Participants with chronic allergic inflammation showed: o Increased eosinophilic airway activity o Higher IgE concentration o Altered microbiome diversity o Enhanced inflammatory signaling pathways Microbiome imbalance showed a significant positive association with inflammatory markers (p<0.05), suggesting that allergic inflammation may contribute to a tumor-supportive lung microenvironment. Conclusion The findings indicate that allergic airway inflammation may influence lung cancer initiation through alteration of microbiome-immune interactions. Persistent inflammatory responses and microbial dysbiosis may contribute to impaired immune surveillance and early carcinogenic changes. Targeting microbiome regulation and inflammatory pathways may provide future opportunities for lung cancer prevention and immunotherapy strategies.

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Keywords: Allergic airway inflammation, lung cancer initiation, microbiome dysbiosis, immune crosstalk, Th2 immunity, cytokines

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In Karnataka, organic farming is ecologically and economically feasible. Shivamogga district is a significant organic agricultural zone due to its favourable agro-climatic conditions, diverse planting pattern, and growing farmer awareness. Demographics, education, landholding, income, employment, marketing, certification, government assistance, and restraints of organic farmers in Shivamogga district are examined in this research. Organic farming has increased farmers' income, soil fertility, chemical input resistance, and sustainable lives, according to the research. However, certification fees, marketing inefficiencies, price volatility, workforce shortages, and insufficient institutional backing continue to hinder organic agricultural growth. The report suggests boosting market links, certification, financial aid, and extension services to promote sustainable organic agriculture. Studies in Shivamogga and Karnataka found that bigger, better-educated farmers embrace organic farming more easily, despite institutional obstacles.

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Keywords: Organic farming, Socio-economic conditions, Sustainable agriculture, Organic certification, Shivamogga District, Karnataka.

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