Volume 13 | Issue 10 |

 Abstract

This paper on literature review critically assesses the auditor's role in fraud detection of financial statements, focusing on the ongoing mismatch between professional ability and public expectation. The paper draws on auditing guidelines, behavioral studies, and regulatory reactions post-scandal to highlight what fraud detection auditors can actually do and what is expected by stakeholders. The paper reviews the fraud triangle model to explore how pressure, opportunity, rationalization, and capability facilitate financial fraud. Specific focus is placed on separating fraudulent financial reporting from asset misappropriation, the difficulties presented by management override of controls, and audit sampling and time-based limitations. The paper integrates research on audit procedures, professional skepticism, corporate governance devices, and technological advances such as data analytics and artificial intelligence. Through case studies of influential accounting scandals, the review analyzes regulatory reactions, litigation effects, and cultural forces affecting fraud prevalence in varied settings. Analysis indicates that auditors identify fraud but that detection frequently happens accidentally instead of systematically, and core contradictions between audit economics and thorough fraud investigation are still unsolved. The review concludes that enhancing fraud detection calls for realistic expectation management, skepticism training improvement, strategic technology implementation, and perhaps reconceptualization of relationships between financial statement auditing and fraud investigation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Auditor responsibility, financial fraud detection, professional skepticism, financial reporting, management fraud, corporate governance, forensic accounting.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Background: Accidents among under-five children are a major public-health concern and a leading cause of morbidity and mortality worldwide. Mothers play a crucial role in accident prevention through adequate knowledge and safe child-rearing practices. Objectives: (1) To assess the pre-test knowledge of mothers regarding prevention of accidents among under-five children. (2) To evaluate the effectiveness of a Structured Teaching Programme (STP) on knowledge regarding accident prevention. (3) To determine the association between post-test knowledge scores and selected demographic variables. Methods: A pre-experimental one-group pre-test-post-test design was adopted. The study was conducted among 60 mothers of under-five children selected through purposive sampling from a community setting in Indore, Madhya Pradesh. A structured knowledge questionnaire was used for data collection. After pre-testing, an STP covering types of accidents, causes, first aid, and preventive strategies was administered. Post-test data were collected after seven days. Data were analyzed using descriptive and inferential statistics such as mean, standard deviation, paired t-test, and chi-square test. Results: The mean pre-test knowledge score was 12.35, which increased to 24.80 in the post-test, indicating a marked improvement after the STP. The calculated t-value (12.64) was greater than the table value at p < 0.05, demonstrating statistically significant improvement. A significant association was observed between post-test knowledge and educational qualification of mothers. Conclusion: The Structured Teaching Programme was highly effective in improving maternal knowledge regarding accident prevention among under-five children. Health professionals should integrate such educational interventions into community health programs to reduce accidental injuries in early childhood.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Structured teaching programme, accident prevention, under-five children, mothers, community setting, Indore.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the era of digital transformation, Artificial Intelligence (AI) has emerged as a key driver of organizational competitiveness, agility, and innovation. While there has been extensive research on the incorporation of AI in advanced economies, its strategic implications in emerging economies have been relatively less explored. This study examines how AI-enabled systems can improve strategic decision-making, operational agility, and innovation outcomes, particularly for organizations in India and similar emerging markets. Using a mixed-method research design, the study combines quantitative data from managerial respondents and qualitative insights from four digitally transformed organizations. The qualitative research findings also indicate that AI-enabled organizations are able to respond faster to market changes, are more efficient in knowledge management, and have stronger coordination across departments. The research findings suggest that organizations that strategically align AI initiatives with their business goals are able to adapt quickly to market volatility and achieve sustainable competitive advantage. Overall, the study concludes that in emerging economies that suffer from limited resources and infrastructure constraints, AI serves as an important strategic tool for process transformation and innovation-driven growth. The article calls on policymakers and business leaders to enhance digital competitiveness by implementing AI-enabled learning systems, leadership development, and building robust information infrastructure.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Digital Transformation, Organizational Agility, Innovation, Emerging Economies, Strategic Management

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Perovskite solar cell, SCAPS-1D, FAPbI?, Graphene, photovoltaic efficiency, renewable energy.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The unprecedented rise in electrical energy consumption across residential, commercial, and industrial domains has made efficient monitoring, analytics, and control essential for cost savings and sustainability. Traditional metering systems typically provide only cumulative or coarse-grained readings, leaving users and managers without appliance-level insights or real-time alerts that could reduce wastage or prevent faults. This paper presents a comprehensive Internet of Things (IoT)- based power consumption and monitoring system that integrates voltage and current sensing, edge processing via a microcontroller, secure wireless transmission, cloud storage and analytics, and user-facing dashboards with control capabilities. The system measures instantaneous power and cumulatively computes energy usage per appliance using calibrated sensors (e.g., ZMPT101B and ACS712). An ESP32/Arduino reads the sensors, filters and timestamps data, and sends it to the cloud over MQTT/HTTPS. The cloud ingests data into a time-series datastore, runs analytics for peak detection and anomaly detection, and exposes dashboards and REST APIs for visualization and control. A relay driver stage enables remote actuation of connected loads for demand response or scheduling. Experimental deployment across multiple household appliances demonstrates measurement accuracy within acceptable error margins, consistent real-time reporting, and effective remote control. The design emphasizes modularity and scalability, allowing additional nodes to be added and future integration with machine learning for predictive maintenance and demand forecasting. This work shows that IoTenabled energy monitoring is a practical, cost-effective route toward smarter energy usage and tangible operational savings while providing a foundation for integration with smart grid and renewable energy systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

IoT, Energy Monitoring, Power Consumption, Cloud Analytics, Remote Control, Smart Grid, Predictive Maintenance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Disability theory in the twenty-first century has shifted from medical and deficit-based models toward perspectives that emphasize diversity, inclusion, and dignity. This paper examines how R. J. Palacio's Wonder (2012) and Mark Haddon's The Curious Incident of the Dog in the Night-Time (2003) challenge traditional ideas of ability and disability. Both novels portray characters often seen as "different" yet redefine resilience, intelligence, and belonging, showing disability as part of human variation rather than limitation. By situating these texts within debates on education, culture, and global citizenship, the study argues that literature is a powerful space for rethinking disability as identity. Such reinterpretations foster empathy and support the creation of more just and inclusive communities.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Disability Studies, human diversity, inclusivity, literature, identity, education, social justice

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Every writer while writing gets inspiration from their own life and surroundings. Nissim Ezekiel, born in a Parsi religion in an Israeli family residing in Bombay, is much revered for his writing in English. His background of being a Jew, which is a minority group in India, is reflected in many of his poems. The alienation he faced in his life from all the spheres right from childhood became the central theme of most of his poems. One of his semi-autobiographical work poems, 'Background Casually' speaks in volume the alienation, marginalisation, experiences faced by a Jewish minority in India. The paper explores how his identity became a crucial factor in the alienness, alienation and otherness throughout his life in context of his poem 'Background Casually'.The paper will also explore how alone he was & felt amidst the busy bustling town of Bombay

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Minority, Alienation,Otherness, Identity

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Maxillary Transverse Deficiency (MTD) in adults presents with constricted maxillary arches, posterior crossbites, dental crowding, and functional and aesthetic concerns. Unlike pediatric patients, adult midpalatal sutures are often fused or highly interdigitated, limiting conventional rapid maxillary expansion (RME) and increasing the risk of dental tipping and alveolar bending. Contemporary approaches, including Surgically Assisted Rapid Maxillary Expansion (SARME) and Mini-implant Assisted RME (MARPE), enable predictable skeletal expansion while minimizing dental side effects. SARME involves osteotomies to overcome sutural resistance, whereas MARPE utilizes skeletal anchorage via palatal miniscrews to transmit expansion forces directly to the basal bone. Both methods improve occlusal function, facial aesthetics, and airway dimensions, though complications such as root resorption, periodontal changes, or surgical risks may occur. Careful patient selection, imaging assessment, appliance design, and retention protocols are essential for long-term stability. Advances in digital planning and minimally invasive techniques continue to enhance the safety and efficacy of adult RME.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rapid Maxillary Expansion, MARPE, SARME, Adult Orthodontics, Maxillary Transverse Deficiency

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In Ayurveda, the concepts of Agni and Ama are considered as the core of health and disease. Classical texts proclaim that all diseases arise from impaired Agni. Agni governs digestion, absorption and metabolism at systemic, elemental & tissue levels with the aid of Jatharagni. Bhutagni and Dhatvagni respectively. Impaired Agni produces Ama which is described as Guru, Picchila and Srotorodhakara forming the root of Samprapti in several autoimmune, gastrointestinal, inflammatory, neuroendocrinal and psychiatric disorders. Biomedicine describes the gut microbiome as a dynamic community comprising trillions of microorganisms that influences digestion, metabolism, immunity and systemic homeostasis. Dysbiosis is now implicated in metabolic, autoimmune, neuropsychiatric and inflammatory diseases. A striking parallel exists between Agni, Ama and microbial digestive-metabolic capacity. Ama resonates with microbial toxins and inflammatory metabolites. Ayurveda prescribes interventions such as Dipana-Pacana, Rasayana, Panchakarma procedures, Takra, fermented preparations like Arishtas, Asavas, Lehyas, etc which are probiotic equivalents and acts in diseases having its Samprapti at Agni-Ama-Dhatu levels. These are beneficial in modulating gut microbial composition and improve immunity. Emerging evidences from experimental and clinical trials on formulations like Triphala, Guduci, Amalaki and fermented products highlight their ability in restoring gut microbial diversity, enhancing short-chain fatty acid production and reducing systemic inflammation. This review attempts to connect Ayurvedic wisdom with microbiome science highlighting how ancient concepts of Agni and Ama may be interpreted through gut microbial ecology. A deeper exploration of these parallels validates Ayurveda's insights and provides opportunities for integrative therapeutics targeting digestive health and thereby improving immunity and preventing chronic disease pathologies.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Agni, Ama, Gut microbiome, Dysbiosis

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Autism Spectrum Disorder (ASD) diagnosis has experienced a remarkable evolution with the integration of Artificial Intelligence (AI) and data-driven healthcare analytics. This paper presents an AI-based diagnostic framework that leverages behavioral data, demographic attributes, and machine learning algorithms to predict the likelihood of ASD in children, adolescents, and adults. The research highlights the methodology, experimental design, and comparative analysis of multiple models including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). The proposed system provides accurate and early detection of ASD symptoms using non-clinical datasets from the UCI repository, ensuring reliable predictions for healthcare practitioners. The framework integrates intelligent data preprocessing and deep learning layers to enhance classification accuracy and minimize diagnostic errors. A cloud-based implementation and interactive dashboard improve accessibility, scalability, and usability for researchers, caregivers, and medical professionals across diverse regions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autism Spectrum Disorder (ASD), Machine Learning, Data Analysis, Predictive Modeling, Healthcare Analytics, Early Diagnosis, Neurodevelopmental Disorders.

  License

Creative Commons Attribution 4.0 and The Open Definition