Volume 13 | Issue 12 |

 Abstract

The large-scale generation of fly ash from coal-based thermal power plants presents serious environmental and land-use challenges. The present study investigates the development of fly ash reinforced polymer composites aimed at converting industrial waste into value-added engineering materials. Composite specimens were fabricated using a compaction-based technique followed by room-temperature curing, with fly ash content varied at 75 wt.%, 80 wt.%, and 85 wt.%. Mechanical properties were evaluated through hardness and compressive strength tests, while tribological performance was assessed under dry sliding conditions. Thermal conductivity measurements were conducted to examine insulation capability, and microstructural analysis was performed using scanning electron microscopy. The results demonstrate that hardness and wear resistance increase consistently with increasing fly ash content due to the ceramic nature of fly ash particles and improved load-sharing mechanisms. Compressive strength exhibited an optimum value at intermediate fly ash content, indicating a balance between reinforcement loading and matrix continuity. Thermal conductivity decreased significantly with higher fly ash content owing to hollow cenospheres, increased porosity, and interfacial thermal resistance. Microstructural observations confirmed uniform particle dispersion and effective interfacial bonding at optimum compositions. The study establishes a clear structure-property relationship and highlights the potential of fly ash reinforced polymer composites for structural, tribological, and thermal insulation applications

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 Keywords

Fly ash, Polymer composites, Mechanical properties, Tribological behavior, Thermal conductivity, Sustainable materials

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetic Retinopathy (DR), a major complication of diabetes, continues to be one of the primary causes of avoidable vision impairment worldwide. As the number of individuals living with diabetes rises and access to specialized eye care remains limited in many regions, the need for timely and accurate DR detection becomes increasingly urgent. This survey explores modern, intelligent solutions aimed at improving DR screening, with a focus on integrating RetinoBot--a conversational AI tool--and automated deep learning frameworks to streamline diagnosis and enhance patient experience. The paper reviews the application of state-of-the-art image analysis models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), both of which have shown high efficacy in interpreting retinal fundus images. It also examines how RetinoBot contributes to proactive patient involvement through features like health education, symptom reporting, and appointment scheduling, all delivered via natural language interaction. Furthermore, the role of Automated Machine Learning (AutoML) is analyzed for its ability to empower clinicians without coding expertise to develop, tune, and deploy DR detection models. By combining AI-powered chat interfaces with AutoML-driven diagnostics, this study highlights a scalable, patient-centric approach to DR management that promises to make eye care more efficient, inclusive, and personalized.

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 Keywords

Diabetic Eye Disease, RetinoBot System, AI-Powered Healthcare Chatbots, Self-Learning Deep Models, AutoML Techniques, Transformer-Based Vision Models, CNN-Based Image Processing, Retinal Image Interpretation, AI Solutions for Patient Engagement

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Indian capital markets have undergone unprecedented transformation over the past decade, transitioning from a broker-intermediated system with substantial entry barriers to a digitally accessible marketplace available via mobile applications. This shift has facilitated mass participation among first-time investors attracted by zero-commission trading, expedited account activation, and real-time market information dissemination. Yet simultaneously, derivative market participants continue to experience persistent losses despite enhanced access to data and trading infrastructure. This research examines the paradox wherein technological advancement correlates with accessibility expansion while outcomes for many retail participants remain suboptimal. Drawing upon behavioral finance theory, market microstructure analysis, regulatory evidence, and platform design research, this study demonstrates that technology functions as an amplifier of existing investor behavioral patterns rather than as an independent determinant of outcome improvement. The implications suggest that sustainable retail participation requires integration of technological infrastructure with behavioral safeguards, investor education, and regulatory oversight incorporating choice architecture and friction mechanisms.

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Retail investors, financial technology, behavioral biases, market accessibility, regulatory governance

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Creative Commons Attribution 4.0 and The Open Definition

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This paper undertakes a Dalit feminist analysis of Baby Kamble's seminal autobiographical narrative, The Prisons We Broke (Jina Amucha, 1986). Moving beyond a singular focus on caste or gender, the analysis positions Kamble's work as a foundational text that illuminates the intersecting oppressions structuring Dalit women's lives in pre- and post-Ambedkarite Maharashtra. Employing Elaine Showalter's model of feminist literary criticism--particularly her concepts of the "wild zone" and "gynocritics"--as a dialogic framework, the paper argues that Kamble articulates a distinct Dalit feminist consciousness. This consciousness emerges from the specific material and corporeal experiences of Dalit women, challenging both patriarchal norms within the community and the Savarna-dominated feminist discourse. Through close reading, the paper examines Kamble's documentation of triple oppression (caste, class, gender), her portrayal of the Dalit female body as a site of abjection and resistance, and her celebration of collective awakening catalyzed by Ambedkarite movement. The paper concludes that The Prisons We Broke performs a dual act of breaking prisons: it dismantles the silent, "wild zone" of Dalit women's experience by bringing it into literary discourse, while simultaneously constructing a gynocritical tradition for Dalit feminism.

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 Keywords

Dalit Feminism, Baby Kamble, The Prisons We Broke, Elaine Showalter, Gynocritics, Wild Zone, Autobiography, Caste, Gender, Ambedkar

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: This paper provides a reinterpretation of Anne Sexton's poems "Menstruation at Forty" and "Housewife" through the theoretical lens of Julia Kristeva's seminal essay "Women's Time" (1981), which distinguishes between cyclical, monumental, and generational modes of feminine temporality. Anne Sexton's poetic language transforms menstruation and domesticity, which are often portrayed as confinement into rhythmic, semiotic acts of resistance against the linear, teleological, masculine temporality of patriarchal culture. Sexton's verses use rhythm, repetition, and corporeal imagery to stage the female body and household as temporal and linguistic sites where the symbolic order of progress collapses into recurrence and endurance. This paper situates Sexton's confessional poetics within Kristeva's framework to show how her language enacts rather than merely reflects theory: her menstruating and domestic speakers embody cyclical recurrence and monumental stillness as forms of revolt. This study argues that by merging Kristeva's psychoanalytic temporality with Sexton's confessional form, feminine time survives within poetic rhythm itself, where bleeding, cleaning, and enduring become modes of linguistic and ontological creation.

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Key Words: Anne Sexton, Confessional Poetry, Feminine Temporality, Julia Kristeva, Semiotic Resistance

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 Abstract

Apicoectomy, also known as root-end resection, is a surgical endodontic procedure performed to remove the apex of a tooth's root and the surrounding periapical infected tissue when conventional root canal therapy has failed or is not feasible[1]. It serves as a conservative approach to preserve natural dentition by eliminating persistent periapical pathology and promoting peri radicular healing. The success of an apicoectomy depends on precise case selection, thorough diagnosis, advanced surgical techniques, and the use of biocompatible root-end filling materials. Recent advancements, including the use of magnification with dental operating microscopes, ultrasonic retrograde instrumentation, and bioactive materials such as mineral trioxide aggregate (MTA) and Bio dentine, have significantly improved treatment outcomes. This review discusses the indications, surgical procedures, materials, success rates, and future trends associated with apicoectomy, emphasizing its importance in modern endodontic microsurgery.

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 Keywords

Apicoectomy, Mineral trioxide aggregate(MTA), root end surgery, biodentine, Endodontic surgery, healing ,root canal

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Creative Commons Attribution 4.0 and The Open Definition

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Abdominal lumps in women can present a diagnostic challenge due to a wide range of possible etiologies. Ovarian cysts, although common, may rarely attain enormous sizes and present as huge abdominal masses, mimicking other intra-abdominal or retroperitoneal pathologies.In this case we report a case of a woman who presented with a progressively enlarging abdominal lump associated with abdominal distension and discomfort. Clinical examination revealed a large, non-tender abdominal mass occupying most of the abdomen.

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CASE STUDY OF HUGE ABDOMINAL LUMP AND ITS MANAGEMENT

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Underwater visual environments suffer from severe image degradation due to light absorption, scattering, color distortion, and turbidity, which significantly affects the performance of automated fish species recognition systems. To address these challenges, this paper presents an integrated deep learning framework for underwater fish species detection by combining diffusion-based image enhancement with a lightweight classification model. Initially, a diffusion probabilistic restoration model is employed to enhance underwater images by improving color fidelity, suppressing noise, and restoring contrast. The enhanced images are then classified using a MobileNetV2-based convolutional neural network optimized for real-time and resource-constrained deployment. Experimental evaluation is conducted using standard image quality metrics such as UIQM, UCIQE, PSNR, and SSIM, along with classification metrics including accuracy, precision, recall, and F1-score. The results demonstrate significant improvements in both visual quality and species recognition accuracy compared to conventional CNN-based approaches trained on raw underwater images. The proposed framework offers a robust and scalable solution for applications in aquaculture monitoring, marine biodiversity analysis, and underwater robotic vision systems.

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 Keywords

Underwater Image Enhancement, Diffusion Models, Fish Species Classification, MobileNetV2, Deep Learning, Marine Vision Systems, Aquatic Image Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Safety is crucial in every industrial or construction arena as accidents can also cause injuries and damages. To mitigate risks in real-time, modern surveillance employs multiple sensors to track and assess risks. For this project, we installed and set up configurations for several sensors including ultrasonic sensors for distance measuring, pulse sensors for monitoring human heart rate, IR flame sensors for identifying fire, and MQ-135 sensor for detecting harmful gases. These sensors allow for constant monitoring and detection of potential threats, leading to appropriate action. Consequently, these features and sensors improve accident prevention and safety at the site.

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 Keywords

Site Safety Detection, Ultrasonic and Pulse Sensor, Smoke and flame Detector, Buzzer and LED, Real-time Alert.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

--- The increasing digitalization of academic workflows has intensified the demand for intelligent systems that can evaluate handwritten assignments with speed, accuracy, and transparency. Traditional plagiarism detection tools are designed for typed text and fail to handle scanned handwritten documents, while manual grading remains subjective, inconsistent, and time-consuming. To address these limitations, this paper presents SmartEval, an integrated deep learning-driven framework for automated plagiarism detection and assignment grading. The system employs Optical Character Recognition (OCR) based on CNN-BiLSTM models with Connectionist Temporal Classification (CTC) loss to convert handwritten text into machine-readable form. The extracted text is processed through a hybrid similarity engine combining TF-IDF, cosine similarity, and contextual embeddings derived from Sentence-BERT to detect paraphrased, semantic, and cross-document plagiarism. Automated grading is performed using a rubric-weighted scoring model enhanced by semantic relevance, coherence evaluation, grammar assessment, and keyword density analysis. Experimental evaluation demonstrates high accuracy in OCR transcription, strong plagiarism detection performance, and grading outcomes that closely correlate with human evaluators. SmartEval offers a comprehensive, scalable, and objective mechanism for evaluating handwritten academic submissions, enhancing academic integrity and reducing educator workload

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 Keywords

Handwritten Text Recognition (HTR), Plagiarism Detection, Automated Grading, Optical Character Recognition (OCR), Deep Learning, Sentence-BERT (SBERT)

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Creative Commons Attribution 4.0 and The Open Definition