Volume 13 | Issue 12 |

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

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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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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

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

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The relationship between Human Rights, terrorism and counter-terrorism is a complex, three-way interaction. It is often described as a "delicate balance" where each element directly impacts the others, sometimes in reinforce ways and other times through conflict. Human rights are the legal boundary and the moral framework within which counter-terrorism must operate to be effective and legitimate. Terrorism is a violation of human rights. Counter-terrorism is a duty of the state to protect human rights.

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Creative Commons Attribution 4.0 and The Open Definition

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Human Rights, Terrorism, Counter-terrorism, Constitution

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Creative Commons Attribution 4.0 and The Open Definition

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Spatial information mining has emerged as a vital research domain as technological advancements continue to generate massive amounts of spatial data from diverse sources such as sensors, satellites, and mobile devices. Among various spatial data mining tasks, co-location pattern mining holds significant importance in geographical data analysis. It aims to identify subsets of spatial features or objects that frequently occur together within a given geographic space, revealing valuable spatial associations and dependencies. The fundamental concept underlying co-location pattern discovery is spatial proximity, which helps determine meaningful relationships among spatial entities distributed across large datasets. However, mining such co-location patterns is computationally expensive due to the high dimensionality and dense neighborhood relationships inherent in spatial data. To address these challenges, researchers have proposed several efficient spatial co-location mining algorithms capable of handling massive and complex datasets. This study introduces an alternative co-location pattern mining approach that utilizes the MapReduce parallel computing framework to improve scalability, reduce execution time, and optimize resource utilization. By distributing computational tasks across multiple nodes, the proposed method significantly enhances the performance of spatial mining operations. Experimental results validate the effectiveness of this framework, demonstrating that it achieves flexible, scalable, and efficient performance in processing large-scale spatial datasets, making it a robust solution for modern spatial data analysis challenges.

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 Keywords

Spatial Data Mining, Colocation Mining, Map-Reduce

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Creative Commons Attribution 4.0 and The Open Definition