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

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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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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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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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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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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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--- 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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Human Rights, Terrorism, Counter-terrorism, Constitution

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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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Spatial Data Mining, Colocation Mining, Map-Reduce

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The growing need for faster and more energy-efficient digital systems has made it crucial to design effective Arithmetic Logic Units (ALUs). Traditional ALU designs often struggle with issues related to power use, circuit complexity, and delays, especially as device sizes keep shrinking. This work details the design and implementation of an optimized 8-bit ALU using standard digital logic methods suitable for VLSI applications. The new architecture can perform a wide variety of arithmetic and logical operations while focusing on simpler hardware, lower delays, and better energy efficiency. A streamlined control unit is included to allow for smooth operation switching with minimal extra work. Simulation results show that the new ALU provides stable performance with lower power needs and reduced gate use compared to standard designs. These features make the proposed ALU a strong option for low-power embedded systems, high-speed processors, and modern digital computing platforms.

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CMOS, 180nm, VLSI, Multiplexer, Logic Unit, Digital Design, ALU

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Toni Morrison's Beloved (1987) is a profound exploration of the enduring trauma of slavery and its intergenerational impact on Black women, families, and communities. Set in post-Civil War Ohio, the novel examines how the haunting memories of enslavement persist through what Morrison terms "rememory," where the past continually intrudes upon the present. This article analyses the intersections of trauma, memory, and motherhood in Beloved, exploring how Morrison redefines freedom, love, and maternal identity within the psychological and historical "afterlife of slavery." The study highlights how Morrison transforms the physical and emotional scars of racial violence into acts of narrative resistance and moral reclamation. Through the central figure of Sethe, a mother whose act of infanticide embodies both defiance and grief; Morrison exposes the contradictions of motherhood under slavery and the radical dimensions of maternal love. The novel's fragmented structure, spectral imagery, and multiplicity of voices mirror the fractured nature of traumatic memory, while its womanist perspective situates motherhood as a site of both pain and empowerment. Ultimately, Beloved functions as a counter-archive that reclaims silenced histories and converts private anguish into collective remembrance, portraying motherhood not only as a burden but as an enduring act of resistance, survival, and spiritual healing.

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Trauma; Memory; Motherhood; Toni Morrison; Beloved

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Anti-wrinkle technology now in use is based on the fact that wrinkles are an inevitable consequence of aging owing to the destruction of scleroprotein fibers and considerable loss of collagen. Due to the stratum corneum's great resilience, conventional therapies like laser and Botulinum toxin have some disadvantages, such as adverse skin reactions, time-consuming treatment methods, and ineffective anti-wrinkle product penetration. In light of this, the cosmetics industry has developed a number of products based on solid and dissolvable microneedles (MNs) combined with antiwrinkle formulations, taking advantage of the patient-compliant technology of MNs to treat skin wrinkles. However, the high molecular weight of the medicines limits the use of these MNs for medication delivery. Although a greater variety of active agents can be delivered by hollow MNs (HMNs), this is an area of antiwrinkle technology that has not received much attention. In this study, we address this gap by discussing the potential of bioinspired 3D printed HMNs in the treatment of wrinkles on the skin. We contrast anti-wrinkling treatment alternatives from the past and present, as well as the methods and difficulties associated with their production and marketing.

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antirinkle agents; hollow microneedles; skin wrinkles; 3D printing

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The evolution of modernism as a transformative literary movement, and the subsequent transitional shift from modernist to postmodernist paradigms, finds its intellectual roots in the Enlightenment movement of the seventeenth and eighteenth centuries. The philosophers and thinkers of that era advocated for a radical shift in the human perception of the world, prioritizing objective, rational methodologies of thought over the adherence to perceived delusions or superstitions. Following this trajectory, modernist writers systematically rejected traditionalist or conventional notions of art, instead generating literary works characterized by a callous, bleakly realistic, and often starkly unembellished perspective. Postmodernism, however, ventures significantly further in its sophisticated depiction of 'reality,' as the writers associated with this period intentionally replaced traditional illusionism with complex modes of abstraction. Kurt Vonnegut Jr. occupies an exceptionally prominent position at the absolute forefront of postmodern art due to his deliberate destabilization of the dominant discourses of religion, science, technology, and historical progress. He achieves this through the intricate intertwining of diverse narrative techniques situated within the specific rhetorical framework of black humor. The present study endeavors to critically examine the specific metafictional elements that Vonnegut employs throughout Cat's Cradle, focusing specifically on how these techniques facilitate a profound defamiliarization of the real. It is this systematic disruption of the reader's expectations and the subversion of established truths that ultimately imparts a distinctiveness and an enduring postmodern resonance to his literary work.

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 Keywords

Postmodernism, narrative strategies, defamiliarization, metafiction, intertextuality.

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The ever-expanding use of code-mixed language, mainly on social media platforms, has resulted in challenges for natural language processing (NLP) tasks, due to its irregular writing pattern and the application of multiple languages within a sentence or phrase. Code-mixing of Gujarati-English is increasingly common in multilingual communities, as Gujarati diaspora around the world switch between their mother tongue and English while commenting/twitting/posting their views. In this paper, we present an efficient solution for word level language identification for low resource scenarios by application of fine-tuned version of DistilBERT--a lightweight transformer-based model. Our dataset consists of code-mixed social media comments from YouTube with each word annotated as one of three language tags: Gujarati, English, or Other. It comprises of 77,761 annotated sentences containing 732,917 words, with language labels distributed as 56.06% Gujarati (GJ), 36.77% English (EN), and 7.10% Other (OT). The distinctive part of this work is its fine-tuning process which is entirely conducted using CPU by dividing the training data into chunks of 1000 sentences each. This chunk-based training allows the large dataset to be processed in incremented versions by preserving the optimizer and scheduler states through different iterations. The proposed model gained an accuracy of 97.09%, precision of 97.02%, recall of 97.09%, and F1-score of 97.01%. These results outperform our baseline ML based Random Forest Model which was trained on hand crafted features and achieved accuracy of 91.2%. This proves the effectiveness of transformer-based fine-tuning for language identification in code-mixed contexts.

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DistilBERT, NLP, LID

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 Abstract

Diabetes mellitus represents a chronic metabolic disorder characterized by hyperglycemia and impaired glucose homeostasis affecting hundreds of millions individuals globally with profound implications for morbidity, mortality, and healthcare expenditure. Conventional pharmaceutical therapies including insulin preparations and synthetic oral agents, while therapeutically effective, demonstrate variable individual responses and substantial adverse effect profiles limiting long-term clinical applicability. Medicinal plants represent valuable therapeutic reservoirs containing diverse phytochemical compounds demonstrating significant antidiabetic properties through multiple complementary mechanisms. Contemporary scientific investigation validates traditional herbal medicine approaches through comprehensive preclinical investigations and clinical trials demonstrating remarkable efficacy in glucose control and diabetes complication prevention. Phytochemical constituents including flavonoids, alkaloids, polyphenols, terpenoids, and saponins exert antidiabetic effects through diverse pathways encompassing enhanced insulin secretion, improved insulin sensitivity, inhibition of carbohydrate-digesting enzymes, stimulation of pancreatic beta cell regeneration, and modulation of glucose transporters. Gymnema sylvestre demonstrates exceptional efficacy through mechanisms promoting pancreatic beta cell proliferation and suppressing sweet taste sensation reducing sugar consumption. Bitter melon (Momordica charantia) activates multiple glucose uptake pathways including AMPK activation and GLUT4 translocation enhancing cellular glucose utilization. Fenugreek (Trigonella foenum-graecum) stimulates glucose-dependent insulin secretion while improving hepatic glucose metabolism. Curcuma longa and related spices including cinnamon activate AMP-activated protein kinase pathways fundamentally regulating glucose and lipid metabolism. Clinical evidence demonstrates herbal formulations reduce blood glucose levels, improve insulin sensitivity, and decrease hemoglobin A1c measurements with superior safety profiles compared with conventional agents. Emerging research emphasizes integration of evidence-based herbal therapeutics with pharmaceutical approaches enabling personalized medicine strategies optimizing individual therapeutic outcomes. Future advancement requires comprehensive long-term safety surveillance, standardization of herbal preparations, biomarker identification predicting responder populations, and development of evidence-based clinical guidelines facilitating informed therapeutic decision-making.

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Herbal Medicine, Antidiabetic Plants, Phytochemicals, Diabetes Mellitus, Glucose Control, Traditional Medicine, AMPK Pathway, Insulin Sensitivity

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 Abstract

Barium ferrites substituted by tin (Sn) with general formula Ba1-xSnxFeO19 (x=0.0 0.1 0.2 0.3) have been prepared by co precipitation method by using the ammonium solution co percipient agent. The samples were characterized by X-ray (XRD), scanning electron microscopy (SEM). The XRD analysis confirms the hexagonal phase formation for all the compounds and indicates that all Sn ions are situated into the lattice of barium ferrite with extra pick of Fe2O3. The crystallite size of all the samples were measured from XRD data. Scanning electron microscopy was carried out to observe the morphology of the synthesized Ba1-xSnxFeO19 ferrites. Temperature-dependent dc electrical resistivity of Tin doped Barium ferrites was investigated as a function Sn concentration. Here, in present study, the effects of Sn doping consecration on particle size and electrical properties are mainly studied.

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Barium ferrites, Ferromagnetism, Resistivity

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 Abstract

In the infancy of science and technology, the number of people on earth was very small. At that time, people did not learn to use nature. They lived naked, eating raw fruits and meat, their natural needs were very limited. As a result of science and technology, people learned to use various natural resources such as water, soil, minerals, and forests to meet their various needs. People started using natural resources like iron, coal, etc. for production. As a result, natural resources in some regions decreased compared to demand. On the other hand, the environment continues to deteriorate due to mineral extraction and management, excessive deforestation, and natural processes. Irrenewable natural resources may eventually run out due to excessive use. It is feared that human civilization will face a major crisis in the 20th century.

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THE DEPLETION OF RESOURCES IN AN INDICATOR OF A DARK ERA FOR FUTURE GENERATION

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