Volume 12 | Issue 12

 Abstract

In this study, we describe the synthesis and characterization of ZnS and manganese-doped ZnS nanoparticles using a simple and cost-effective chemical method with a polyvinyl alcohol (PVA) matrix. This approach is particularly well-suited for the industrial-scale production of nanoparticles. The synthesized materials were characterized through various techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-visible absorption spectroscopy, and photoluminescence (PL) analysis. Notably, the PL spectrum of the ZnS:Mn nanoparticles revealed a peak at 603 nm, which is attributed to the 4T1-6A1 transition of the manganese dopant.

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 Keywords

Nanoparticles, photoluminescence analysis, X-ray diffraction, ZnS

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India, the world's largest democracy, faces critical challenges in its electoral system, including voter fraud, accessibility issues, transparency concerns, and logistical inefficiencies. Blockchain technology, known for its immutability, transparency, and security, offers a transformative solution. This paper explores how blockchain can enhance the Indian voting system, presenting a detailed architecture, its potential benefits, and the challenges of implementation. The study also analyzes global case studies and proposes a phased approach to adoption in India, ensuring scalability, inclusivity, and trust.

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 Keywords

Blockchain Technology, Indian Electoral System, Digital Voting, Election Transparency, Voter Security, Decentralized Ledger, Immutable Records, Remote Voting, Electoral Reforms, Smart Contracts

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Urinary tract infections (UTIs) are prevalent, especially among women, and are often recurrent. This review explores the potential role of cranberry juice in UTI prevention, focusing on its primary active compounds--proanthocyanidins (PACs)--and their anti-adhesive properties against Uropathogenic Escherichia coli (UPEC). PACs inhibit bacterial adhesion to the urinary tract line, which may reduce infection rates. While clinical trials have reported mixed results, certain studies suggest a beneficial effect of cranberry products in lowering UTI recurrence in high-risk populations. This review discusses cranberries's active constituents, mechanisms of action and clinical efficacy, as well as limitations in current research, including variability in cranberry product formulations and dosages. Given concerns over antibiotic resistance, cranberries could serve as a non-antibiotic prophylactic option for UTI management.

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Proanthocyanidins (PACs), Anti-adhesion, Escherichia coli (E. coli), UTI recurrence prevention, Biofilm inhibition, Natural remedies for UTI, Cranberry PACs mechanism of action, Antibiotic resistance.

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The project presents a Heart Disease Prediction System leveraging a Decision Tree Classifier to identify the likelihood of heart disease based on medical parameters. The dataset includes key features such as age, cholesterol levels, blood pressure, and exercise-induced metrics, which are crucial indicators of cardiovascular health. The system is designed to process these inputs, train the model, and deliver predictions with commendable accuracy. By implementing a Flask-based web application, the solution ensures user-friendly and real-time access to the predictive tool, making it ideal for both healthcare professionals and individuals. This platform can facilitate early detection, enabling proactive measures to prevent severe outcomes. It not only aids in clinical decision-making but also empowers individuals to monitor their heart health. The combination of machine learning and web technologies enhances accessibility, paving the way for efficient, data-driven healthcare solutions. With its robust design, the system contributes to better heart disease risk assessment and management.

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 Keywords

Heart disease prediction, Decision Tree Classifier, Machine learning, Medical parameters, Cardiovascular health, Risk assessment, Real-time predictions, Flask-based web application, Healthcare professionals, Individual health monitoring, Early detection, Preventive healthcare, Data-driven healthcare solutions, User-friendly interface.

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Abstract: The fast fashion industry has revolutionized the accessibility of trendy clothing, fostering a culture of rapid consumption and frequent wardrobe renewal. However, this business model, driven by planned obsolescence and cost-cutting measures, has come at a steep environmental, social, and economic cost. This paper delves into the significant challenges posed by fast fashion, including its contributions to environmental degradation, and micro plastic pollution, as well as its exploitation of labour in low-income countries. The analysis also highlights the industry's reliance on unsustainable practices such as synthetic fibre use, hazardous dyeing processes, and a global supply chain that prioritizes efficiency over ethics. Despite growing consumer awareness and claims of sustainability, the industry remains plagued by opaque practices and a lack of regulatory oversight. By examining the interplay between consumer behaviour, industry practices, and regulatory frameworks, this paper seeks to uncover pathways for a more sustainable future. It emphasizes the urgent need for systemic change, integrating responsible production, ethical labour standards, and informed consumer choices to mitigate the environmental and social impacts of fast fashion while fostering a more equitable global economy.

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Key words: Fast Fashion, Environmental Degradation, Exploitation of Labour, Sustainability Consumer Behaviour

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The New Austrian Tunneling Method (NATM), a versatile tunneling approach, has become a preferred method in complex geological conditions. The Himalaya, due to its unique characteristics present major challenges for tunneling during design and construction phases as it represents an extensive zone of crustal deformation with highly deformed rock masses, unstable slopes and is one of the most seismically active region. In this paper we provide a comprehensive review of the application of NATM in Himalayan geology, and how it has helped to overcome major challenges faced during tunneling. Our findings highlight the flexibility of NATM in handling the dynamic geological conditions of the Himalaya which emphasize the need for continuous monitoring, flexible support systems, and technological advancements to enhance tunnel stability and safety.

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NATM, Himalayan Geology, Tunnel Construction, Geotechnical Challenges, Seismic Activity, Squeezing Ground, Rock Burst, Shear Zones, Fault zones, Shotcrete, Rock Bolts, Stress Monitoring, Hydrological conditions, Adaptive Support Systems

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This paper provides an in-depth review of career guidance systems, emphasizing their evolution through the integration of machine learning techniques, particularly the K-Nearest Neighbors (KNN) algorithm. It examines the development of intelligent career recommendation systems that utilize personality traits, aptitude scores, and educational backgrounds to deliver tailored career advice. Synthesizing insights from various studies, the paper highlights the effectiveness of KNN in applications such as collaborative filtering, resume classification, and precise career recommendations. A novel hybrid framework is proposed, combining KNN with complementary machine learning algorithms and incorporating a matching formula for improved career predictions. Additionally, the paper addresses challenges like data imbalance and overfitting, offering practical solutions to enhance system performance and reliability.

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Career recommendation, Assessment, Ocean model, KNN .

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Bael, also called Aegle marmelos (Linn) Correa, is a member of the Rutaceae family, is commonly available in moderately sized, slender, aromatic tree with well-known medicinal uses in India and Additionally in Bangladesh, Pakistan, sri lanka and Burma are its native habitat. This plant is dedicated to lord Shiva. In addition, the plant is thought to have significant medicinal value. Whose medicinal description is also depicted in the Ajanta cave and is mentioned in ancient texts such as the Veda, Puranas, Charakha Samhita and Brihat Samhita. The plant is used in Panchang form in Ayurveda to treat Ulcer, dysentery and diarrhea. According to folklore, plant part can be use to treat high blood pressure, malaria, ulcers, stomachaches, diabetes, skin condition, typhoid, cancer and other diseases. The plant fruit is edible and has significant therapeutic value due to the presence of vitamins, minerals, and other antioxidants. Phytochemicals screening of Aegle marmelos leaves extract confirmed the presence of Alkaloid, saponin, flavonoids, phytosterol, tannins, terpenoids, amino acid, coumarins, phenol, fatty acid and phenolic compound. The plant is associated with medicinal uses and also holds various pharmacological and therapeutic properties including ,anti-microbial activity, anti-cancer, cardioprotective effect, anti-hyperlipedemic activity, anti-ulcer activity, anti-pyretic or anti-inflammatory activity, hepatoprotective activity, radioprotective activity, anti-spermatogenic activity, wound-healing activity, anti-convulsant activity, immunomodulator effect, anti-depressant, anti-proliferative effect, anti-malarial activity, anti-histiminic activity, anti-arthritis, anti- oxidant, anti- fungal activity, analgesic and toxicological studies. In this review article, an effort has been made to summarize the pharmacological, phytochemical, and medicinal aspects of the Bael plant.

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Bael plant,Rutaceae,Anti cancer,Aegle marmelos.

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Bringing up a transgender kid is not simply parenting-- it's an excursion of adoration, learning and advancement. The parenting style adopted by parents has a profound resonance on child's life and such impact is fundamental for those children who identify themselves as transgenders at the very early stage of their lives. This paper investigates the encounters of parents exploring their child's personality, underlining the significance of giving a strong and confirming position to transgenders in the society. It investigates the crucial topic of gender identity disorder and confronts the conventional norms, encouraging acceptance of such people through Laurie Frankel's semi-autobiographical novel This Is How It Always Is. It also examines the strong intersections between identity, family and society, stressing the significance of making a more comprehensive and tolerating environment for transgender youngsters. It highlights the primary role of parental acknowledgment in advancing the prosperity and emotional well-being of transsexual kids, showing the valuable insights and advice on how to parent a transgender child.

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Parenting, transgender child, gender identity disorder, binary gender system, societal norms.

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Safe drinking water is vital to health. Although most people in the world have access to safe drinking water, as many as 663 million people still drink from untreated springs and groundwater sources, most living in remote rural areas. The scarcity of safe drinking water remains a serious problem in rural areas. In particular, groundwater pollution is caused by heavy metals, which are extremely harmful to human health due to their persistent, bio-accumulative, and hazardous nature. The scarcity of clean drinking water is a critical issue, driving the need for innovative, cost-effective, and efficient water purification solutions. This research focuses on developing an affordable, multi-functional clay-based ceramic filter matrix to treat drinking water. The proposed filter leverages the natural properties of clay, combined with other locally available materials, to create a porous matrix capable of removing pathogens, turbidity, and chemical contaminants. The fabrication process involves carefully selecting and preparing clay, mixing it with granulated carbon, molding it into desired shapes, and firing at controlled temperatures to achieve optimal porosity and structural integrity. The addition of granulated carbon is intended to improve the removal of contaminants such as organic compounds, chlorine, and heavy metals, thereby addressing microbial and chemical impurities. The resulting ceramic matrix provides physical filtration and incorporates antimicrobial and adsorptive functionalities, making it a versatile solution for various water quality challenges. Laboratory tests demonstrate that the clay-based ceramic filter can significantly reduce microbial contamination, remove suspended particles, and adsorb harmful chemicals, ensuring safe drinking water. Additionally, the low cost of production and the use of readily available materials make this technology accessible to low-income communities, particularly in rural areas. This study underscores the potential of clay-based ceramic filters as a sustainable and scalable solution for improving water quality, contributing to public health, and supporting the global effort to provide safe drinking water for all.

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drinking water, critical issue, ceramic filter, eco-friendly, economical

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The Permanent Magnetic Synchronous Motor (PMSM) has become increasingly popular in high-performance applications, especially in electric vehicles, due to its excellent power density, high power factor, and superior efficiency. This research presents a novel approach to speed control for PMSM drives utilizing fuzzy logic control techniques. While Field Oriented Control (FOC) has been the conventional method for regulating torque and speed in PMSM systems, advancements in vector control have broadened the usage of PMSMs to sectors traditionally dominated by DC motors. The proposed fuzzy logic-based speed controller undergoes extensive testing using MATLAB/SIMULINK across various scenarios, including sudden changes in load and quick fluctuations in speed, even encompassing abrupt reversals. Through thorough analysis and simulation, this research aims to validate the effectiveness of this method in achieving precise speed control and robust performance in dynamic conditions. By integrating the advantages of PMSM technology with the flexibility and strength of fuzzy logic control, this study aims to enhance high-performance electric drive systems, particularly in applications that demand rapid and responsive motor control. Ultimately, this work aspires to advance the current state of electric drive technology, establishing new benchmarks for efficiency and performance in challenging environments.

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PMSM, PWM, Fuzzy Logic Controller, Vector Control.

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This paper proposes an adaptive energy management strategy for hybrid energy storage systems (HESS) in electric vehicles (EVs) using PI and PID controllers. Existing speed-based energy management strategies have limitations. The proposed method optimizes power allocation between batteries and ultracapacitors using PID control, filtered demand power, and PSO algorithm for parameter optimization. Simulation results demonstrate improved performance, economy, and prolonged battery life.

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Electric Vehicles (EVs), Hybrid Energy Storage System (HESS), Energy Management, PI Controller, PID Controller, Power Optimization

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Abstract: - Introduction Artificial Intelligence (AI) is transforming healthcare by offering innovative solutions in diagnostics, treatment planning, and disease prevention. In dentistry, AI applications span radiology, orthodontics, endodontics, and restorative procedures, enhancing accuracy and efficiency. Despite its potential, the extent of AI awareness, knowledge, and adoption among dental professionals remains unclear, necessitating a focused investigation. Methodology A cross-sectional survey was conducted among 100 dental professionals, including practicing dentists, academics, and postgraduate students across various specialties. Participants were recruited through online and offline channels, adhering to inclusion criteria of a minimum Bachelor of Dental Surgery (BDS) qualification and active engagement in clinical practice or teaching. A structured questionnaire comprising five sections assessed demographics, AI awareness and attitudes, knowledge, barriers to AI adoption, and educational preferences. The tool's reliability was validated (Cronbach's alpha = 0.8) through a pilot study. Data collection involved voluntary participation with informed consent. Results The study revealed high awareness (84%) of AI applications in dentistry, with participants acknowledging its utility in enhancing diagnostic accuracy and efficiency. However, significant barriers to AI adoption were identified, including a lack of technical knowledge, inadequate infrastructure, and ethical concerns. The majority of participants expressed a strong preference for AI-focused educational programs, emphasizing hands-on training and workshops. Conclusion The findings underscore the need to address technical and infrastructural barriers to foster AI integration in dentistry. Targeted educational initiatives are essential to bridge the knowledge gap and equip dental professionals with the skills required to leverage AI effectively. This study provides a foundation for developing strategic interventions to promote AI adoption, ensuring its seamless incorporation into routine dental practice.

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Keywords: - Artificial Intelligence, Dentistry, Knowledge, AI Applications.

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The research focusses on the creation of a sustainable nonwoven composite air conditioner (AC) filter made from natural sisal fiber and recycled polyester fiber. The composite combines the strength and biodegradability of sisal fiber with the resilience and reusable nature of recycled polyester, resulting in a lightweight, cost-effective, and environmentally responsible filtering material. Important criteria such as fibers mixing ratios, bonding processes, and filtration effectiveness were assessed. The results show higher air filtration efficiency, lower pressure drop, and improved mechanical properties, indicating its potential for HVAC applications. This breakthrough helps with waste management and the advancement of green materials in the filtration industry.

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nonwoven composite, sisal fiber, recycled polyester, air conditioner filter.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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This paper looks into the integration of data visualization for finance, sales, and predictive analytics using AI/ML techniques in Power BI. The purpose of this research is to explore the capacity to enhance financial decision-making, sales forecasting, and trend analysis that data visualization and machine learning models can potentially create. A large dataset that contains financial, sales, and economic data was considered to demonstrate predictive analytics techniques, which would emphasize time-series forecasting and regression models. The native AI features of Power BI, which include its forecasting tools and the integration of machine learning via Python and R scripts, are used to build precise predictive models. Results reveal that the accuracy of sales and financial predictions increases substantially, key influencers are identified, and actionable insights are made available for decision-makers by the integration of Power BI's interactive dashboards with machine learning models. It demonstrates the advantages of deploying AI-powered visualizations such as decomposition trees and forecasting charts in Power BI for getting complex financial data in view. Data visualization combined with AI/ML techniques integrated with Power BI makes the tool a powerful tool business can use to help it optimize financial analysis and sales prediction

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 Keywords

Data Visualization, Finance, Sales, Predictive Analytics, AI/ML, Power BI

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Visual Question Answering (VQA) is an interdisciplinary domain at the intersection of computer vision and natural language processing, focusing on answering questions about images. This study proposes an enhanced framework that incorporates sentiment analysis into VQA systems, enabling context-aware and sentiment-sensitive responses. Such integration is pivotal in applications like e-commerce, healthcare, and social media, where understanding emotions in visual content significantly improves user interactions. The proposed methodology combines state-of-the-art VQA techniques with sentiment analysis models. Visual feature extraction is achieved using a pre-trained convolutional neural network (CNN) such as ResNet, while language understanding employs transformer-based architectures like BERT. A multimodal fusion mechanism integrates visual and textual data, augmented with sentiment features extracted using a separate sentiment analysis pipeline. The fused embeddings are then fed into a deep neural network to generate contextually relevant answers. Experiments are conducted on benchmark datasets such as VQA 2.0 and Visual Sentiment Ontology (VSO), incorporating synthetic datasets with sentiment annotations. Results demonstrate a significant improvement in performance, achieving an accuracy of 89.85% compared to 76.80% for baseline VQA models on the VQA 2.0 dataset. Additionally, contextual relevance is enhanced with sentiment features contributing to improved emotional understanding in responses. This paper contributes a novel multimodal approach that bridges the gap between VQA and sentiment analysis, addressing the lack of emotional intelligence in traditional VQA systems. The findings indicate promising avenues for future exploration in adaptive AI systems.

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Visual Question Answering, Sentiment Analysis, Multimodal Fusion, Context Aware Responses, Emotion Recognition, Deep Neural Networks

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 Abstract

Ovarian cancer is the sixth most common cancer and the seventh leading cause of cancer deaths among women worldwide. Descriptive cross-sectional approach was used for this study. The population included all the women above 40 years of age attending in Gynaecology OPD, WCH, JIPMER. The sample of the study was 240 women who fulfilled the inclusion criteria. The data was collected using structured questionnaires. The tool consisted of 2 Parts, Part A: Socio-demographic proforma, Part B: Clinical variables. After the ethical clearance the data was collected for 3 days. Both descriptive and inferential statistics were used for data analysis. All the categorical data were presented on frequencies and percentages. Analysis was carried out in SPSS version 22.0 (SPSS-Statistical Package for Social Science version 22.0). All statistical analysis has been carried out at 5% level of significance and p-value <0.05 was considered significant. The result of the study among 240 samples of women above 40 years of age in gynaecology OPD WCH, JIPMER showed that majority of the participant 76.6%(184) had inadequate knowledge, only 17% (41) of them had moderately adequate knowledge, 5.8% (14) had adequate knowledge and 0.4% (1) had excellent knowledge. Among Socio demographic variables there is association between education and income of the participants with the level of knowledge. The present study assessed the level of knowledge among women above 40 years of age attending gynecological OPD, WCH, JIPMER. It was found that majority of them had inadequate knowledge regarding ovarian cancer. Hence the study suggests that there is need to create awareness about early detection of ovarian cancer among the women. Further this study strongly suggest that there is a strong urge to improve the education and socio economic status of women as there was association found between education and income of the participants with knowledge.

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Ovaries, Cancer, Gynecology, OPD, Women

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Conventional drug delivery system has many issues so bulk of studies has now shifted from synthetic drugs to herbal drugs. The proposed observe turned into achieved and completed to assess the wound healing potential of the herbs like azadiracta indica (neem) and aloe Vera while formulated in shape of transdermal patches. The present study includes the drug delivery via transdermal patches for treating, curing, stopping diverse pores and skin allergy, contamination or wound healing. The fundamental purpose of this observe turned into to formulate the natural transdermal patches wherein neem plant extract is loaded in aloe vera patches which assist to deal with the pores and skin contamination like rashes, redness, and in wound healing. Herbal method includes the extract of herbs, vegetation and its element like root system and shoot system that are wealthy in diverse phytochemicals which allows to deal with diverse injuries, disorder or contamination. In diverse observe it's been visible and located that the vegetation like neem and aloe have the wound healing activities. Formulation were evaluated for the various organoleptic properties ,pH, thickness, moisture content etc.

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Aloe,Neem,Transdermal patches

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The primary objective of this study was to create user-friendly tablets of a model drug to enhance patient adherence to antilipidemic therapy. Many patients struggle to adhere to the prescribed dosage regimen, often skipping doses due to difficulties in swallowing tablets, bitter taste, and the unavailability of water when traveling. Mouth dissolving tablets (MDTs) offer a promising solution to improve patient compliance with the therapeutic regimen. These tablets disintegrate rapidly in the mouth, typically within a minute, eliminating the need to swallow the whole tablet or to use water. In this study, the bitter taste of the antilipidemic drug was effectively masked using the wet granulation method, employing both rapid mixture and a fluidized bed granulator. A total of 15 MDT formulations were prepared and subjected to comprehensive evaluation, including tests for hardness, friability, taste, in vitro disintegration time, wetting time, drug content, and in-vitro drug release. The optimized formulation achieved an in vitro disintegration time of 36 seconds, a hardness of 4-5 kg/cm2, a friability of 0.69%, and an impressive 99.5% drug release within 30 minutes, all while maintaining an acceptable taste. This successful development of patient-friendly MDTs for antilipidemic therapy holds significant promise for enhancing patient adherence to the prescribed therapeutic regimen.

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 Keywords

Taste masking, Mouth dissolving tablet, Rosuvastatin, Wet granulation.

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Creative Commons Attribution 4.0 and The Open Definition