Volume 12 | Issue 12 |

 Abstract

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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 Keywords

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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 Keywords

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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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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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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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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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