Volume 13 | Issue 4 |

 Abstract

Marma Sharira, a key concept in Ayurveda, refers to vital anatomical points where muscles, veins, ligaments, bones, and joints meet. Traditionally revered for their life-sustaining nature, these points were meticulously studied and protected during surgical interventions in ancient India. Marmas are the energy reservoirs, the centre of consciousness, and the hidden essential spots of the body and psyche. Marmas, which resemble acupressure spots, relate to different nerves (Nadis) and energy wheels (Chakras). In modern surgical practice, the understanding of Marma Sharira offers significant clinical insights, particularly in trauma care, regional anaesthesia, and minimally invasive procedures. By correlating Marma points with critical neurovascular junctions and anatomical landmarks recognized in contemporary medicine, surgeons can enhance surgical precision, minimize iatrogenic injury, and improve patient outcomes. The Marma points are where the life power, or Pran is located. Year-long blockages in the Marma points caused by toxins, stress, and bad emotions can result in pain, energy blockages, swelling, and a variety of ailments. Ayurveda implies marvel effect with Marma practice in surgical field through vital points to be focused on while treating the aliments.

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 Keywords

Marma, Marma Sharira, Vital Points, Marmabhighata.

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The rapid advancement of AI technologies such as Generative Adversarial Networks (GANs) and Diffusion Models has enabled the creation of hyper-realistic synthetic images, posing significant challenges in verifying the authenticity of digital content. This project addresses the research problem of distinguishing real images from AI-generated ones. We propose a solution by developing a Convolutional Neural Network (CNN)-based classification system, trained on a curated dataset of real and synthetic images. The model integrates with a web application that allows users to upload images and instantly receive classification results. Our CNN model achieved an accuracy exceeding 85% on test data, demonstrating its effectiveness. This paper outlines the dataset preparation, CNN architecture, training process, evaluation metrics, and future enhancement possibilities.

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 Keywords

AI image detection, CNN, deep learning, image classification, real vs AI, computer vision

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Iron deficiency is one of the most prevalent micronutrient deficiencies worldwide, necessitating innovative strategies for iron fortification. Chelation is a process of binding of ions and their molecules to metal ions. It enhances the bioavailability of iron and minimizes the challenges of oxidative stability and sensory properties with traditional iron fortification. Casein, a major milk protein, has a high affinity for binding divalent metal ions, forming stable complexes that protect iron from premature reactions in the food matrix and gastrointestinal tract. Chelation of iron with casein improves the solubility of iron in aqueous environments. Yoghurt, a widely consumed functional food, presents an ideal vehicle for nutritional fortification. The standard yoghurt was prepared according to the FSSAI specifications. The chelated iron was added into the boiled milk in the concentrations of 0.5g, 1.0g, 1.5g and 2.0g per 100ml milk respectively before fermentation. The four samples were analyzed for physicochemical, microbial, and sensory properties. Sensory analysis revealed that the yoghurt samples having of 0.5g and 1.0g and 1.5g levels of chelated iron were equal to the standard yoghurt in color, taste and texture compare to other concentrations. The iron content in 0.5, 1.0, 1.5, 2.0g yoghurt samples were control-0.5mg, 0.5-3.4mg, 1.0-5.2mg, 1.5-7.6mg and 2.0-9.8mg respectively was analyzed by ICP-OES method. The study highlights the potential of casein-chelated iron as an innovative fortification method, paving the way for nutritionally enriched dairy products.

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 Keywords

Iron, Fortification, Yoghurt, Fermentation, Casein chelated iron.

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 Abstract

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 Keywords

chronic kidney disease, descriptive, hemodialysis, tertiary hospital

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The rapid increase in vehicle usage worldwide has led to a surge in road accidents, resulting in millions of deaths and injuries annually. Human errors, vehicle malfunctions, and poor road conditions are major contributors. Delayed emergency responses exacerbate the issue. Road accidents have become a serious global concern. Traditional safety features like seat belts and airbags reduce injuries but don't prevent accidents. Modern technologies like IoT and sensor-based systems are being integrated into vehicles to improve safety. These systems detect potential hazards and alert drivers. They can help prevent accidents and improve road safety. Millions of people die due to the accidents. The main purpose of this paper is to rovide vehicle safety and a solution that automatically alert the driver to be cautious. In this system continuously monitoring the vehicle performance using sensors and the behaviour of driver with the use of IOT Technology. The Vehicle black box receives the information from various sensors like the MEMS sensor. If the accident occurs, by using GPS the vehicle location is traced and the information is sent to local hospital and police through the message. With the IOT Technology, this location is always traced in the cloud platform service. If any panic problems arises then the location is automatically sent through message and also the data will be uploaded through an IOT platform. The system incorporates an ultrasonic sensor to detect obstacles around the vehicle, ensuring timely responses to potential collisions. A relay is used to control the DC motor, which simulates vehicle movement, allowing for precise adjustments based on sensor input. Additionally, a buzzer provides audible alerts in the event of abnormal conditions, enhancing overall safety by promptly notifying the operator of any issues.

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 Keywords

Arduino UNO, Ultrasonic Sensor, MEMS Sensor, Alcohol Sensor, Potentiometer, GPS Module, GSM Module, LCD, DC Motor, Relay, Buzzer.

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This paper presents a refined approach to maximizing the effectiveness of grid- connected photovoltaic (PV) systems through an enhanced Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. While traditional P&O styles are valued for their simplicity, they frequently struggle with slow shadowing pets and significant power losses due to steady- state oscillations. Adaptive MPPT algorithms have surfaced as a result, stoutly conforming the anxiety step size grounded on propinquity to the maximum power point (MPP). still, they frequently come with increased computational complexity. To strike a balance between speed, delicacy, and simplicity, the proposed Modified P&O(MPO) algorithm limits the MPP hunt zone to just 10 of the power- voltage (P- V) wind -- where the MPP generally resides. It intelligently divides the wind into four regions using an estimated open- circuit voltage. In regions far from the MPP, it applies a large, fixed step size for quick shadowing, while by near regions, it adopts an adaptive strategy to minimize oscillations and power loss. Simulation results using MATLAB/ SIMULINK under varying irradiance biographies including sinusoidal, ramp, and realistic one- day conditions -- demonstrate the effectiveness of the proposed MPO algorithm. It achieves a fast confluence time of just 15 milliseconds and an emotional shadowing effectiveness of 99.8, outperforming both conventional and recent adaptive MPPT ways. This work highlights a practical and high- performance result for boosting the trustability and energy affair of ultramodern PV systems.

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 Keywords

Maximum Power Point Tracking Algorithm, Matlab Simulink, Open Circuit Voltage.

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 Abstract

In modern healthcare systems, the involvement of Third Party Administrators (TPA) plays a crucial role in managing insurance claims and ensuring that patients receive timely care. However, the discharge process for TPA patients in hospitals can often be time-consuming due to the need for multiple administrative approvals, coordination between the hospital and insurance providers, and documentation requirements. Reducing these delays is crucial for optimizing hospital operations and improving patient experience.

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 Keywords

TPA discharge process, Hospital discharge efficiency, Delay in discharge, patient satisfaction, Insurance claims management, Discharge coordination.

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Abstract-Crankshaft supporting bearings in internal combustion (IC) engines are critical for maintaining structural integrity, reducing friction, and ensuring smooth operation, especially at high engine speeds. However, premature wear of these bearings can lead to increased noise, vibration, and potential engine failure, posing significant challenges in automotive engine reliability. This research investigates the causes of whining noise and vibration issues observed in 160cc motorcycle engines which are attributed to defects in the crankshaft bearings. The primary objective is to analyze and identify bearing defects through vibration analysis, comparing faulty bearings with healthy counterparts. The study explores the development and progression of bearing defects over time, focusing on common failure modes such as damage to rolling elements, inner races, outer races, and cages. The research aims to establish methodologies for early detection and diagnosis of bearing faults. By pinpointing the root causes of bearing degradation and providing diagnostic solutions, this work contributes to improving thedurability and performance of motorcycle engines. The findings are expected to enhance engine reliability, reduce warranty costs, and offer a better user experience by addressing complaints related to bearing-related noise from engine and vibration issues.

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Rolling Element Bearing, Bearing Defect, Vibration Measurement

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Abstract The Historical Place Badami along with Aiholle, Pattadakal and some other sites in and around the valley of the River Malaprabha in Bagalkot District of Karnataka, contain some of the earliest temples built in stone of southern India, beginning with the constructions of the Early Chalukyan dynasty, which ruled from Badami (ancient Vatapi) during the 6th to 8th centuries CE. However, the history of construction of monuments in stone go back much farther in time, as evidenced by the large number of megalithic monuments that are distributed at several sites in the Malaprabha Valley. In continuation of our earlier work which argues that the Chalukyas and their successors were continuing the tradition of commemoration exemplified by the megaliths into later monumental architecture - ranging from temples to miniature shrines to other forms of commemoration, this paper examines the immediate landscape around the Bhutanath Temple at Badami (Vatapi) and interpret it as a memorial landscape with various forms of commemorative structures.

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Keywords: Huligemmana Kolla, , Bhutanath Temple, Megalithic, Ranganath Gudda, Agastya Teertha, Mahishasuramardini Durga, Hemakuta Hill and Kappearabhata

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Day by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. Heart disease cases are increasing rapidly every day, so it's really important to predict and detect them early. Catching the disease in time can help save lives and improve treatment. This research project focuses on predicting whether a person is likely to have heart disease, based on their medical information and history. To do this, we built a heart disease detection system that uses machine learning. We used several machine learning algorithms like Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest to analyze the data and predict if a patient has heart disease or not. The system looks at patterns and trends in the collected medical data to make smart predictions. After testing the performance of these models, we found that they can help improve medical care, make early diagnoses, and even lower healthcare costs. This project helped us gain valuable knowledge about how machine learning can be used to detect heart disease. The system was developed using a pynb (Jupyter Notebook) file format.

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 Keywords

Machine learning, Heart Disease, Diagnosis, Algorithm _

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Creative Commons Attribution 4.0 and The Open Definition