Volume 13 | Issue 5 |

 Abstract

Gastrointestinal (GI) Hemorrhage is a critical medical condition marked by bleeding within the digestive system, which can occur at any point along the alimentary tract. It can be caused by factors such as peptic ulcer disease, inflammatory bowel disease (IBD), or excessive use of nonsteroidal anti-inflammatory drugs (NSAIDs). Early detection is crucial to prevent morbidity and mortality, but existing diagnostic modalities, including endoscopy and imaging studies, are invasive, costly, and uncomfortable for patients. This project proposes a non-invasive diagnostic system utilizing bioimpedance spectroscopy to detect GI hemorrhage. The system involves placing surface electrodes on the abdominal wall, where a small alternating current (AC) signal is used to measure tissue impedance. The presence of blood in the GI tract alters the impedance characteristics, which are detected by the impedance sensor and processed in real-time using a microcontroller-based unit, such as Arduino. he designs ensures rapid, painless, and accurate detection without the need for hospitalization. The proposed system aims to improve patient outcomes by offering a user-friendly, portable, and cost-effective solution for early diagnosis. It is especially beneficial in home-care settings and for patients who require frequent monitoring. Future developments include integrating artificial intelligence (AI) to enhance diagnostic accuracy and expanding the system's application to detect other gastrointestinal disorders non-invasively. SMART Medical Diagnostic is changing the way we detect gastrointestinal bleeding by using a simple, non-invasive method. With impedance sensor technology, it quickly and safely finds internal bleeding without the need for uncomfortable procedures. This new approach gives doctors accurate results in real-time, making diagnosis faster and easier for patients. SMART Medical Diagnostic is the future of gastrointestinal care--smart, simple, and patient-friendly. Keywords: Gastrointestinal (GI) hemorrhage, Gastrointestinal bleeding, Alimentary canal, Peptic ulcer disease, Inflammatory bowel disease (IBD), Nonsteroidal anti-inflammatory drugs (NSAIDs), Early detection, Morbidity and mortality, Non-invasive diagnostic system, Bioimpedance spectroscopy, Tissue impedance, Surface electrodes, Abdominal wall sensing

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Gastrointestinal (GI) hemorrhage, Gastrointestinal bleeding, Alimentary canal, Peptic ulcer disease, Inflammatory bowel disease (IBD), Nonsteroidal anti-inflammatory drugs (NSAIDs), Early detection, Morbidity and mortality, Non-invasive diagnostic system, Bioimpedance spectroscopy, Tissue impedance, Surface electrodes, Abdominal wall sensing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Vehicle overloading presents critical challenges to infrastructure maintenance and traffic safety. Current static weighing systems suffer from operational inefficiencies, prompting the development of an intelligent real-time monitoring solution. This research introduces an IoT-based overload detection system utilizing precision load cells and edge processing for instantaneous weight analysis. The system addresses three key consequences of overloading: compromised vehicle stability, accelerated mechanical wear (chassis, suspension, steering), and regulatory non-compliance. Implementation results demonstrate 92.3% detection accuracy with automated alerts via integrated displays and remote notifications. The solution significantly improves upon traditional methods by enabling proactive load management during cargo operations, simultaneously enhancing safety, compliance, and operational efficiency in commercial transport.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

vehicle overloading, real-time monitoring, IoT system, load cells, edge processing, weight detection, traffic safety, infrastructure protection, regulatory compliance, intelligent transportation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study is about the use of Variable Frequency Drives in Direct Current to Alternating Current applications. A Variable Frequency Drive (VFD) primarily manages the speed and torque of an AC motor by modifying the frequency and voltage it receives. However, their role in DC-to-AC conversion presents new concepts of power control for improvement in system efficiency and adaptability in systems that demand versatility within electrical outputs. A Variable Frequency Drive (VFD) is a key component in modern electrical systems, used to regulate the speed and torque of motors by altering the input frequency and voltage.This study focuses on the design and selection of VFDs, particularly for industrial applications, with emphasis on the key components such as diodes in the rectifier stage and IGBTs in the inverter stage. The paper also includes the detailed calculation for selecting VFD components based on motor specifications, using a 5 HP (3.73 kW) motor as a case study. The main objective is to offer insights into the appropriate VFD ratings, component selection, and the application of VFDs for controlling motors efficiently.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project aims to develop a virtual try-on system for shirts using computer vision techniques in Python. The system will leverage the OpenCV library to capture live video streams from a webcam and apply body gesture recognition to track key points on the user's body. By overlaying virtual shirts onto the user's video feed in real-time, the system will simulate a realistic try-on experience. The project will focus on enhancing the user's online shopping experience by providing a visual representation of how different shirts look and fit. The system's interface will allow users to interactively select and try on various shirts, providing them with a personalized and engaging shopping experience. Evaluation of the system will be based on its accuracy in simulating shirt fit, user feedback on usability and realism, and its ability to seamlessly integrate with existing e-commerce platforms. The outcome of this project is expected to improve user satisfaction and increase confidence in purchasing shirts online. Additionally, we identify and discuss similarities and differences in these methods.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

This project focuses on developing a real-time virtual try-on system for shirts using Python and OpenCV, incorporating body gesture recognition and keypoint tracking. Keywords include virtual try-on, computer vision, OpenCV, real-time overlay, body tracking, e-commerce integration, and user experience enhancement.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract Background: Hamstring injuries are common among athletes and can be particularly challenging to manage in para-athletes with underlying medical conditions. Thalassaemia minor may influence tissue healing, necessitating specialized approaches in rehabilitation. Aim: To evaluate the effectiveness of Growth Factor Concentration (GFC) therapy as a complementary intervention in the physiotherapeutic management of a hamstring injury in a T47 para-athlete with thalassaemia minor. Methodology: This case study involved a 31-year-old female T47 para-athlete diagnosed with a semitendinosus tear confirmed via ultrasound. Initial conservative treatment showed limited improvement. GFC therapy was introduced alongside a structured physiotherapy regimen. Functional and clinical assessments were conducted before and after treatment, including range of motion tests, manual muscle testing, pain scales, and balance evaluation. Results: Post-treatment results demonstrated significant improvement: hamstring strength improved from Grade 3/5 to 5/5, pain reduced from 8/10 to 1/10, and flexibility and stability were fully restored. The athlete returned to sport-specific activity without recurrence of symptoms. Conclusion therapy, when combined with targeted physiotherapy, significantly enhanced the healing process and functional recovery in a para-athlete with complex clinical considerations. It may be considered a viable adjunct in sports rehabilitation protocols, particularly for cases with compromised healing potential.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Hamstring strain, para-athlete, Growth Factor Concentration, thalassaemia minor, physiotherapy, soft tissue injury, sports rehabilitation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Autoimmune joint diseases are characterized by chronic synovial inflammation and immune dysfunction, leading to joint destruction and impaired function. Integrative approaches such as Ayurveda may offer therapeutic potential through multi-targeted strategies addressing symptoms and immune modulation. This study evaluated the clinical effectiveness and immunomodulatory impact of the Researchayu Regimen for Autoimmune Treatment (RRAT) in patients with seropositive autoimmune joint diseases. An open-label, retrospective, single-arm, multicenter study was conducted at five ResearchAYU centers across Maharashtra, India. A total of 55 patients aged 40-85 years with knee pain and positive rheumatoid factor (RA factor) and/or antinuclear antibody (ANA) were included. The RRAT protocol involved structured Ayurvedic oral formulations, topical Shuddhu Lepa, and therapeutic purgation (Sadhyovirechana). Clinical assessments and laboratory investigations (RA factor, ANA, C-reactive protein [CRP], and serum uric acid) were performed at baseline and after 90 days. Statistical analysis included descriptive statistics, McNemar's test for seroconversion, and paired t-tests or Wilcoxon signed-rank tests for continuous variables, with significance at P < 0.05. At day 90, 80.64% of ANA-positive and 81.25% of RA factor-positive participants achieved seroconversion. CRP and serum uric acid levels showed significant reductions of 33.23% and 25.05%, respectively. Clinically, there was a 61.22% mean relief in walking-related pain and a 57.5% resolution of morning stiffness. No serious adverse events were reported. These findings suggest that the RRAT protocol may offer beneficial clinical and immunological effects in autoimmune joint diseases. Prospective randomized controlled trials are needed for further validation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autoimmune joint disease, Rheumatoid arthritis, Ayurveda, Researchayu Regimen for Autoimmune Treatment (RRAT), Immunomodulation, C-reactive protein, Seroconversion.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in communication, behavior, and social interaction. Early detection of ASD is crucial for effective intervention, yet access to diagnostic tools remains limited in many regions. This study explores the application of machine learning techniques to develop a predictive model for ASD screening. Using a publicly available dataset, we preprocess and analyze features such as age, gender, and behavioral scores to train multiple algorithms, including Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest. The Random Forest algorithm demonstrated the highest accuracy, precision, and recall, achieving an Area Under the Curve (AUC) of approximately 1.0. A user-friendly web interface was developed using Streamlit to facilitate accessibility. Our results highlight the potential of machine learning as a preliminary screening tool for ASD, particularly in resource-constrained settings.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autism Spectrum Disorder, Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project aims to design and implement a Flask -based web application for automated leaf nutrition and disease classification. The application allows for the users to uploads a leaf image or capture one in real time using a webcam or mobile camera. The system uses OpenCV for preprocessing and a CNNor Mobile Net-based deep learning model to analyze the leaf. The model classifies the leaf into categories such as "healthy","Nutrientdeficient" or diseased. The platform ensures real time processing, displaying results on a user-friendly web interface.The application is built using flaskfor the backend.HTML,CSS Bootstrap for the frontend and TensorFlow orPyTorch for deep learning it is deployed on cloud platforms like python Anywhere making it a practical tool for smart agriculture .

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Flask web application,Leaf disease detection, Leaf nutrient classification, Image classification, Deep learning, CNN (Convolutional Neural Network), Mobile Net, OpenCV preprocessing, Real-time image analysis, Webcam integration, Mobile camera support, User-friendly interface.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Intellectual property rights (IPRs) in the contemporary knowledge economy have a central function to support innovation while influencing the manner in which knowledge is disseminated worldwide. Yet, there are conflicts between safeguarding creators' rights, providing public access to ideas, and dealing with socio-economic imbalances in knowledge dissemination. This article examines the ways in which IPRs have developed, how they influence innovation and access, and what obstacles they create to equal knowledge sharing on the global level

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Trade Marks, Patent, India

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A real-time human detection and people counting system designed for surveillance, crowd monitoring, and behavioral analytics. The system leverages the YOLOv8 deep learning model for accurate and high-speed object detection, combined with the DeepSORT tracking algorithm to ensure consistent identity tracking across video frames, even under occlusion or dense crowd conditions. A motion detection module based on frame differencing and contour analysis dynamically triggers detection only when significant movement is detected, reducing unnecessary computation and improving responsiveness. The graphical user interface (GUI), developed using Python's Tkinter library, allows users to manage video streams, adjust sensitivity, and view live statistics including real-time person count and motion alerts. Experimental results show that the system achieves high detection precision (up to 92.1%) and recall (98%) with a mean average precision (mAP) of 0.971, and maintains a real-time frame rate of 20-30 FPS on GPU hardware. Comparative analysis with the SSD (Single Shot Detector) model demonstrates that YOLOv8 provides superior performance in accuracy and tracking robustness, making it well-suited for both indoor and outdoor deployments. This work demonstrates the viability of deploying intelligent vision-based systems in scalable real-world applications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Computer Vision ,DeepSORT, GUI, Human Detection,kalman filter, Motion Detection, Python, Real-Time Tracking, Surveillance, YOLOv8

  License

Creative Commons Attribution 4.0 and The Open Definition