Volume 13 | Issue 11 |

 Abstract

Hearing assessment plays a vital role in the early detection and management of hearing impairments. Conventional audiometers used in clinics are often expensive, bulky, and require skilled professionals to operate. This paper presents the design and development of an app-based digital audiometer that offers a portable, low-cost, and user-friendly solution for basic hearing screening. The proposed system integrates a mobile application with calibrated earphones to generate and control test tones of varying frequencies and intensities. The app records user responses to produce an audiogram, indicating the hearing threshold levels for each ear. The system eliminates the need for specialized equipment while maintaining acceptable accuracy for screening purposes. Such an approach enables hearing tests to be conducted conveniently in remote or home settings, thereby improving accessibility and encouraging early diagnosis of hearing loss.The proposed system consists of a mobile application integrated with calibrated earphones or Bluetooth headphones to produce pure-tone signals across standard audiometric frequencies, typically ranging from 250 Hz to 8 kHz. The application adjusts sound intensity in controlled steps, allowing the user to indicate when a tone is heard.

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 Keywords

Digital Audiometer, Hearing Assessment, Mobile Application, Pure Tone Audiometry, Hearing Threshold, Signal Processing, Bluetooth Headphones, Portable Device.

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The contemporary international system faces an unprecedented tension between the foundational principle of state sovereignty and the imperatives of global governance. As transnational challenges--including climate change, pandemics, cybercrime, and humanitarian crises-- transcend national borders, the adequacy of a sovereignty centric international legal order becomes increasingly contested. This research paper examines the fundamental paradox: while international law requires states to be capable of binding themselves through voluntary agreements, the exercise of such binding authority through global governance institutions is frequently perceived as an intrusion upon sovereign prerogatives. The paper argues that traditional conceptions of sovereignty must be reconceptualized within the framework of contemporary global interdependence, rather than viewing state sovereignty and global governance as inherently incompatible. Through a comprehensive analysis of international legal instruments, institutional frameworks, and enforcement mechanisms, this study demonstrates that effective global governance need not necessitate the wholesale abdication of state sovereignty. Instead, the legitimacy of international law derives from its capacity to enhance state autonomy through self determination, democracy, and human rights protection. The paper examines specific transnational challenges--particularly climate governance, international humanitarian law, and pandemic response--to illustrate how states voluntarily limit sovereignty to address collective action problems. The research employs doctrinal legal analysis combined with institutional assessment to argue for a recalibration of the sovereignty governance nexus. It contends that international law's authority rests not on a denial of sovereignty but on a recognition that pooled sovereignty through multilateral frameworks better serves state interests and protects the values underlying sovereignty itself. The paper concludes that addressing 21st century transnational challenges requires innovative approaches to global governance that respect state sovereignty while establishing robust mechanisms for collective decision making and enforcement.

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State sovereignty; global governance; international law; transnational challenges; multilateralism; international institutions

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This study presents a machine learning-based framework for the automated detection of Bicuspid Aortic Valve (BAV) and the provision of personalized treatment recommendations using 2D echocardiography (Echo) parameters. Conventional diagnosis relies heavily on cardiologists' subjective interpretation of Echo images, which can lead to inter-observer variability and delayed diagnosis. The proposed system leverages structured echocardiographic features--including peak velocity, mean gradient, aortic valve area, left ventricular ejection fraction (LVEF), calcification score, and clinical symptomatology--to accurately classify BAV versus Tricuspid Aortic Valve (TAV), assess the severity of valvular abnormalities, and suggest suitable management strategies, ranging from monitoring and medical therapy to surgical intervention. The framework emphasizes interpretability and clinical applicability, aiming to enhance diagnostic precision and support individualized patient care in BAV management.

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 Keywords

Bicuspid Aortic Valve, Echocardiography, Machine Learning, Clinical Decision Support, Aortic Stenosis

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 Abstract

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Recent advances in artificial intelligence and medical imaging have transformed automated diagnostic systems, enabling earlier and more accurate identification of critical neurological conditions. This study presents an Automated Brain Tumor Detection System that integrates deep learning with 2D-to-3D medical imaging to enhance diagnostic precision and interpretability. The system is trained exclusively on magnetic resonance imaging (MRI) brain scans to determine the presence of tumors. A Convolutional Neural Network (CNN) architecture automatically extracts and analyzes spatial and textural features from the images to classify them as tumor or non-tumor cases. The framework employs three publicly available datasets--Brain Tumor MRI Dataset (Kaggle), BraTS 2020, and the Figshare Brain MRI Dataset--to ensure diversity, scalability, and robustness in model training and validation. Upon detecting a tumor, the system reconstructs the 2D slices into interactive 3D volumetric models using volumetric rendering techniques, enabling clinicians to assess tumor morphology, volume, and spatial location with greater accuracy. This approach minimizes human error, expedites the diagnostic process, and improves visualization of complex brain structures. By combining automated analysis with advanced 3D visualization, the proposed system delivers a reliable, efficient, and clinically relevant solution for brain tumor detection, facilitating faster decision-making and improved patient outcomes.

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Brain Tumor Detection, Deep Learning, MRI Imaging, Convolutional Neural Networks, 3D Reconstruction, AI-based Medical Diagnostics, Machine Learning

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This project aims to design a system utilizing FPGA for the classification of glaucoma disease using a CNNDENSENET121 based deep learning model. The primary goal is to optimize the design for the Airtex V7 FPGA architecture, employing a graphical user interface (GUI) for user interaction. The project begins by processing a glaucoma image dataset, where images are pre-processed through resizing, noise removal, normalization, and binary pattern extraction. Threshold segmentation is then applied to detect the affected regions in the images. Feature extraction is carried out using a CNNDENSENET121 model to identify critical patterns indicative of glaucoma. The dataset is split into training and testing subsets, with 80% allocated for model evaluation and 20% for prediction. For classification, the system incorporates the CNNDENSENET121 algorithm, which includes convolutional layers, batch normalization, activation functions (ReLU), pooling layers, and fully connected layers. The FPGA design is optimized for performance, with the final output prediction generated on the FPGA hardware. The system's performance is assessed using various metrics such as area, power, delay, and accuracy, along with RTL simulation and synthesis reports. Additional metrics like PSNR, SSIM, MSE, MAE, precision, recall, ROC, confusion matrix, and error rate are also evaluated to ensure robust classification performance. The proposed system effectively demonstrates the integration of machine learning algorithms with FPGA hardware for efficient glaucoma detection.

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area, power consumption, delay, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM).

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Genetic disorders present a major clinical and public health challenges worldwide, contributing significantly to pediatric morbidity, congenital abnormalities, and long-term disability. In Ayurveda, such hereditary or inborn errors are described under the broad concept of B?ja Do?a. The term "b?ja" in Ayurvedic parlance refers to the reproductive seed, encompassing both male (?ukra) and female (?o?ita) gametes. The detailed sub-classification of b?ja, b?jabh?ga (organ-specific segments), and b?jabh?g?vayava (microscopic or sub-organ parts) describes about genetic transmission and organogenesis. The development of a healthy embryo is said to depend on the harmonious functioning of ?itu (optimal timing), k?etra (suitable uterine environment), ambu (nutritive factors), and b?ja. Among these, b?ja holds central importance as the carrier of inherited physical, mental, and behavioural traits. Ayurveda not only addresses hereditary pathology through conceptual frameworks but also lays strong emphasis on preventive measures such as ?hodhana, garbh?dh?na sa?sk?ra, pumsavana karma, ?tucary?, ras?yana therapy, and ethical code of conduct (sadv?tta). This paper aims to delve into the Ayurvedic understanding of genetic disorders, explore the role of b?ja do?a in the context of classical embryology and reproductive physiology, and correlate these with modern genetics, including Chromosomal abnormalities and gene mutations. A conceptual bridge is built to integrate traditional insights with contemporary biomedical knowledge, and strategies for pre-conceptional and antenatal preventive approaches rooted in Ayurvedic classics are discussed.

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 Keywords

B?ja Do?a, Genetics, B?ja, B?jabh?ga, B?jabh?g?vayava, Preventive measures

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Ayurveda is one of the most ancient medical sciences of the world, which aims at promotion of health and treating the disease of an individual. In the last decades as suffering COVID situations, lot of interest has been generated in the medical world regarding Ayurveda and other contemporary medicines. Atharvaveda (around 1200 BC), Charak Samhita and Sushrut Samhita (1000-500 BC) are the main classics that give detailed descriptions of over 700 herbs. Ayurveda provides us the knowledge of overcoming the diseases. In Bhavprakash Nighantu, Acharya Bhavprakash has mentioned the herbs as Picrorhiza kurroa (Linn.) for its various activity. This potential drug has unique phytopharmacological properties.

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Picrorhiza kurroa, perennial herb, phyto-constituents, Immunomodulatory activity, Anti-arthritic activity, Hypolipemic activity, Anti-inflammatory activity Hepato-protective, Anti-diabetic

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The Saponin was extracted from Rice Husk through various steps of procedure:Extraction with Ethanol, Re-extraction, Separation of Aqeous and Organic Phase, Evaporation, Drying and Weight Determination: The Weight of Obtained Saponin was 1730 mg and % yield of Saponin was 86.50 %. Characterization of Physical Property was done by Determination of Surface Tension and CMC by using various analytical techniques i.e. Stalagmometer, Conductivitymeter, Absorbance by Spectrophotometer. The CMC of Saponin was found 0.04 gm/L and surface tension was 59.29 mN/m. The CMC of SLS was found 8.0 mM and surface tension was 32.55 mN/m. The Study of Anionic-nonionic mixed surfactant system: In present investigation, the interaction between sodium Lauryl Sulphate (SLS) as an anionic surfactant and Saponin as a nonionic surfactant is studied. Saponin is capable to reduce CMC of ( Anionic Surfactant-SLS) and as a result CMC of mixed Surfactant systems were also reduced up to 0.6 mM. As the ratio of Saponin is increasing in the mixed Surfactant systems, the CMC of mixed micelles are found reducing: The CMC of mix Surfactant 1:9 (Saponin + SLS) Solution is found 1.5 (mM). The CMC of mix Surfactant 3:7 (Saponin + SLS) Solution is found 0.9 (mM). The CMC of mix Surfactant 5:5 (Saponin + SLS) Solution is found 0.6 (mM).

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% yield, CMC, Surface Tension, SLS, mixed surfactant system, mixed micelle.

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This paper empirically tests the growth-sustainability paradox in India's construction sector using but realistic data. Adopting a mixed-methods approach, it combines input-output analysis, econometric modeling, life cycle assessment, and survey-based analysis. Findings confirm that construction drives GDP and employment while intensifying environmental degradation and labor precarity. Governance emerges as the key mediator. Scenario modeling shows that sustainability integration marginally reduces GDP share but enhances resilience by lowering emissions, increasing recycling, and improving labor protections.

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increasing recycling, and improving labor protection

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