Volume 14 | Issue 5 |

 Abstract

Career planning is a critical skill in the rapidly evolving 21st-century workplace, where the World Economic Forum projects that approximately 39% of core work skills will change by 2030. Existing career guidance platforms remain fragmented, static, and non-personalized, limiting their effectiveness for students and early-career professionals. This paper presents the design and development of a web-based Career Path Guidance System built using full-stack technologies--HTML/CSS/JavaScript for the frontend and Python/JSON for the backend. The proposed system provides personalized career roadmaps, skill assessment modules, and structured learning pathways in a unified, responsive interface. The architecture adopts a lightweight, database-free design using JSON files for persistence, ensuring simplicity and maintainability. Testing results confirm that all functional modules--including authentication, profile management, and roadmap generation--operate correctly and reliably. The platform addresses key gaps in existing systems by offering interactive, personalized, and data-driven career guidance, thereby empowering users to make proactive career decisions. Index Terms--Career guidance, full-stack web development, personalized roadmap, skill assessment, Python, JSON, HTML/CSS/JavaScript, career planning platform.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Career guidance, full-stack web development, personalized roadmap, skill assessment, Python, JSON, HTML/CSS/JavaScript, career planning platform.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Black cotton soil in India is another name for expansive soil. It tends to change its volume a lot because it takes in a lot of water and shrinks when it dries. This constant swelling and shrinking puts stress on structures, which can lead to damage like cracks in floors, foundations, and basement walls. The main problems with expansive soil are that it doesn't hold weight well, it compresses easily, it swells and puts pressure on things, and it shrinks. Studies have shown that using plastic and industrial waste materials is a good way to make expansive soil more stable. Adding materials like sawdust, rice husk, sugarcane bagasse, waste tires, incineration ash, fly ash, ceramic dust, glass fiber, broken bricks, agricultural waste, sawdust ash, and lime to the soil can make it better for geotechnical purposes. These improvements include a better maximum dry density, an optimal moisture content, a plastic limit, a liquid limit, a shear strength, a specific gravity, a swelling potential, and a California Bearing Ratio. Standard geotechnical tests like the Standard Proctor test, the Atterberg limits test, the specific gravity test, the unconfined compressive strength test, the California Bearing Ratio test, and the free swell test are used to measure these properties. This review paper is about figuring out the best amount of waste materials to use to make expansive soil better.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Expansive clay, Soil stabilization, Waste plastic, Industrial by-products, Geotechnical enhancement, Swelling behavior, Shear strength, Sustainable construction materials.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Parkinson disease (PD) is a progressive neurodegenerative disease, which has a significant impact on speech production due to motor disabilities. The early and precise diagnosis of PD is a burning clinical issue that indicates the necessity of non-invasive and valid diagnostic methods. This paper proposes a speech-based method of PD detection, and it employs spectral acoustic features and a support vector machine (SVM) classifier. Spectral descriptors such as Mel Frequency Cepstral Coefficients (MFCC) and Mel-spectrogram coefficients are received by acoustic signals in order to capture pathologic variations in the vocal patterns. Experimental findings show that the suggested framework provides an accuracy of 85% exceeding a number of the state-of-the-art approaches. These results suggest that spectral speech biomarkers together with machine learning algorithms provide an effective and non-invasive method of screening PD. Therefore, the suggested method has great potential in aiding the non-invasive diagnosis in the clinical environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

PD, Speech signal, RF ,Spectral Feature.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Extracellular vesicles are believed to play a vital role in various diseases. The study of extracellular vesicles (EVs) and nanoparticles (NPs) is rapidly expanding because recent discoveries have revealed a much greater complexity and diversity than was appreciated only a few years ago. New types of EVs and NPs have recently been described. Proteins and nucleic acids previously thought to be packaged in exosomes appear to be more enriched in different types of EVs and in two recently identified membranous NPs, exomeres and super meres. Thus, our understanding of cell biology and intercellular communication facilitated by the release of EVs and NPs is in flux. In this review, we describe the different types of EVs and NPs, highlight recent advances, and present major outstanding questions. Despite their clinical potential, the lack of sensitive preparatory and analytical technologies for EVs poses a barrier to clinical translation. New analytical platforms, including molecular ones, are thus actively being developed to address these challenges. Recent advances in the field are expected to have a far-reaching impact in both basic and translational studies.

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Role of Extracellular Vesicles in Intercellular Communication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Somdevsurivirachit Neetivakyamritam main Nyaaya Vyavastha

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

2320-2882

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project aims to enhance the classification of bone fractures in X-ray images using deep learning models. Accurate identification of fractures is crucial in the medical field, and this project explores multiple approaches to improve classification performance. The models used include VGG19 combined with Random Forest, MobileNet with Support Vector Machine (SVM) and Random Forest, EfficientNet with SVM and XGboost,. Each model is trained and tested to evaluate its ability to correctly classify different types of bone fractures. By comparing these models, the project identifies strengths and limitations of both standalone and hybrid architectures. A simple frontend interface is created using HTML, CSS, and JavaScript to allow users to interact with the classification system and view predictions. The application is not intended for deployment but rather to help understand the workflow of deep learning in medical imaging. This work provides insight into how deep learning and traditional machine learning methods can be combined for better results in image classification tasks, particularly in healthcare.

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 Keywords

Deep Learning, Bone Fracture, X-ray, VGG19, MobileNet, EfficientNet, SVM, Random Forest, Classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the potential of e-resource-based employment and livelihood diversification in the disadvantaged haor regions of Bangladesh, where traditional occupations like agriculture and fishing dominate. Despite the challenges posed by climatic vulnerabilities and limited access to modern resources, these communities exhibit adaptability through informal digital entrepreneurship and mobile-based services. The study examines the current state of digital infrastructure, skill levels, and socio-economic barriers that limit the full integration of technology in these regions. A mixed-methods approach, combining qualitative interviews and quantitative surveys, was employed to assess the role of ICT (Information and Communication Technology) in improving local livelihoods. Findings suggest that while there is a growing interest in digital solutions, key barriers such as poor internet connectivity, low digital literacy, and gender disparities need to be addressed. The study proposes targeted interventions, including digital literacy programs, improved infrastructure, and the promotion of female entrepreneurship, to facilitate a more inclusive digital economy in haor areas. Ultimately, the research highlights the significant opportunities and challenges of integrating e-resource-based employment strategies into the rural livelihoods of Bangladesh, aiming to contribute to sustainable rural development in vulnerable regions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-resource, Digital Employment, Haor Regions, Rural Livelihoods, ICT for Development, Rural Entrepreneurship, Rural Development

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

College students experience stress as a complex condition which involves multiple areas of their mental and emotional states together with their physical health and social interactions. Researchers currently study stress through combined stress metrics which fail to show how different stressors develop over time. The present study addresses this gap by examining the effects of Rajyoga meditation using a dimension-wise analytical framework. A quasi-experimental pre-test-post-test control group design was employed with 100 college students divided into experimental and control groups. Data were analyzed using descriptive statistics, paired samples t-tests, independent samples t-tests, and Cohen's d. The experimental group participated in an 8-week Rajyoga meditation intervention, while the control group received no structured training. The researchers used descriptive statistics together with paired samples t-tests and independent samples t-tests for data analysis, while effect size estimation was conducted using Cohen's d. The experimental group demonstrated significant reductions across all stress dimensions (p < .001). The change magnitude differed among domains because cognitive and emotional dimensions showed maximum decline while behavioral and physical dimensions achieved moderate progress and social stress experienced minimum transformation. The experimental group displayed significantly lower post-test stress scores when compared to the control group across all testing dimensions. Rajyoga meditation functions primarily through internal regulatory mechanisms which include improved thought awareness and cognitive-emotional restructuring, that ultimately affect social and behavioral development. The study establishes that multidimensional analysis functions as vital component for understanding stress reduction which occurs through different processes across various domains.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rajyoga Meditation; Multidimensional Stress; Cognitive Stress; Emotional Regulation; Quasi-Experimental Design

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents a smart and portable mini digital tool designed for the prediction of optimum maturity of sugarcane crops using NIR (Near Infrared) sensors, image processing, deep learning, and IoT technologies. The main objective of the system is to identify the ideal harvesting stage of sugarcane accurately, helping farmers and sugar industries improve sugar yield and crop quality. Traditional methods for checking sugarcane maturity are time-consuming, laboratory dependent, and often inaccurate. To overcome these limitations, the proposed system uses a combination of NIR sensors for sugar content estimation, camera-based image processing for cane color and texture analysis, and deep learning algorithms for intelligent maturity prediction. The NIR sensor collects spectral data related to sugar concentration and moisture content, while image processing techniques extract important features such as color variations, texture, and surface patterns of the sugarcane. The collected data is processed using deep learning models such as Convolutional Neural Networks (CNN) to classify the maturity level of the crop. The entire system is controlled using ESP8266/ESP32 microcontroller technology integrated with IoT for real-time monitoring and cloud-based data storage. The predicted maturity status is displayed on a digital display and can also be viewed through a mobile application or web dashboard. The proposed mini digital tool provides a low-cost, portable, and efficient solution for smart agriculture applications. It reduces manual effort, increases prediction accuracy, and supports farmers in making better harvesting decisions. The project can further be enhanced using Artificial Intelligence and advanced Machine Learning models for fully automated crop monitoring systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NIR Sensor, Sugarcane Maturity Prediction, Deep Learning, Image Processing, IoT, Smart Agriculture, CNN, ESP8266, Crop Monitoring, Portable Agricultural Device

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Hair plays a vital role in human attractiveness and reflects internal health. Since ancient times, herbs have been used for cleansing and maintaining hair. Herbal shampoo powders made from ingredients like brahmi, reetha, shikakai, amla, aloe vera, fenugreek, and neem are preferred due to their safety and effectiveness. These natural formulations help remove dirt, sebum, and dandruff while promoting hair growth, strengthening, and conditioning without causing damage. Shampoo primarily cleanses the scalp and hair by removing accumulated impurities. Compared to synthetic products, herbal shampoos are gaining popularity as they nourish hair and improve its health, offering a natural and gentle alternative for hair care.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Polyherbal shampoo, Natural ingredients, Herbal powder, Anti-dandruff, Organoleptic properties, Cleansing ability, Conditioning agent.

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Creative Commons Attribution 4.0 and The Open Definition