Trauma plays a major role in the formation of an individual’s self A devalued self often emerges from having a marginal status in the society, where traumas from racism, poverty, violence and exploitation are more likely to occur. The most difficult aspect of traumatic situations for victims is feeling that one is powerless to change his or her situation. Terrorism in Literature puts forth a number of thought-provoking questions before the readers as well as the writers. It ranges from the ethical function of literature to reconsidering our cultural assumptions about identity, relationality, and intentionality, to what contingencies determine how or if the individuals survive the devastations of trauma. Hosseini’s The Kite Runner published in the year 2003, introduces the readers to the country of Afghanistan, looks at the universal theme of transgression and forgiveness, of homeland and exile. This novel serve as a medium for the hapless millions of Afghans to voice the trauma of their existence. The Kite Runner (2003) gives a vision of what Afghanistan was before its association as a haven for terrorists. Hosseini deals with the theme of terrorism in his first novel The Kite Runner, in which he has given a genuine insight to the people of the world about the extremists Taliban, in particular, and the culture, and the traditions of Afghan people in general. This paper seeks to examine Hosseini's depiction of the complexities of life in Afghanistan during the regime of the Talibans. It shows how the country was seen as an asylum for some terrorists and their allies who are accused of the attack in the United States and other countries. Moreover, the paper addresses the need to fight against terrorism as a phenomenon to which the world became the witness of this gruesome reality. It, therefore, provides an analysis of terrorism, revealing the people's suffering caused by this terrorism. This paper seeks to examine Hosseini's depiction of the complexities of life in Afghanistan during the regime of the Talibans.It shows how the country was seen as an asylum for some terrorists and their allies who are accused of the attack in the United States and other countries

Buccal drug delivery has emerged as an effective alternative route for systemic and local administration of therapeutic agents. The buccal mucosa offers a highly vascularized, easily accessible, and relatively permeable surface that enables rapid drug absorption while bypassing hepatic first-pass metabolism. Buccal patches—thin, flexible, mucoadhesive dosage forms—provide controlled and sustained drug release with improved patient compliance and minimal gastrointestinal irritation. This review provides a comprehensive overview of buccal patches, including oral mucosa anatomy, mechanisms of buccal absorption, classification, formulation components, advantages, evaluation parameters, and types of natural and synthetic mucoadhesive polymers used. Various manufacturing methods such as solvent casting, direct milling, and hot-melt extrusion are discussed along with examples of drugs delivered via the buccal route. The review highlights the potential of buccal patches as a promising drug delivery system for enhancing bioavailability and therapeutic efficacy of numerous drugs

This project focuses on developing A system capable of automatically producing captions for pictures and convert those captions into natural-sounding speech. The system leverages The Flickr8K dataset, which is contains thousands of photos with numerous captions added by humans,to train a model for deep learning. The visual characteristics of the images are extracted using a pre-trained VGG16 Neural Network Convolution (CNN), while the sequence generation is performed using Networks using Long Short-Term Memory (LSTM) combined with a system of attention to produce contextually accurate and grammatically correct captions.The generated captions are then passed to a Text-to-Speech engine, which converts the passage into audio. The incorporation of vision, language, and speech technologies results within a structure that is not just able to describe images but also narrates them aloud, making it very helpful for situations such as assisting visually impaired individuals, automated content creation, education tools, and interactive systems.The project is implemented using TensorFlow/Keras for deep learning and Flask for building a web-based front end. The interface allows users to upload an image, view the generated caption, and listen to the audio narration. The evaluation of The apparatus is done using BLEU scores, which measure the caliber of the produced captions against human-annotated references. While the system achieves reasonable performance, challenges remain in improving accuracy, handling complex images, and producing more natural voice outputs

Hearing loss is one of the most common sensory impairments worldwide and often remains undetected until it begins to affect communication, learning, employability and quality of life. Conventional hearing screening relies on calibrated audiometers operated by trained clinicians in controlled environments. While clinically robust, such pathways are expensive, time-consuming and geographically inaccessible for many people, especially in rural or resource-constrained settings. Modern smartphones, however, combine high-fidelity audio output, capable processors and intuitive touch interfaces, making them promising platforms for preliminary, self-administered screening. This project presents Hearing Test App, an Android application that enables users to self-assess their hearing in a structured and repeatable manner and to track results on their own device. The app is developed in Java using Android Studio, follows an activity-based MVC structure and uses SQLite for local data storage to support offline usage and privacy. A user authentication module provides registration, login and password reset so that test histories remain personalised and protected. Within the hearing assessment workflow, the app presents tones at multiple frequencies for each ear and allows users to indicate audibility via a simple interaction. Although the application is positioned as a screening tool and not a medical device, it adopts good practices from pure-tone screening to provide meaningful, relative indicators of hearing ability. Results are shown in an easy-to-understand summary and retained locally for longitudinal comparison. The design also anticipates future extensions such as device/headphone calibration, ambient-noise checks, audiogram-style visualisation, export of results and multilingual support to improve accessibility

An extremely common neurodegenerative disease is Parkinson's disease (PD), which impacts motor functions, with tremor being one of its most debilitating symptoms. Accurate detection and classification of tremors are crucial for diagnosis, treatment monitoring, and rehabilitation. Traditional assessment methods—clinical observations and patient self-reports—are subjective, prone to bias, and lack real-time applicability. To overcome these limitations, this study proposes an intelligent tremor detection system leveraging wearable sensors and artificial intelligence. Recorded tremor-related motion signals (accelerometer and gyroscope) were preprocessed and segmented for extracting time-resolved information. Time- and frequency-based wavelet features were computed from which both deep learning models and machine learning models were trained. the TPCNNs and MLP for the comparative analysis networks, novelNameRandom Forest, Support Vector Machine (SVM), Naı̈ve Bayes, ConvolutionalNeural Networks (CNN) and Long Short Term Memory (LSTM). The experimental results showed that LSTM obtained the best classification accuracy which was attributed to its excellent performance in temporal dependency learning for biomedical signals. Random Forest also performed well with good interpretability, followed by relatively modest CNNs and MLPs. The conclusions are verified and it can be observed that deep learning-based models such as LSTM can offer reliable, real-time tremor detection which is eligible for using in wearable healthcare devices. The proposed system provides a feasible answer to the need for continuous, objective monitoring of tremor that is between subjective clinical assessments and automatic intelligent systems. It offers hope for provident medical care over the lifetime, targeted curative treatments and successful remote healthcare monitoring..

The digital interface of an educational institution, primarily its website, serves as the first point of contact and a continuous resource for students, parents, and staff. It is tasked with disseminating a vast array of critical information, including admission procedures, academic calendars, examination schedules, faculty details, and campus announcements. Traditionally, this information is presented through static web pages, requiring users to navigate complex menus and sift through extensive text to find specific details. This often becomes a time-consuming and frustrating process, particularly during peak periods such as admissions or examinations, leading to information delays and user dissatisfaction

Stroke is recognized as one of the most critical medical conditions, leading to high rates of mortality and long-term disability worldwide. It occurs when the supply of blood to the brain is interrupted, either due to an obstruction in the vessels (ischemic stroke) or internal bleeding (hemorrhagic stroke). The consequences of stroke are often severe, including impaired mobility, loss of memory, and difficulties in performing daily activities. Early diagnosis and timely intervention are therefore essential to reduce fatality rates and improve recovery outcomes. However, conventional diagnostic tools such as CT scans and MRI, while effective, are often expensive, time-consuming, and dependent on advanced medical infrastructure. Limitations make them unsuitable for large-scale, real-time, and low- cost stroke detection, especially in developing regions. Presents a computer-aided stroke prediction model using advanced deep learning approaches. In this study, medical data were collected and preprocessed to design predictive models (CNN), DenseNet, and VGG-16 architectures. Each model was evaluated metrics like accuracy, precision, and execution speed, the study aimed to find the most efficient technique. The outcomes show that the CNN-based model consistently achieved better outcomes compared to DenseNet and VGG-16, demonstrating higher prediction accuracy and faster computation. By effectively learning complex patterns from patient data, the CNN model enables accurate identification of individuals at risk of stroke. The proposed framework highlights the role of AI in modern healthcare, particularly in non-invasive and cost- effective disease prediction. It provides a decision-support system that can assist clinicians in early risk assessment and timely medical intervention, reducing the likelihood of fatal outcomes. To improve patient monitoring, minimize healthcare costs, and ultimately contribute to the global effort in combating stroke-related mortality and disability.

Abstract: Today world is facing new challenges regarding environmental change, Green business practices is one of the innovations which refer to strategies and operations that minimize environmental impact while promoting sustainability and ethical responsibility. As global awareness of climate change, resource depletion, and environmental degradation increases, businesses are increasingly adopting eco-friendly approaches across their supply chains, production methods, and corporate policies. By integrating these as energy efficiency, waste reduction, sustainable supply sourcing, eco-design, and responsible recycling. Implementing green practices not only helps protect the environment but also enhances brand reputation, fosters customer loyalty, and often leads to long-term and medium-term cost savings. NCC can organize training programs and workshops focusing on green entrepreneurship skills targeted at young women cadets, helping them become eco-entrepreneurs or green leaders. Women cadets can be groomed as change agents who mentor community women in sustainable business practices and innovation. NCC can partner with governmental and non-governmental organizations to create platforms where women entrepreneurs get access to resources, markets, and mentorship additionally through NCC’s extensive institutional reach. Support for women’s green startups can be enhanced through NCC’s network and mobilization capabilities. NCC can integrate modules on environmental sustainability and women empowerment in its curriculum, nurturing eco-conscious, empowered women leaders from the cadet level. Special projects can be launched such as “One Cadet, One Tree” with a focus on empowering young women cadets in environmental stewardship and entrepreneurial green business models. NCC units can conduct awareness campaigns on sustainability and women’s economic empowerment in schools and local communities, encouraging participation in green business activities. Campaigns like tree planting, waste management, and plastic-free drives led by NCC women cadets can inspire and involve women from their neighborhoods. NCC trained Women’s with all the possibilities which can help the economic empowerment to the society. This paper going to explore the challenges and innovates ideas to be skilled in different areas without distributing environment.

Abstract: Price elasticity of demand (Ed) quantifies the responsiveness of quantity demanded to price changes, assuming other factors remain constant. Traditional methods of calculating Ed, such as the percentage method and point method, often produce mathematically inconsistent results in the computation of marginal revenue (MR) and total revenue (TR) changes when analysed bidirectionally along the demand curve. This paper critically reviews exiting methods of calculating price elasticity of demand such as percentage method and point method with the help of illustrative explanation and highlights inconsistencies in them and to overcome these inconsistencies, the midpoint (arc elasticity) method is introduced, which employs the arithmetic mean of price and quantity between two points on the demand curve. Using geometric and algebraic analysis, the paper shows that this method ensures mathematically consistent and direction-independent calculation of Ed, MR and TR changes. The findings provide a more reliable framework for both theoretical modelling and applied economic analysis, enhancing the accuracy of revenue projections and pricing strategies.

Abstract: This study examines the connection between early technology exposure and social-digital outcomes as per parental guidance practices among Generation Alpha children. The research focuses on how parents mediate technology use regarding children's digital development. Data were surely collected from 141 millennial parents in Bangalore through structured questionnaires. Moreover, this approach helped gather reliable information for the study. Four major factors were used in this study: Technology-driven Social Validation Pressures, Digital Learning Disruptions, Marketing Influence and Consumer Susceptibility, and Emotional Resilience, which have been divided into high and low groups. In addition, the use of logistic regression was to check how technology exposure and parent control factors definitely affect children's results. The study also looked at basic family details to see their impact. It is also observed that learning orientation and parental support help to reduce high validation pressures in children, but resilience only increases these pressures unexpectedly. Basically, the model got 85.7% accuracy with good agreement (κ = 0.71), but the ROC curve showed poor discrimination with an AUC of 0.461, which is the same as saying it struggles with probability predictions. As per these findings, logistic regression shows good potential for predicting Gen-Alpha's digital validation habits but also has clear limitations. Regarding the results, this method can explain some patterns but cannot capture all aspects of their online behavior. The study shows that parental strategies and socio-cognitive orientations both shape children's digital behaviors. It further suggests that nonlinear modeling approaches can provide better predictive insights, as the relationship itself is complex.