Volume 14 | Issue 4 |

 Abstract

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Keywords: Chronic bronchitis, educational intervention, self-care, prevention, adults.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid evolution of Artificial Intelligence (AI) and mobile technologies has significantly reshaped the dynamics of English communication and language education. This research paper examines how digital environments are transforming linguistic practices, learning methodologies, and cultural frameworks associated with English. The study moves beyond the conventional perspective of technology as a neutral tool and instead conceptualizes it as an active force that influences communication patterns and educational structures. Using a qualitative and interpretive approach, the study explores key developments such as multimodal communication, AI-driven learning platforms, and smartphone-based linguistic interactions. These innovations have enhanced accessibility, flexibility, and learner autonomy, enabling users to engage with English beyond traditional classroom settings. However, the research also highlights critical concerns, including the decline of formal language structures, overdependence on automated systems, and reduced engagement with deep reading and analytical writing. The findings suggest that English is increasingly becoming a flexible, context-driven language shaped by digital interactions and global cultural exchanges. The study concludes that educational systems must adopt a balanced approach that integrates technological advancements while preserving critical thinking, linguistic accuracy, and cultural diversity. Such an approach is essential to ensure meaningful and sustainable language learning in the digital age.

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Creative Commons Attribution 4.0 and The Open Definition

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Digital Communication, Artificial Intelligence, English Language Learning, Mobile Learning, Cultural Transformation, Linguistic Practices

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Fiber Reinforced Concrete, Fly Ash, Steel Fiber, Compressive Strength, Sustainable Concrete.

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Offline signature verification is an effective biometric authentication method with widespread applications in areas including finance, law, and security. However, because of variations in the way that each person writes, as well as the existence of highly skilled forgeries, distinguishing real from false signatures can be very difficult. As a result, a number of researchers have proposed various methods for investigating the validity of signatures by applying principles of image processing and machine learning. Typically, researchers will preprocess the image of the signature, extract features related to it, and then classify these features to determine if a signature is either real or false. Historically, researchers have used many different methods to accomplish signature verification, including Support Vector Machines, Artificial Neural Networks, Hidden Markov Models, and Dynamic Time Warping, as well as more recently developing sophisticated detection models using deep learning architectures like Convolutional Neural Networks to improve accuracy. The purpose of this paper is to provide a survey of the current methods for offline signature verification to detail the advantages and disadvantages of each method. In addition, by comparing the strengths and weaknesses of their methodologies, researchers and practitioners can gain insight into what types of improvements can be made to signature verification systems in order to enhance their accuracy and dependability.

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Creative Commons Attribution 4.0 and The Open Definition

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Signature verification, Forgery detection, Biometrics, Image processing, Machine learning.

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Creative Commons Attribution 4.0 and The Open Definition

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Adolescents' psychological adjustment and overall well-being are influenced by attachment relationships and coping mechanisms. Due to being impacted by things such as parental deployment, frequent relocation, and parents being temporarily separated from them, children of defence personnel are more frequently thought to be at higher psychological risk than other children. Despite these assumptions, empirical research does not support this assumption with inconsistent findings. Therefore, the purpose of this study was to investigate attachment relationships, coping mechanisms, and psychological well-being among adolescents from both civilian and defence families. Quantitative cross-sectional data (comparing two groups of children) were collected. The sample included 30 children of defence personnel compared to 30 civilians (aged 14 - 18). The samples were compared using independent sample t-tests on the following standardized measures: Attachment Style, Brief COPE, and Psychological Well-Being (PWI) measures. No statistically significant differences were found between civilian versus defence personnel children regarding attachment, coping mechanisms, or psychological well-being. Thus, the findings suggest that a parent's occupational background does not solely determine the emotional adjustment of adolescents. This research highlights the role of familial and social supports in promoting adolescent well-being.

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Creative Commons Attribution 4.0 and The Open Definition

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attachment style, coping mechanisms, psychological well-being, adolescents, defence families.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Physical laws, Principles, Physical Expressions, Mathematical Formulation.

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UniAccess is a low-cost, multi-modal accessibility assistant that enhances digital interaction for users with physical, motor, and speech impairments. It integrates eye-tracking, voice commands, and hand gesture recognition into a single platform, enabling seamless mode switching. Using technologies like OpenCV, MediaPipe, and SpeechRecognition, it allows real-time, hands-free control, improving accessibility, flexibility, and user independence.

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Creative Commons Attribution 4.0 and The Open Definition

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eye-tracking, voice commands, and hand gestures

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Creative Commons Attribution 4.0 and The Open Definition

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Developing an effective model to identify uncertainties in real-world situations has been challenging due to incomplete, inconsistent, or ambiguous data. This research paper presents a new framework (FPNSM) basedon a five-component membership structure that extends current neutrosophic soft matrixes by enhancing and/or increasing uncertainty breakdown into levels of truth, contradiction, uncertainty, hesitation or falsity; enables users to create a more detailed and dynamic description of an undetermined (uncertain) piece of data. The FPNSM is developed by combining three theoretical components (fuzzy sets, neutrosophic sets and soft set theory). It systematically defines the matrix structure, fundamental operations, and applicable properties of the various algebraic structures associated with the FPNSM. It provides an evaluative basis through the establishment of score/accuracy functions that allow an evaluator to rank their alternatives in a variety of uncertain, dynamic environments. A decision algorithm based on the FPNSM is provided to assist in making decisions based on multiple criteria. In addition, the effectiveness of the FPNSM methodology has been demonstrated through a case study; therefore, this research demonstrates how using the FPNSM can enhance the precision of the output produced when evaluating alternative options relative to an objectively and systematically determined standard of performance compared with existing processes. Subsequently, this research represents an important advancement in uncertainty modeling and provides an avenue for further research into the fields of decision support systems, engineering applications, and data analysis under variable/complex conditions.

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soft set, Pentapartitioned neutrosophic soft set, fuzzy pentapartitioned neutrosophic soft set, fuzzy pentapartitioned neutrosophic soft matrix

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This work introduces the conceptual design of a RAG Based Learning Management System that harnesses semantic PDF analysis, vector-indexed storage, and intelligent module synthesis to offer each student a tailored study experience. Students supply two PDF inputs-- a set of Previous Year Questions (PYQ) and subject-specific study notes--which the system tokenises, chunks, and encodes into a persistent vector store through dense semantic embeddings. A Retrieval-Augmented Generation pipeline driven by the Gemini 2.5 large language model then cross-references retrieved chunks to produce cohesive study modules, each bundling curated YouTube video links, concise AI-drafted revision summaries, time-bounded MCQ assessments, and an interactive progress dashboard. A document-grounded conversational agent complements these modules by letting learners pose free-form questions against their own uploaded content. The overall stack pairs a FastAPI service layer with a React SPA presentation tier built with Vite and the YouTube Data API to yield a modular, scalable digital learning environment. Upon deployment, the system is anticipated to cut study-preparation overhead substantially while broadening syllabus coverage and sustaining higher learner engagement relative to conventional static platforms.

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Retrieval-Augmented Generation (RAG), Personalized Learning, Learning Management Systems (LMS), Vector Databases, Semantic Embeddings, PDF Analysis, Adaptive Learning, Large Language Models (LLMs), FastAPI, React.js, Gemini API.

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Object detection is an important issue in computer vision. It enables computers to find objects in images and video streams automatically. This capability is crucial for many applications, including surveillance systems, self-driving cars, medical imaging, and automation. Traditionally, object detection relies on complex processes that involve proposing objects, extracting features, and classifying them. These processes can be resource-intensive. Recently, the field has progressed with the introduction of single-stage object detectors like YOLO (You Only Look Once). These detectors have significantly increased the speed of object detection while keeping accuracy high. This paper introduces a new object detection framework called VisionX. VisionX is a web-based system that uses deep learning object detectors and modern web technologies. Its goal is to create a unified framework for various object detection applications. The framework uses YOLOv8 for object detection. It supports multiple input sources, including images, video streams, and webcam feeds. VisionX is built with React for the frontend and Flask for the backend. It includes key features such as object model selection, dataset management, real-time detection, filtering objects based on confidence levels, and identifying suspicious objects for security purposes. The system processes input data through a series of steps, including data preprocessing, object detection with YOLOv8, filtering results, and visualizing the objects. Experimental results indicate that this object detection framework is effective in terms of accuracy and speed. It effectively connects machine learning object detectors with user applications. VisionX can be applied in various scenarios, such as surveillance systems, smart object monitoring, and AI-driven automation.

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Creative Commons Attribution 4.0 and The Open Definition

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Object Detection, YOLOv8, Computer Vision, Deep Learning, Real-Time Detection, React, Flask, Web-Based System, Surveillance Systems, Machine Learning.

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Creative Commons Attribution 4.0 and The Open Definition