Volume 13 | Issue 5 |

 Abstract

Artificial intelligence( AI) has revolutionized colorful fields, including healthcare, medicine delivery, and material wisdom. Liposomes, as protean nano carriers, have surfaced as promising tools in AI operations. This paper explores the crossroad of liposomes and AI, pressing their synergistic eventuality in medicine delivery, medical imaging, diagnostics, and further. We claw into the mechanisms of liposomal medicine delivery and bandy how AI algorithms enhance targeting, effectiveness, and remedial issues. likewise, we examine recent advancements in liposome-grounded imaging agents and biosensors eased by AI- driven analysis ways. also, challenges and unborn directions in integrating liposomes with AI are bandied, paving the way for innovative results in individualized drug and the diagnostics.

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 Keywords

Artificial intelligence, DENDRAL program, target identification, virtual screening, d e - novo drug design, machine learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The advancement of metal additive manufacturing, particularly using stainless steel 316L, has opened new frontiers in producing complex, high-performance components. This study focuses on optimizing key 3D printing parameters--including laser power, scanning speed, layer thickness, and hatch spacing--for the fabrication of parts using 316L stainless steel powder with controlled composition. A systematic design of experiments (DOE) approach is employed to assess the influence of these parameters on densification, surface finish, and mechanical properties such as tensile strength, hardness, and elongation. Additionally, the chemical composition of the powder is tailored to ensure consistent melt pool dynamics and enhanced printability. Microstructural analysis through scanning electron microscopy (SEM) and X-ray diffraction (XRD) is conducted to understand phase formation and grain morphology. The results demonstrate a significant improvement in mechanical performance with optimized processing conditions, underscoring the potential of parameter tuning and composition control in achieving reliable and high-strength stainless steel components for demanding engineering applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

3D Printing, Stainless Steel 316L, Additive Manufacturing, Parameter Optimization, Powder Metallurgy, Mechanical Properties, Controlled Composition, Microstructure, Selective Laser Melting (SLM), Metal Additive Manufacturing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid evolution of smart classroom management systems has transformed traditional learning environments, enabling enhanced efficiency and student engagement. TedxStudy, a MERN stack-based smart classroom management system, integrates AI-powered automation, real- time resource management, and interactive student support tools to optimize educational workflows. Key features include an AI-driven chatbot for student assistance, automated attendance management, resource sharing and booking, quiz link distribution, and structured safety protocols to ensure a secure learning space. The platform provides dedicated teacher and student dashboards, incorporating a personalized to-do list for students, and streamlining daily tasks and academic responsibilities. By leveraging cutting-edge web technologies and AI automation, TedxStudy enhances teacher efficiency, student engagement, and administrative processes. This paper explores its system architecture, core functionalities, and impact on modern education, along with a comparative analysis of existing solutions. Future advancements will focus on adaptive learning analytics, enhanced security, and scalability, further strengthening its role in smart education ecosystems

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Classroom, AI-powered chatbot, Automated Attendance Management, Resource Sharing, Resource Booking, Quiz Management, Student Dashboard, Teacher Dashboard, To-Do List, Adaptive Learning, Student Engagement, Learning Management System (LMS).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Gastroesophageal reflux disease (GERD) is a common condition managed with medications, but osteopathic visceral manipulation (OVM) offers a non-pharmacological alternative. This narrative review explores OVM's role in GERD management, focusing on mechanisms of action and clinical outcomes OVM improves GERD symptoms through enhanced diaphragmatic mobility, reduced visceral tension, autonomic modulation, and better circulation. Clinical studies report symptom relief, but evidence is limited. OVM is a promising adjunct for GERD management, but larger studies are needed to confirm its effectiveness.

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 Keywords

Osteopathic Visceral Manipulation, GERD, Manual therapy for GERD, and Visceral manipulation and reflux .

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The use of green energy is becoming increasingly more important in today's world. Therefore, electric vehicles are currently the best choice for the environment in terms of public and personal transportation. Because of its high energy and current density, lithium-ion batteries are widely used in electric vehicles. Unfortunately, lithium-ion batteries can be dangerous if they are not operated within their Safety Operation Area (SOA). Therefore, a battery management system (BMS) must be used in every lithium-ion battery, especially for those used in electric vehicle. In this work, the purpose, functions and topologies of BMS are discussed in detail. In addition, early battery models along with the hardware and system designs for BMS are covered in a literature review. Then, an improved battery model is introduced, and simulation results are shown to verify the model's performance. Finally, the design of a novel BMS hardware system and its experimental results are discussed. The possible improvements for the battery models and BMS hardware are given in the section on conclusions and future work. A battery management system (BMS) is proposed which is used for electronic vehicle that manages a rechargeable battery (cell or battery pack), such as by protecting the battery from operating outside its safe operating area, monitoring its state using PIC microcontroller.The controlling device of the whole system is PIC microcontroller. The integrated modules to the controller are temperature sensor, Battery pack along with relays, Charger and LCD Module. When the battery pack gets drained, it will charge through relays. Here we are using two relays for fast and slow charging. Here DC MOTOR works as a vehicle. While running the vehicle microcontroller will display the voltage and current values on LCD module as well as it displays the temperature continuously. If the temperature value crosses the set limit then PIC microcontroller active the buzzer for alerts. Based on the battery voltage it will charge the battery in two modes like fast and slow. Here relay works as a switch to on/off the charging connection. To achieve this task microcontroller loaded program written in embedded C l

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Electrical vehicle, Embedded System.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Introduction: Social media marketing is a form of marketing that utilizes online platforms for brands to advertise their products or services. The study documented the results in the extent of salons' social media marketing functionality through the perceptions of online consumers. Methodology: Using descriptive-comparative research design, a survey questionnaire was made and validated. The study obtained 88 respondents using convenience sampling techniques. Results: The study showed that there is no significant difference in the customers' perceived level of social media marketing when grouped according to sex. However, when grouped according to the generation and salon being reviewed, these groups had significantly different perceptions towards the sharing, presence, reputation, and relationship functionality of the page. Discussion: The study found that salons exhibited an overall high functionality for their social media pages. Specifically, it assessed the six functionalities in terms of Identity, Conversation, Presence, Reputation, Relationship, and Sharing Functionality. Online consumers perceived Identity Functionality as being utilized the most effectively among the six functionalities. This result showed that salons had distinguishable images for their brands, which consumers could quickly identify on their respective social media pages. The lowest assessed dimension is Relationship Functionality, which implied that the salons lacked connections with their online consumers. Conclusion: Owners and management of salons are recommended to create social media marketing strategies targeting their required demographic. Social media managers may explore the possibilities of employing techniques that give online users an incentive to continue interacting with their page. Posting interactive content that online users can participate in also develops authentic relationships. Strengthening these areas could lead to better customer retention and satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Marketing Functionalities; Online Consumer's Perceptions; Salon; Brand Image; Social Media Marketing; Qatar; Doha; Identity Functionality; Relationship Functionality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project focuses on developing an emotion recognition model that simplifies the process into key steps like data collection, feature extraction, and real-time deployment, while also considering ethical implications and user-friendliness. By accurately interpreting emotions from speech, facial expressions, and body language, such a model can enhance digital interactions by providing emotionally aware feedback, crucial for decision-making and improving user experience in various applications. To ensure the model performs effectively across various scenarios, a multimodal approach is utilized, combining audio-visual information and contextual data for enhanced emotion recognition. The system is trained using deep learning and machine learning algorithms on an extensive dataset that captures diverse emotional expressions. Implementing this model in real-time applications can be particularly beneficial in areas such as virtual communication tools, educational platforms, and healthcare systems, where emotional intelligence is essential for meaningful and responsive interactions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Emotion Detection, Deep Learning, NLP, Multimodal, Real-Time Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Explainable Artificial Intelligence (XAI) is a field of AI that focuses on making machine learning models transparent, interpretable, and understandable to humans. As AI systems become increasingly complex and integral to decision-making in areas like healthcare, finance, and autonomous systems, the need for interpretability grows to ensure trust, fairness, and accountability. XAI techniques aim to provide insights into model predictions, helping users understand the rationale behind AI-driven decisions. Methods such as feature importance analysis, SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and decision trees enable a balance between model performance and interpretability. The adoption of XAI not only improves user trust but also ensures compliance with ethical and regulatory standards like GDPR. This paper explores various XAI techniques, their applications, challenges, and future directions in bridging the gap between AI's predictive power and human interpretability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Explainable AI (XAI), Transparency in AI, Post-Hoc Methods, SHAP and LIME, AI in Healthcare and Finance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

India's foreign investment in Africa has emerged as a vital component of its broader diplomatic and economic engagement with the continent. This paper examines the growing role of Indian foreign direct investment (FDI) in Africa, highlighting key sectors such as pharmaceuticals, information technology, agriculture, infrastructure, and energy. It explores how these investments are driven by mutual interests, including access to natural resources, expanding markets, and strategic partnerships in the Global South. The study also assesses the impact of Indian investment on African development, technology transfer, employment generation, and capacity building. Moreover, it situates India's approach within the broader context of South-South cooperation, comparing it with China's investment model. Through an analysis of bilateral agreements, institutional mechanisms, and private sector initiatives, the paper underscores India's evolving role as a development partner in Africa.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Foreign Direct Investment (FDI), India-Africa Relations, South-South Cooperation, Economic Diplomacy, Infrastructure Development, Technology Transfer, Sustainable Development, Private Sector Engagement, Strategic Partnership, Resource Mobilization, Capacity Building, Bilateral Trade, Development Cooperation, Pharmaceutical Industry, Energy Security, Agricultural Investment, Digital Economy, Employment Generation, Geopolitical Influence, Multilateral Institutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The AI-Based Career Guidance System is an intelligent platform designed to assist students and recent graduates in making informed career decisions by offering personalized recommendations. It addresses the common challenges faced by individuals who are uncertain about their future paths due to limited awareness, rapidly changing job markets, and a lack of proper guidance. By utilizing machine learning and natural language processing techniques, the system analyzes a wide range of user data, including academic background, personal interests, acquired skills, and long-term aspirations. This data is collected through a user-friendly web-based interface that simplifies the process of inputting relevant information. The AI models then process this data to identify patterns and correlations between the user's profile and successful career trajectories in the current job market. Based on this analysis, the system suggests tailored career options that align with both the individual's strengths and market demands. Additionally, a feedback mechanism allows users to rate and review the recommendations, enabling the system to continuously improve its predictive accuracy and relevance through iterative learning. This adaptive approach ensures that the guidance provided remains up-to-date and aligned with evolving industry requirements.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning Algorithm, Google ML Kit, Cosine Similarity, Natural Language Processing, Career Recommendation System, Pattern Recognition

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Creative Commons Attribution 4.0 and The Open Definition