Metallurgical and Materials Engineering (E-Journal)
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Investigation of Al-Ti Nano Graphene Hybrid Approach in Power Mixed EDM: Advancing Electrical Conductivity, Stability, and Material Removal with Ceramic Integration
Get PDFThe composite Al-Ti nano graphene alloy is efficient for industrial applications. Few research created nano graphene-aluminum composites. This research uses electrical arc machining to examine the effects of Al-Ti nano graphene composites on hardness, removal rate, roughness, resistance, and microstructure. The MIKROTOOLS DT- 110, with micromachining and high accuracy, was used for this work. Olympus SZX-12, Zeiss Supra 35 VP scanning electron microscope, and Energy Dispersive Spectroscopy were utilized to evaluate novel Al-Ti nano composites. Micro EDM, powder mixed micro-EDM, and die-sinker EDM were the major experimental methods. This study found steady improvement in corrected electrical conductivity, optimized machinery efficiency, Kojima and Pandey plasma channel radius approximations, and maximum heat flux approximations for the formulated new aluminum composites. The present study found successful machining with low material removal rate; cardon deposition controlled machining outputs (surface cracks and porosity); hybrid process achieved successful machining and removal rate; and high performance properties for the developed composites compared to conventional types. This research found the hybrid Al-Ti nano graphene method important for electric conductivity, stability, and material removal. This is the first research to examine power mixed EDM's advantages from ceramics with optimum performance
An Analysis of Economic Viability of Bell Metal Production in Sarthebari of Assam
Get PDFOne of the oldest traditions in Sarthebari, Assam, is the manufacturing of Kah (bell metal), which connects indigenous artisan economy with cultural legacy. From the time of ancient kings to the present day status the industry provides livelihood to lots of people in the region. This study provides a detailed economic comparison of conventional copper-tin alloy casting costs and modern industrial production processes. The study investigates the costs of raw materials, labor, energy use, and equipment investments using primary data gathered from structured interviews with regional artisans and secondary data from government records and commodities exchanges. Although industrial technologies lower unit costs through mechanization, the cultural value and market share of handcrafted products allow for higher prices, enhancing artisan livelihoods. The study also evaluates how global copper and tin price changes affect production costs, as well as the significance of regulatory interventions and sustainable tourism initiatives in supporting the traditional sector. The study concludes that a balanced strategy that sustains the artisanal skills essential to Sarthebari's Kah production while embracing selective technology is required
Enhancing Battery Health in Electric Vehicles: AI-Enhanced BMS for Accurate SoC, SoH, and Fault Diagnosis
Get PDFElectric vehicles (EVs) are the only way to solve both harmful fuel emissions and other environmental issues. Safety, efficiency, and lifetime of electric vehicles depend on their battery management system (BMS), which is thus essential. Since internal resistance causes the capacity of the battery to drop with age, the BMS must continuously check its state. Reliable State of Health (SoH) as well as State of Charge (SoC) predictions call for more complex algorithms considering charging time, current, and capacity. Artificial intelligence (AI) driven methods increase the precision of diagnostics, the speed of issue identification, and the regulation of thermal management—all of which help to ensure the performance and safety of batteries. A malfunction diagnostic system offers further more protections. By means of successful use of these BMS algorithms, Energy Storage Systems (ESS) achieves effective control of battery capacity and long-term viability of operations of electric vehicles
Sustainable Energy Storage System: A Metrological and AI-Based Control Approach
Get PDFEnergy storage systems (ESS) play an essential role for improving the longevity, dependability, and efficiency of power systems. Manufacturers accomplish this by providing grid support services and reducing the unpredictability of green energy sources. Because energy markets and grid conditions constantly shift and the many components of the system interact in complex ways, it is still challenging to get ESS to function and be regulated as effectively as possible. Artificial Intelligence (AI) is thus emerging as a promising means of enhancing ESS control techniques, offering smart and adaptable solutions to these challenging problems. This study examines many AI-based control strategies for improving the performance of energy storage devices. The most recent developments in deep learning, machine learning, reinforcement learning (RL) and evolutionary algorithms for ESS control are examined. It demonstrates their capacity to real-time adjust control techniques, understand intricate patterns from historical data, and capture nonlinear system dynamics. By mixing AI methods with normal optimisation and control algorithms, the study additionally addresses about how to make ESS work faster and more reliably. To lower high loads, balance loads, control frequency, and add green energy, this article addresses a few ways AI-based ESS control can be employed. The accuracy, effectiveness, and stability of energy sources might be enhanced by AI's potential to change the way energy storage systems are designed and operated
The Impact of Leadership Styles on Employee Well-Being: A Social Science Perspective
Get PDFLeadership styles serve as fundamental instruments for forming the way employees and work environments interact as well as staff well-being. The investigation in this paper focuses on how transformational leadership styles and transactional leadership styles and autocratic leadership styles along with laissez-faire leadership styles influence employee psychological levels and emotional well-being and professional development. The research implements both literature review and empirical examinations to discover the main factors behind work environments that become either favorable or unfavorable. Studies show transformational leadership creates positive work relationships between leaders and workers because it enhances their engagement and satisfaction but autocratic leadership typically generates stress and burnout conditions. Research findings present organizations with essential information about establishing supportive workplaces that promote productivity
Digital Trust Redefined: Blockchain-Based Notarization System Using Eid Card
Get PDFIn the postmodern era, ensuring trust in electronic transactions is fundamental, especially for notarization and document verification. Traditional notarization systems rely on centralized authorities and are susceptible to forgery, data leaks, and unauthorized manipulations. This paper proposes a blockchain-based notarization system incorporating electronic identity (eID) cards to facilitate secure, immutable, and irrevocable record authentication. The eID card serves as an advanced verification tool, allowing users to securely sign and notarize documents with cryptographic verification. The notarization process involves hashing the document, storing the hash on a decentralized blockchain ledger, and linking it to the user's eID-based digital signature. Any change to the document renders the notarization invalid, preventing fraud and unauthorized alterations. A decentralized verification mechanism enables authorized entities, such as government agencies, banks, and legal institutions, to verify notarized documents without relying on a single central authority. The system integrates Inter Planetary File System (IPFS) or similar secure storage solutions to store document copies off-chain, leaving only cryptographic proofs on the blockchain to balance security and efficiency. This blockchain-based notarization system provides enhanced security, transparency, and reduced operational costs compared to traditional approaches. It eliminates intermediaries, reduces processing time, and provides irrefutable proof of authenticity. The system is applicable to various use cases, including legal contracts, property deeds, and educational certificates, ensuring tamper-proof notarization
Designing Scalable Data Product Architectures With Agentic AI And ML: A Cross-Industry Study Of Cloud-Enabled Intelligence In Supply Chain, Insurance, Retail, Manufacturing, And Financial Services
Get PDFThe emergence of industrial product lines enabled the creation of the most complex products ever. Product models are necessary to design, configure and maintain this complexity. The systems at the core of current scalable product based software development are usually realized as rigid to change data models embodied in relational databases. This makes it expensive to exploit product model data and hampers innovation. Semantic technologies remove many of these problems but until recently lacked the performance and scalability to be put into production for large product lines. With the advent of linked data platforms this has changed. This paper outlines our design considerations for a product model framework based on the linked data principle and motivated by both business and technical needs. We present our architectural blueprint for product models and show how we apply this to three different domains. These domain models cover conventional data products, devising interaction with humans, and facilitating cooperative distributed creation of data collections. Our chosen level of generalization enables us to expose important ideas factored into our framework. It also sets the stage for open collaboration on the development and extension of product model ontologies.
Currently our data products exist as independent implementations to a varying degree addressing their respective business needs. We plan to join forces with partners to realize a family of linked data products describing different fields of human endeavor. Case studies are the ideal method to get involved in such an endeavor. To that end we invite readers to contribute to our effort. In the remainder of the paper we first outline design rationales in Section 1. Section 2 presents a blueprint for a linked data product family. Domain models, realizing components of the product blueprint, are then described in Section 3. The paper is concluded in Section 4
Fintech's Role In Financial Inclusion In The Indian Landscape
Get PDFFinancial inclusion remains a critical component in India's journey toward equitable and sustainable economic growth. In recent years, financial technology (fintech) has emerged as a transformative force, bridging the gap between traditional financial services and underserved populations. This study explores the evolving role of fintech in enhancing financial inclusion across India by examining digital payment platforms, mobile banking, peer-to-peer lending, and micro-investment services. The paper highlights how innovations such as Aadhaar-enabled payment systems, UPI (Unified Payments Interface), and mobile wallets have increased accessibility, affordability, and convenience of financial services for marginalized groups. It also discusses regulatory frameworks, public-private collaborations, and infrastructural challenges that influence the effectiveness of fintech-driven inclusion. By evaluating both the opportunities and limitations of fintech in the Indian context, the study provides insights into how technology can be leveraged to achieve broader socio-economic empowerment and reduce the financial exclusion gap
Proposed Interoperable Reliable & Scalable Iot Architecture Implementing To Education Sector
Get PDFThe Internet of Things (IoT) technology is ubiquitous, but increasingly complex and diverse. There are multiple vendors and suppliers are available, hence communication and coordination between heterogeneous devices is problematic due to lack of standardized interoperability. The systems were employed separate, proprietary protocols, not able to communicate each other smoothly. With the increased emphasis on information and communication technology in education sector there is a tremendous need for the integration of systems. Today, IoT is the biggest game-changer in the education industry. It allows cyberspace communications to happen between various objects, sensors, controllers and actuators, has revolutionized the education system entirely. An Interoperable scalable and reliable architecture based on MQTT is proposed for seamless communication on the top layer, an Application layer at the network stack. Due to heterogeneity in terms of data formats, protocol specifications and structure seamless integration is needed. In order to check the reliability, QoS, scalability and performance, interoperability testing is performed to monitor efficiency in this dynamic real-world environment.
The graphical interface supports configuring messages and provides an integration of faculty, staff, students, and parents via messaging system using MQTT protocol which offers lots of advantages to IoT ecosystem
Early Detection of Diabetic Retinopathy Using Dynamic Routing CapsNet with EfficientNet Feature Extraction
Get PDFDiabetes patients may develop diabetic retinopathy, an eye disorder that may result in blindness and vision loss. It is considered as the major cause of blindness in the world among the working-age people. It can result in blindness if it is not discovered early. Moreover, there is no cure for DR; treatment keeps the eyesight intact. Early diagnosis and treatment of DR can greatly lower the possibility of visual loss. This paper proposes a novel Dynamic Routing-CapsNet (DR-CN) algorithm by integrating Dynamic Routing algorithm and Capsule Networks (CapsNet). The Dynamic Routing algorithm is used to train the network and create relationships between the capsules. Then, the EfficientNet is used for feature extraction because of its high accuracy and scalability. Also, the Capsnet is used for employing the relationship between the features by enhancing the performance of the method to differentiate among the various stages of DR. This method was performed based on the dataset which achieves a result of 98% accuracy by using the Convolutional Neural Networks (CNN) employing classification accuracy. Moreover, CNN is very effective for the classification of the images because they can easily learn about the features from the input images. These findings demonstrate that Dynamic Routing-CapsNet (DR-CN) algorithm provides a solution for DR screening, efficiently helps in early detection, and is useful for the healthcare system by reducing its difficulty in detection. Dense U-Net demonstrated exceptional segmentation performance, achieving accuracy rates of 0.94 (training set variation) and 0.93 (K-Fold cross-validation). Additionally, DR-CN showcased outstanding diabetic retinopathy classification results with 98.6% accuracy, 94.4% sensitivity, 94.3% specificity, and 96.2% F-Measure