Metallurgical and Materials Engineering (E-Journal)
Not a member yet
915 research outputs found
Sort by
An Empirical Study on the Economic Impact of Cybersecurity Breaches and Computer Fraud on SMEs
Get PDFSmall and Medium Enterprises (SMEs) are indispensable for economic development; however, they are highly susceptible to cyber attacks and computer fraud. This research empirically investigates the financial implications of cyber risks to SMEs in terms of monetary losses, reputational backlash, business continuity break and remediation costs. This research adopts a mixed-methods approach, with primary data collected via structured surveys and interviews with SME owners, IT managers, and cybersecurity experts, and secondary data obtained from industry reports and case studies. Regression analysis was utilized to determine the relationship between cybersecurity incidences and business performance through descriptive and inferential statistical techniques. The results show that cyber-risk incidents bring about considerable financial losses, reduced customer confidence, regulatory penalty payments (fines) that have long-term implications for SMEs. The study concludes that there is a strong need for robust cybersecurity frameworks, better awareness programs, and regulatory interventions to mitigate risks associated with the Internet of Things (IoT). These insights can help policymakers better support the small business sector to improve protections against cyberattackers, and as this research shows, small businesses can take certain actions to minimize their own economic vulnerability
Mechanisms of Low Salinity Water Flooding for Enhanced Oil Recovery: A Comprehensive Review
Get PDFIn the recent times, low salinity water flooding (LSWF) has been observed as a promising enhanced oil recovery (EOR) technique both in sandstone and carbonate reservoirs. It involves injection of low salinity water which alters the wettability and interfacial properties of the reservoir rock and crude oil, thus leading to improved oil recovery. This paper presents an overview of the current state of knowledge on the mechanisms of LSWF, gained from both experimental studies and field applications. The results obtained from laboratory analysis and field applications of LSWF have been critically reviewed showing a mixed response in sandstone and carbonate reservoirs. However, the efficacy of LSWF is dependent on various factors, like, reservoir properties, injection water composition and operating conditions. In this paper, a review of the various mechanisms like fines mobilization, wettability alteration, pH reversal, multicomponent ion exchange (MIE), mineral dissolution, micro-dispersion formation etc., affecting the improved oil recovery with LSWF in both sandstone and carbonate reservoirs have been highlighted. This review work can be helpful in proper designing of LSWF in view of the operating parameters and its successful implementation, through a better understanding of its mechanisms depending on different reservoir properties
Investigating the Role of Ahara (Diet) in the Etiopathogenesis of Amavata: An Ayurvedic Perspective
Get PDFAyurveda, considered to be the knowledge of life, has existed for over five millennia as India's time-honored approach to holistic healing. Unlike contemporary Western medicine which aims to solely treat disease indicators, Ayurveda takes a comprehensive perspective on wellness by perceiving life as a sophisticated interplay between the physical, mental, and spiritual elements. Derived from two Sanskrit terms—Ayuh signifying life or longevity, and Veda connoting knowledge or science—Ayurveda jointly translates to the "study of lifespan". This ancient system not only deals with treating sicknesses but preventing illness altogether, promoting longevity, and cultivating vitality through obtaining wisdom about harmonizing with nature
Securing Data Privacy and Integrity in Cloud Computing Using Blockchain and Quantum Cryptography
Get PDFData privacy and integrity maintenance becomes more difficult as cloud computing expands. Such complex and advanced cyberattacks need for stronger and better defenses than traditional security measures can offer. A research paper that combines blockchain technology with quantum cryptography to improve data security in the cloud paradigm. In addition, aspects of blockchain itself – its distributed and cryptographically verified ledger – offer the possibility of transparency and immutability of the life of the data. Excited by that? Unlike this, Quantum Cryptography applies the concepts of quantum mechanical physics in the form of quantum key distribution (QKD) which theoretically allows secure communication that is resistant to computational attacks (and even those executed on quantum computers). This paper proposes a cloud hybrid security framework that combines these two pathbreaking technologies to provide an end-to-end data security mechanism in cloud computing. This framework deploys the construction of smart contracts of Blockchain to automate the enforcement of such security policies while using Quantum cryptography's unbreakable encryption to enable the secure transmission of valuable data and information. Moreover, This study also covers practical usage, performance evaluation, and integration challenges of integrating Blockchain/Quantum Cryptography with cloud infrastructures.It has been established through the comparative analysis of existing security models that this hybrid approach offers improved mitigation towards the cyber threats ranging from data breaches, unauthorized access, and man-in-the-middle attacks. The experimental results demonstrate that integrating Blockchain and Quantum Cryptography can significantly enhance cloud data privacy, integrity, and trustworthiness. With this advancement, quantum-secured cloud-computing environments will be built to guard their data with a security measure that offers a strong, future-proofed level of security and prevents quantum-based threats that may arise in the future
Enhancing YOLOv8 for Vehicle Detection in Intelligent Traffic Management
Get PDFObject detection has witnessed significant advancements with the introduction of deep learning models. In this study, we evaluated YOLOv8 model on the BDD100K dataset over 25 epochs, focusing on its progression in precision, recall, mean Average Precision (mAP), and loss metrics. The YOLOv8 model was trained and evaluated on the BDD100K dataset. Data preprocessing was done by resizing images and applying augmentation techniques like scaling, flipping, and cropping to enhance generalization. The model utilized an anchor-free detection head and an optimized backbone network for improved performance. Training was conducted over 25 epochs using the stochastic gradient descent optimizer, with non-maximum suppression and confidence thresholding applied during post-processing to refine detections. Performance was assessed using precision, recall, mAP, and loss metrics. Early epochs were marked by high classification and box losses, which reduced significantly by epoch 5. Precision and recall improved consistently, with precision increasing from 0.81 to 0.74 and recall stabilizing around 0.72–0.78. Middle epochs (6–20) showed stabilization in losses, with box loss reducing to 0.7–0.8 and classification loss to 0.4–0.5. Performance metrics peaked during this phase, achieving a precision of 0.90, recall of 0.84, and mAP@50 and mAP@50-95 values of 0.90 and 0.74, respectively. During the final epochs (21–25), the model achieved optimal stability, with box loss around 0.69 and classification loss at 0.37. Precision remained at 0.90, recall at 0.85, and mAP metrics stabilized at 0.91 and 0.74. A comparative analysis underscored YOLOv8’s superiority over YOLOv5, achieving higher F1-score (0.87 vs. 0.67), mAP@50 (0.91 vs. 0.682), and mAP@50-95 (0.74 vs. 0.449). YOLOv8’s architectural advancements like anchor-free detection head and optimized backbone improved object detection. YOLOv8’s balance of precision and recall, supported by strong post-processing, affirms its robustness for real-world applications
A Real-Time Ergonomic Coaching System for Neurosurgeons Using Virtual Reality and Machine Learning-Based Posture Analysis
Get PDFBackground: Musculoskeletal disorders continue to affect neurosurgeons with the prevalence of work-related musculoskeletal disorders exacerbated by long hours of static postures in performing delicate and precise surgeries. Ergonomics training, despite its increasing emphasis, is still not adequately integrated within the structure of neurosurgical education.
Objective: In this paper, we present to you the Automated Ergonomics Coaching System (AECS)- a virtual reality-based training platform with Artificial Intelligence-powered pose estimation that enables real-time feedback in creating ergonomic awareness during neurosurgical simulations.
Methods: Twenty-four neurosurgical residents were randomly divided into two groups, an AECS group and a control group. All participants underwent three sessions of simulation training on spine surgery. The quantification of posture deviations was carried out by the model pose and was supplemented by pre- and post-usability questionnaires to assess usability and ergonomics awareness
Results: Though not proven statistically, there was a difference in the ergonomic error rates between the two groups (p=0.278). AECS members showed steady progress from one trial to the next. They had good posture check, were more confident in their ability to correct ergonomics, and gave the system high marks for usability. A large percentage, 73.91%, said that they now have better knowledge as to the strains that come with postures.
Conclusions: AECS shows potential as a practical, feedback-rich training system that enhances ergonomic posture in surgical education. Real-time pose estimation and interactive guidance support a safer, more sustainable learning pathway for surgeons. Future work will explore long-term retention and cross-institutional validation
Promoting Women Entrepreneurs in Kerala through Influencers: An Analysis of Strategies Utilizing Instagram Influencer Marketing as a Medium
Get PDFThe evolving demographics of India have influenced the opportunities available to women entrepreneurs and their economic contributions. Social media has profoundly influenced women's entrepreneurial identities, with Instagram serving as a crucial platform for displaying products and services. Influencer marketing has emerged as a potent instrument for empowering women entrepreneurs in Kerala, enabling them to expand their audience and enhance sales. This study seeks to examine the impact of influencer collaborations on the promotion of products and services by women entrepreneurs and their efficacy in expanding market reach. Case studies and content analysis were employed to examine successful collaborations, evaluate the efficacy of social media material, and conduct comprehensive interviews with entrepreneurs to comprehend the effects of these collaborations
Advanced Concrete for 3D Printing: Material Development and Structural Integrity
Get PDFAdditive manufacturing of buildings is an emerging method of construction technology, which has potential to manufacture concrete structures. It encompasses investigation into novel concrete designs aspiring for suitability in 3D printing, with consideration given to their physical properties and the structural necessities surrounding the load-bearing requirements they must possess. Different additives, such as polymers and nanoparticles, are investigated to improve printability as well as mechanical characteristics and endurance and to overcome major issues in extrusion behavior, interlayer adhesion, and post-print curing mechanisms. Moreover, the research examines how deviations in mix design affect the mechanical properties of 3D printed concrete constructs, specifically, in terms of structural durability across varying environmental parameters. By grasping the relationship between these components, the discoveries offer further comprehension of how the plan of materials impacts the printability and long-term performance of the concrete used in 3D-printing, providing important data to direct what might happen uses of development
Parametric Comparative Analysis of Advance Concretes for Green Construction Practices
Get PDFOn another hand, in the construction field, enhancement of concrete technology has played a vital role in meeting the needs of better performance, sustainability, and durability of the infrastructure. In this article we will provide a detailed comparison of five of the most advanced concrete materials used today: Ultra High Performance Concrete (UHPC), Self-Healing Concrete, Geopolymer Concrete, High Performance Concrete (HPC) and Fiber Reinforced Concrete (FRC). These materials have unique characteristics that make them suitable for specific forms of engineering applications. Self-Healing Concrete is an experimental technique that involves the remarkable concept of healing structural integrity over a long time, while Ultra High Performance Concrete is well known for exceptional strength and accuracy. Being a low carbon footprint, eco-friendlier alternative Geopolymer Concrete uses about 50% to 90% less energy than the traditional cementitious binding matrix. Because of its high strength and durability, HPC is suitable for demanding applications, whereas FRC enhances concrete’s resistance to cracking and dynamic loads. This review, therefore, delves into the specific attributes, advantages, obstacles, and wide-ranging use cases associated with these advanced materials with the objective of informing the future direction of research and development in the concrete technology industry
A Mathematical Model on Women's Osteoporosis
Get PDFIn this paper, a mathematical model of women's osteoporosis is formulated. Three control parameters used such as diet with exercise, nutrition intake and medication. The equilibrium analysis is performed. The boundedness and positiveness of the osteoporosis models are performed. The local and global stability is studied using the R.H. criteria and Lyapunov's approach. The reproduction number of osteoporosis is calculated. The sensitivity analysis is performed for the model. Numerical simulations are performed to show the flow of the variables using MATLAB