Authors: Shani Singh

Abstract: Injection molding remains the dominant process for high-volume plastic production, but its strong sensitivity to process parameters, material behaviour, and cooling efficiency makes traditional trial-and-error optimization costly and slow. Moldex3D, a dedicated 3D CAE package for polymer processing, enables detailed simulation of filling, packing, cooling, and warpage, allowing defects such as short shot, weld lines, air traps, sink marks, voids, and deformation to be predicted before tool manufacture. By combining non-Newtonian flow models, temperature-dependent material data, and realistic mold and cooling layouts, Moldex3D helps designers and process engineers optimize gate locations, runner balance, packing profiles, and conventional or conformal cooling channel designs. This review consolidates recent Moldex3D-based research across automotive, consumer, electronic, and medical applications, with emphasis on flow and shrinkage analysis, warpage prediction in fibre-reinforced parts, and use in multi-cavity and thin-walled molds. Advanced and hybrid workflows are also examined, including integration with CAD/CAE platforms, topology and DOE-based optimization, and transfer of fibre orientation and residual stress fields into structural FEA. Key limitations are identified in material modeling (PVT, viscosity, fibre orientation), mesh and computation cost for full 3D models, and incomplete coupling to structural durability analysis and real machine behaviour. Finally, the review highlights future opportunities for AI-assisted optimization, cloud-based simulation, and digital twin integration, positioning Moldex3D as a core enabler of simulation-driven, intelligent injection molding.

DOI: https://doi.org/10.5281/zenodo.18494341

 

Published by: vikaspatanker

Authors: A. Priyadharsini

Abstract: The rapid adoption of artificial intelligence in the healthcare sector has led to an increased demand for high-quality medical datasets. However, the sensitive nature of patient information and the strict regulatory requirements surrounding healthcare data often restrict institutions from sharing data with external entities. Federated Medical Learning (FedMed) presents a promising solution by enabling multiple healthcare institutions to collaboratively train deep learning models without exposing raw patient data. This paper proposes a robust FedMed framework that integrates federated averaging, secure aggregation, and advanced privacy-preserving techniques to ensure confidentiality while maintaining high model performance. Experiments conducted using medical imaging datasets demonstrate that the FedMed model achieves accuracy levels comparable to centralized deep learning approaches, while significantly reducing privacy risks. The findings highlight the potential of FedMed to enable scalable, secure, and efficient AI-driven healthcare applications across diverse medical environments.

Published by: vikaspatanker

Authors: Hachimenum Nyebuchi Amadi, Biobele A. Wokoma, Barineka Richard Zarakpege, Richeal Chinaeche Ijeoma

Abstract: Transformer differential protection can experience false trips and mis-coordination, especially during external feeder faults and magnetizing inrush conditions. These malfunctions can compromise supply continuity, reduce system reliability, and put unnecessary stress on critical substation equipment. This research examines the reliability of the existing differential protection scheme at Corner Stone Substation and develops an enhanced adaptive configuration aimed at mitigating false operations while ensuring secure and selective fault clearance. To establish a performance baseline, historical relay event records from 2024 to 2025 were analyzed. A detailed MATLAB/Simulink model of the 15MVA, 33/11kV transformer protection system was created. The baseline protection scheme was tested under internal faults, external feeder faults, and transformer energization conditions. Subsequently, an improved protection configuration that integrates adaptive directional logic was implemented and validated through comparative simulations. The study found that the existing differential protection at Corner Stone Substation was reliable during internal faults, operating within 100 to 120 milliseconds. However, it was prone to false tripping during transformer energization, which produced an inrush current of approximately 6000 A with significant second harmonic distortion. Additionally, mis-coordination occurred during external feeder faults exceeding 7kA, with trip times ranging from 60 to 100 milliseconds. By integrating adaptive directional logic, the new scheme achieved secure restraint during external faults while maintaining rapid isolation of internal faults in less than 120 milliseconds. MATLAB simulations confirmed that the improved configuration enhanced selectivity, minimized false operations, and ensured reliable coordination between transformer and feeder protections. The findings indicate that adaptive directional differential protection improves selectivity, reduces false operations, and ensures robust coordination between transformer and feeder protections. This advancement contributes to enhancing protection strategies for modern substations and has potential applications for mitigating relay misoperations in other high-voltage grid systems.

DOI: https://doi.org/10.5281/zenodo.18493328

Published by: vikaspatanker

Authors: Olamilekan Emmanuel Solomon, Hachimenum Nyebuchi Amadi, Barididum P. Biragbara, Richeal Chinaeche Ijeoma

Abstract: This study aims to analyze the impact of integrating a 100MW solar photovoltaic (PV) system into the Port Harcourt 132kV transmission network, specifically to assess its effects on grid performance and stability. The increasing incorporation of renewable energy, especially solar PV, presents operational challenges such as voltage fluctuations, reactive power imbalances, harmonic distortion, and frequency instability. If left unmanaged, these issues can lead to transformer overloading, grid congestion, and increased system losses. To address these challenges, we conducted load flow, voltage stability, and harmonic analyses using the Electrical Transient Analysis Program (ETAP) to model the existing network and evaluate the integration of the 100 MW solar PV systems alongside a battery energy storage system (BESS). The simulation results indicated that prior to integration, critical buses (T1A = 89%, T2A = 89.1%, T3A = 90.8%) and transformers (T1A = 112.8%, T2A = 111.8%, T3A = 91.7%) were operating beyond acceptable limits. After integration, the bus voltages improved to T1A = 96.06%, T2A = 96.11%, and T3A = 97.36%. Additionally, transformer loading decreased to T1A = 71%, T2A = 70%, and T3A = 46.8%, while total network losses significantly reduced from 6086.8 kW + j32740.7 kvar to 1093.172 kW + j9392.581 kvar. These findings demonstrate that the coordinated integration of solar PV and BESS can enhance voltage stability, reduce system losses, and minimize transformer stress. The study recommends supportive policy frameworks to encourage large-scale solar PV integration with energy storage, representing a sustainable approach to improving grid reliability and advancing Nigeria’s transition to renewable energy.

DOI: https://doi.org/10.5281/zenodo.18493122

 

Published by: vikaspatanker

Authors: Prachi Kishan Varu

Abstract: The aviation industry is among the most energy-intensive, producing about 2.5% of total CO₂ emissions worldwide. As demand for air travel continues to grow and airports as major energy consumers and infrastructure hubs continue to develop, the role of green airports is increasingly evident. Green airports are transforming aviation infrastructure through renewable energy systems, sustainable technology, and intelligent and rational waste management systems. This paper investigates the innovations in sustainability that have the potential to contemporize airport operations, including the application of renewable energy deployment systems, smart waste systems, and digital technologies that reduce environmental footprint. This paper employs global and India examples to highlight best practices, policy enablers, and challenges as traditional airports transition to sustainable airports that are ready for the future.

DOI: https://doi.org/10.5281/zenodo.18492944

Published by: vikaspatanker

Authors: Sachin Kumar

Abstract: Smart Tourist Safety refers to the integration of digital technologies such as the Internet of Things (IoT), mobile applications, artificial intelligence, and real-time data analytics to enhance the safety and security of tourists. With the rapid growth of global tourism, ensuring tourist safety has become a critical concern for destinations and governments. Smart safety systems enable real-time monitoring, emergency response, location tracking, and risk prediction, helping tourists navigate unfamiliar environments securely. This study explores the concept of Smart Tourist Safety, examines key technologies involved, and discusses their role in improving emergency management, crime prevention, and overall tourist confidence. The findings highlight that smart safety solutions not only reduce risks but also enhance destination attractiveness and sustainability

DOI: https://doi.org/10.5281/zenodo.18492762

Published by: vikaspatanker

Authors: Akhilash Pennam

Abstract: This paper proposes a cloud-based CRM solution developed on the Salesforce platform to modernize gas station operations by automating fuel booking, inventory management, supplier coordination, and customer interactions. The system uses custom objects, role-based security, low-code automation through Flows, and Apex triggers to enforce business rules and reduce manual effort. Real-time dashboards and analytical reports provide insights into fuel consumption, inventory status, and revenue trends. Testing and validation results indicate improved operational efficiency, data accuracy, and service responsiveness, confirming that the proposed CRM solution is scalable, secure, and suitable for multi-branch deployment and future enhancements.

DOI: https://doi.org/10.5281/zenodo.18477450

Published by: vikaspatanker

Authors: Sanjith Rajesh, Prof Ankit Shrimankar

Abstract: Artificial Intelligence (AI) is quickly changing aerospace, fields traditionally shaped by human creativity and engineering skill. AI helps optimize rocket trajectories and allows for autonomous spacecraft navigation. It has become a crucial part of modern exploration. Its capacity to handle large amounts of data in real time enables engineers to foresee mechanical failures before they happen. It also helps design more efficient propulsion systems and simulate complex missions to distant planets. It can also pre-calculate whether our expectations from an aircraft are met as per design conjectures. As humanity aims to colonize Mars and expand the limits of space travel, AI serves as both a driving force and a protector. It is transforming how we build, launch, and maintain the machines that help us to circumnavigate and go beyond the Earth. The objective of this hybrid review is to find and abstractly define AI’s use in aviation. analyze faults that can occur due to its use from real published fault reports and extrapolate its use in Aeronautics and in some cases Astronautics. All inferences are concluded based on exhaustive review of research by reports published by credible government recognized sources on events occurring from the date of induction of AI in the field of aerospace. Multiple angles were viewed mostly from the consumer, the manufacturer and regular civilians.

Published by: vikaspatanker

Authors: Ms. V. Dhanalakshmi, Sanjuga S K, Sindulaxme J, Soundarya M

Abstract: Carbon dioxide (CO₂) emissions from industrial and organizational operations such as energy consumption, transportation, and operational processes significantly impact environmental sustainability. Accurate carbon footprint prediction is essential for reliable emission analysis and informed reduction planning. However, traditional systems rely on static calculation methods, which fail to capture dynamic operational patterns and complex emission relationships. The proposed system employs a machine learning–based framework to predict carbon footprint in industrial and organizational environments. Activity-based operational data such as electricity consumption, fuel usage, and transportation parameters are first subjected to data preprocessing and feature engineering. The processed data are then utilized in ensemble regression modeling to generate reliable emission predictions. The system predicts total carbon emissions and provides category-wise emission analysis to identify major emission-contributing activities. The proposed solution enables data-driven decision-making for sustainable operational planning and emission reduction, fostering environmentally responsible practices through analytical assessment of carbon emissions.

DOI: https://doi.org/10.5281/zenodo.18468154

 

Published by: vikaspatanker

Authors: Sathya Seelan J, Dharshini S

Abstract: Cloud computing has become a foundational technology for modern organizations, enabling scalable, flexible, and cost-efficient access to computing resources through the internet. Enterprises across sectors increasingly rely on cloud services for data storage, application deployment, business operations, and critical decision-making processes. The flexibility offered by cloud computing allows organizations to dynamically scale resources, reduce operational costs, and rapidly deploy innovative applications. Despite these significant advantages, the widespread adoption of cloud computing has introduced complex cybersecurity challenges that threaten data confidentiality, integrity, and availability, creating an urgent need for robust security frameworks. The shared and distributed nature of cloud environments, coupled with multi-tenancy, virtualization, and third-party service management, expands the attack surface and exposes systems to a variety of sophisticated cyber threats. These threats are further amplified by rapid technological advancements, including the integration of Internet of Things (IoT) devices, edge computing, and artificial intelligence (AI) applications in cloud platforms, which increase connectivity but also add layers of vulnerability. Malicious actors exploit misconfigurations, weak authentication mechanisms, and software vulnerabilities to gain unauthorized access, steal sensitive information, or disrupt services, highlighting the importance of proactive security measures. This research paper provides a comprehensive analysis of major cybersecurity threats associated with cloud computing and evaluates existing and emerging security mechanisms employed to mitigate these risks. Key threats discussed include data breaches, account hijacking, insecure application programming interfaces (APIs), insider threats, denial-of-service (DoS) attacks, ransomware, and compliance-related vulnerabilities. Data breaches remain one of the most critical concerns, as attackers can access sensitive information stored in cloud systems through technical exploits, human errors, or inadequate security policies. Account hijacking, often achieved through phishing attacks, malware injection, or credential theft, allows attackers to manipulate cloud resources, disrupt services, or launch further attacks within an organization’s network. Insecure APIs, which serve as communication gateways between applications and cloud services, pose substantial risks if improperly designed or inadequately secured, enabling unauthorized access, data manipulation, or denial-of-service attacks. Insider threats, whether intentional or accidental, continue to be a persistent challenge due to the trusted access employees or contractors have to cloud resources. The paper also explores the shared responsibility model in cloud computing security, which delineates the division of security obligations between cloud service providers and cloud users. While providers are tasked with securing the underlying infrastructure, including physical hardware, virtualization layers, and platform services, users are responsible for securing data, applications, access credentials, and configurations. Misunderstanding or neglecting these responsibilities can result in security gaps, misconfigurations, and increased exposure to cyberattacks. To address these challenges, the study analyzes a range of mitigation strategies, including advanced encryption techniques for data at rest and in transit, identity and access management (IAM) solutions, multi-factor authentication, continuous monitoring, intrusion detection and prevention systems, and compliance with international security standards such as ISO/IEC 27001, NIST frameworks, and GDPR.

Published by: vikaspatanker