Daily Archives: February 5, 2026

Authors: Shani Singh

Abstract: Additive manufacturing (AM) has evolved from a rapid prototyping technique into a key production technology for complex, high-performance components in aerospace, automotive, biomedical, and energy sectors. However, the mechanical reliability of AM parts remains highly sensitive to process parameters, thermal history, and defect formation mechanisms. Traditional empirical and physics-based models struggle to describe the nonlinear and multidimensional interactions that arise in AM processes, limiting their ability to predict strength and structural integrity. Machine learning (ML) has emerged as a powerful alternative, capable of learning complex process–property relationships directly from data and providing accurate predictions for tensile strength, porosity, surface roughness, hardness, and dimensional accuracy. This review synthesizes recent advances in ML applications across polymer-, metal-, and ceramic-based AM technologies, focusing on process parameter analysis, mechanical strength prediction, defect monitoring, and parameter optimization. The discussion highlights commonly used ML algorithms, sensor integration strategies, and hybrid optimization approaches, and identifies key research gaps related to dataset scarcity, model generalization, interpretability, and cross-platform reproducibility. Finally, the review outlines future directions, including digital twins, physics-informed ML, and reinforcement learning, to enable autonomous, industrial-grade intelligent additive manufacturing systems.

DOI: https://doi.org/10.5281/zenodo.18494934

 

Published by: vikaspatanker

Authors: Sampang, Althea Mae A., Halik, Iederf Sean B, Labadan, David Andrew, Canada, Kevin Raymart C

Abstract: The researchers aimed to develop a multi-sensor automated system for mosquito detection and environmental surveillance, Mosquitect, having a purpose of offering support in the early assessment of dengue risks in areas prone to mosquito presence. MosquiTect is designed with the use of an Arduino UNO R4 Microcontroller that aids in tracking wingbeat frequencies, temperature, humidity, and visual-based detection of mosquitoes through a camera module. The data analysis is performed by utilizing percentages and means. During the 14-day testing, the findings show that MosquiTect had a 97.64% of success rate in terms of detecting wingbeat frequencies and gender identification signals; temperature and humidity provided a 100% success rate in monitoring environmental parameters; and a 77.07% success rate in terms of visual-detection of mosquitoes. The result shows that MosquiTect holds high relevance in the facilitation of preventive steps against dengue, especially in tropical areas. MosquiTect also possesses strong practicality for aiding governmental departments in forming preventive measures for dengue. This cultivates improvements in the capability of optical detection and image recognition, energy efficiency, environmental surveillance, and predictive modelling for the population of mosquitoes and their potential dengue outbreaks by the public health agencies.

DOI: https://doi.org/10.5281/zenodo.18494907

Published by: vikaspatanker

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