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Comparative Performance Evaluation of Magnetron Sputtered TiN, AlCrN, and TiAlCrN Coatings
Get PDFThis study presents a comparative performance evaluation of magnetron-sputtered TiN, AlCrN, and TiAlCrN coatings on tool steel substrates. Coatings play a pivotal role in enhancing the durability of engineering components exposed to harsh conditions. TiN is known for outstanding hardness and durability, AlCrN for superior wear resistance, and TiAlCrN for exceptional thermal stability and oxidation resistance. The substrate (EN 1.2363) underwent PVD coating using a closed-field unbalanced magnetron sputter ion plating (CFUBMSIP) process in a water-cooled stainless-steel vacuum chamber with four magnetron ports, operating for 3 hours per batch followed by 12 hours of natural convection cooling, utilizing TiN, AlCrN, and TiAlCrN films deposited for 180 minutes in an Ar and nitrogen atmosphere. X-ray diffraction (XRD) revealed distinctive crystal structures, with all coatings exhibiting a common preferred orientation of the (111) plane and TiAlCrN showing a ternary nitride phase. Scanning electron microscopy (SEM) images displayed the compact nature of TiAlCrN with finer grains, while TiN exhibited a densely compacted with no evidence of delamination and AlCrN showed denseness with smaller grains. Nano-indentation test was conducted to assess the coatings\u27 hardness and elastic modulus. TiAlCrN exhibited the highest hardness (3091±243 HV), highest elastic modulus (369.86±54.19 GPa), and the best wear rate (0.1887 x 10-4 mm3/Nm-1) suggesting potential suitability for applications demanding superior rigidity and wear resistance. The study provides valuable insights for materials scientists and engineers in optimizing coating selection for specific applications
AI-Driven IoT Healthcare System for Real-Time ECG Analysis and Comprehensive Patient Monitoring
Get PDFThis study explores the integration of deep learning and Internet of Things (IoT) technologies to enhance healthcare delivery, with a primary focus on improving electrocardiogram (ECG) analysis and real-time patient monitoring systems. The research presents the development of two innovative deep learning models based on the MIT-BIH dataset, enabling highly accurate ECG analysis. One model is trained for precise R-R peak detection, while the other performs effective classification of ECG signals into five distinct disease categories. The study also introduces an integrated healthcare system that seamlessly captures patients\u27 real-time physiological data, including ECG, SpO2, and temperature, using an ESP32 microcontroller and Raspberry Pi. An IoT infrastructure with Node-RED IBM Platform and Message Queuing Telemetry Transport (MQTT) securely transmits the ECG data to the advanced analysis algorithms. The user interface displays patients\u27 vital signs, including heart rate, oxygen saturation, and temperature, providing healthcare professionals with comprehensive real-time insights. By integrating the deep learning models, which achieve approximately 99% accuracy, alongside robust sensor technology and an IoT architecture, this system aims to transform healthcare by enabling highly precise ECG analysis and remote patient monitoring. The findings of this study underscore the potential of the synergistic convergence of deep learning, sensor technology, and IoT to advance healthcare delivery and improve patient outcomes
Optimized Dehydrogenation of Magnesium Hydride with Fe and Cu Additives
Get PDFSolid state hydrogen storage with the use of MgH2 has gained widespread attention because of its good reversible storage capacity. This metal hydride however slowly releases hydrogen at high temperatures ranging between 325 – 375 oC. An important feature required of MgH2 is its ability to commence hydrogen release at a fast rate and reduced temperature. Transition metal-containing alloys/compounds having either Fe or Cu as the principal element have been used to improve the dissociation of hydrogen from MgH2. Studies involving the use of each metal in its elemental form as an additive are minimal. This study investigated the catalytic effects of elemental Fe and Cu on the hydrogen release performance of MgH2 for potential applications in transportation and power generation. It was observed that MgH2/Fe had a better dehydrogenation performance; the temperature of onset of MgH2/Fe and MgH2/Cu dehydrogenation were 205, and 215 oC which were 98 and 88 oC lower than that for as-received MgH2. The highest amount, 1.9 wt. % H2 was released by MgH2/Fe while 1.44 wt. % H2 was released by MgH2/Cu. The activation energy for dehydrogenation was reduced from 150.5 kJ/mol for as-received MgH2 to 89.8 and 79.8 kJ/mol in MgH2/Cu and MgH2/Fe respectively. It took 18.8 min for as-received MgH2 to commence dehydrogenation while that for MgH2/Fe and MgH2/Cu composites started from 12.2 and 13.4 min respectively. The in-situ formed Fe and Cu in MgH2 after milling acted as active catalytic sites for its improved dehydrogenation with MgH2/Fe behaving better
A Computational Model of Attention-Guided Visual Learning in a High-Performance Computing Software System
Get PDFThis research investigates transformer architectures in high-performance computing (HPC) software systems for attention-guided visual learning (AGVL). The study focuses on the effects of environmental factors and non-contextual stimuli on cognitive control. It reveals how attention increases responses to attentive stimuli, thereby normalizing activity across the population. Transformer blocks use parallelism and less localized attention than current or convolutional models. The study investigates the use of transformer topologies to enhance language modeling, focusing on attention-guided learning and attention-modulated Hebbian plasticity. The model includes an all-attention layer with embedded input vectors, non-contextual vectors containing generic task-relevant information, and self-attentional and feedforward layers. The work employs relative two-dimensional positional encoding to address the challenge of encoding two-dimensional data such as photographs. The feature-similarity gain model proposes that attention multiplicatively strengthens neuronal responses based on how similar their feature tuning is to the attended input. The attention-guided learning approach rewards learning with neural attentional response gain, which the network modifies via gradient descent to achieve the projected objective outputs. The study discovered that supervised error backpropagation and the attention-modulated Hebbian rule outperformed the weight gain rule on MNIST; however, concentration differed
Complementary Machining – Machining Strategy for Surface Modification: A Review
Get PDFAll fabrication techniques utilized to manufacture metallic parts modify the surface integrity of the part. Complementary machining is a relatively recent machining strategy characterized by combining metal cutting and mechanical surface treatment. Typically, it implies that after conventional machining, the cutting insert is used reversely to modify the surface by local plastic deformation. To improve product performance, mechanical surface treatment is an additional phase in the manufacturing process chain that usually results in longer production times and higher costs. As a result, a variety of hybrid techniques have been created, such as complementary machining, which has the benefit of using conventional machine tools and their associated cutting tools. The study on complementary machining is reviewed in the article. The main focus is to assess the viability of complementary machining to modify surface integrity for enhanced properties by specifically establishing its effect on tool wear, surface roughness, microhardness, fatigue, microstructure, and residual stress state
Automated Data Quality Control System in Health and Demographic Surveillance System
Get PDFThe automated data quality management system serves as a comprehensive solution developed to enhance the precision, dependability, and uniformity of data within data-driven organizations. Such systems play an important role in eliminating the shortcomings associated with manual data quality management, which is prevalent in health and demographic surveillance systems (HDSS). The ongoing difficulty of ensuring data quality through manual processing hinders the HDSS\u27s capacity to optimize data quality effectively. To address this challenge, our study adopted design science methodologies to provide guidelines for the design and implementation of the automated data quality control system. The open source technologies (Pentaho data integration, R Studio, SQL, Windows task scheduler) were used to facilitate the automation and validation of the incoming and database resident data. The quality of data has vastly improved since the implementation of the proposed system. The findings suggest that the automated data quality control system exhibited superior performance compared to the manual methods, thereby minimizing errors and time-wasting efforts.
Recycling of Brick and Road Demolition Waste in the Production of Concrete
Get PDFConstruction and public works sites generate a significant amount of waste that is often costly to dispose of. To reduce the environmental impact and promote sustainability, recycling and recovering this waste is increasingly being recognized as a viable solution. This paper presents the findings of an experimental program investigating the feasibility of using brick and road demolition waste as concrete components. By substituting a portion of sand and cement with recycled materials, this study compares the properties of the reference concrete with concrete containing varying amounts of brick waste and road demolition debris. The obtained results demonstrate that the produced concrete with up to 40 % recycled content achieved a compressive strength exceeding 20 MPa after 28 days. This study suggests that recycled brick and road demolition waste could be a sustainable and economical substitute for conventional aggregates. Incorporating these materials into concrete reduces the cement content while maintaining or even improving the fresh and hardened properties of the concrete. However, it is crucial to limit the use of road demolition sand to 10 %, crushed brick fines to 20%, and brick sand (CBS) to 30% to ensure optimal performance
Characteristics and Efficiency of Heat Transfer for Natural Convection by Ag-CuO/H2O Hybrid Nanofluid Inside a Square Cavity with Corrugated Walls
Get PDFA parametric numerical study is conducted on laminar natural convection and heat transfer in a cavity with opposing undulated walls saturated with a hybrid Ag-CuO/water nanofluid. The two vertical walls of the sinusoidally undulated cavity are maintained at hot and cold temperatures, while the upper and lower walls are thermally insulated. The investigation examines the effects of relevant parameters such as the sinusoidally undulated geometry of the walls for different volumetric fractions of nanoparticles (0% ≤φ≤6%) and Rayleigh numbers (103 ≤ Ra ≤ 106) Thefinite-volumee discretization method is employed to solve the system of governing equations. The results indicate that an increase in the volumetric fraction of nanoparticles enhances the heat exchange rate in the cavity. Additionally, the Rayleigh number, with a significant increase in surface area, strongly influences the dominant heat transfer mode in the cavity. Furthermore, an increase in the number of wall undulations leads to a reduction in the heat transfer rate
Utilizing Support Vector Machines to Detect Hate Speech on Social Media
Get PDFThis paper presents a novel application of Support Vector Machines (SVM) in developing automated systems to detect hate speech on social media platforms, addressing a critical need for scalable solutions to enhance online safety and societal cohesion. By leveraging SVM\u27s proven efficacy in managing high-dimensional data and optimizing the balance between precision and recall, the study offers a comprehensive methodology that includes data collection, preprocessing, model training, deployment, and evaluation. The results demonstrate robust average performance across key metrics, affirming the model\u27s reliability in accurately identifying hate speech while minimizing false positives. This research advances the field by showcasing the practical and theoretical contributions of SVM in automated hate speech detection, highlighting its potential to significantly improve content moderation practices. The findings underscore the necessity for ongoing refinement of detection systems and collaborative efforts among researchers, technology firms, and policymakers to create more inclusive online environments that promote respectful discourse and community well-being
An Ultra High Frequency Radio Frequency Identification Compatible Circular Polarized Microstrip Antenna Array
Get PDFThis study focuses on the development, simulation, and practical validation of a metal-only microstrip patch antenna and its array designed to increase gain. The antenna is specifically designed to meet the growing demand for reliable and efficient RFID systems in various fields, such as asset tracking, inventory management, and smart logistics. Our design utilizes a truncated patch with an air substrate to achieve high gain and circular polarization. The antenna\u27s dimensions measure 468 × 188 × 31 mm³ and deliver impressive performance metrics, boasting a return loss |S11| of -18.21 dB and an estimated gain exceeding 10.6 dBi. These figures compare favorably with the simulated results, which indicate an |S11| of -20.8 dB and a total gain of 11.2 dBi. Our microstrip antenna array demonstrated consistent Circular Polarization quality throughout the radiation angle, with an Axial Ratio of less than 3 dB. This antenna has emerged as a compelling solution for UHF-band RFID technology to meet the demands of various real-world applications