Critical Applications Research Articles

Modeling, Control and Diagnosis of Electrical Machines and Devices

Nowadays, the increasing use of electrical machines and devices in more critical applications has driven the research in condition monitoring and fault tolerance [...]

SEDONUT: A Single Event Double Node Upset Tolerant SRAM for Terrestrial Applications

Radiation and its effect on neighboring nodes are critical not only for space applications but also for terrestrial applications at modern lower-technology nodes. This may cause static random-access memory (SRAM) failures due to single- and multi-node upset. Hence, this article proposes a 14T radiation-hardened-based SRAM cell to overcome soft errors for space and critical terrestrial applications. Simulation results show that the proposed cell can be resilient to any single event upset and single event double node upset at its storage nodes. This cell uses less power than others. The hold, read, and write stability increases compared with most considered cells. The higher critical charge of the proposed SRAM increases radiation resistance. Simulation results demonstrate that out of all compared SRAMs, only DNUSRM and the proposed SRAM show 0% probability of logical flipping. Also, other parameters such as total critical charge, write stability, read stability, hold stability, area, power, sensitive area, write speed, and read speed of the proposed SRAM are improved by –19.1%, 5.22%, 25.7%, –5.46%, 22.5%, 50.6%, 60.0%, 17.91%, and 0.74% compared with DNUSRM SRAM. Hence, the better balance among the parameters makes the proposed cell more suitable for space and critical terrestrial applications. Finally, the post-layout and Monte Carlo simulation validate the efficiency of SRAMs.

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

Attentional adversarial training for few-shot medical image segmentation without annotations.

Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset.

One-step synthesis and characterization of Y2O3 nanoparticles via emulsion detonation method

Yttrium oxide (Y2O3) nanoparticles have critical applications in many industries, including ceramics, optics, and biomedicine. However, conventional synthesis methods are typically cumbersome and time-consuming. Herein, a novel emulsion detonation method successfully achieved the one-step synthesis of Y2O3 nanoparticles. In this method, yttrium nitrate hexahydrate was uniformly dispersed in the emulsion explosive as a precursor and an auxiliary oxidizer. After the emulsion explosive was detonated in a detonation chamber, the detonation products were collected, purified and characterized by X-ray diffraction, Fourier transform infrared spectrometer, Raman spectrometer, X-ray photoelectron spectroscopy, field emission scanning electron microscopy, field emission transmission electron microscopy, and ultraviolet spectrophotometer. The results show that the as-synthesized powders are high-purity cubic phase yttrium oxide nanoparticles with a spherical-like morphology and an average particle size of approximately 30 nm. These Y2O3 nanoparticles have a wide (5.53 eV) energy band gap, close to the conventional methods. Based on detonation theory, thermochemical theory, and characterization results, this text discussed the mechanism of the Y2O3 nanoparticles synthesized by the emulsion detonation method. During detonation, the formation of the Y2O3 nanoparticles underwent three stages: the formation of the Y2O3 molecules by the collision of Y and O ions, the formation of the crystal nucleus at supersaturation concentration, and crystal growth. This study provides a novel, cost-effective method for efficiently synthesizing Y2O3 nanoparticles.

Enhanced authentication and device integrity protection for GDOI using blockchain

AbstractRecent device‐level cyber‐attacks have targeted IoT critical applications in power distribution systems integrated with the Internet communications infrastructure. These systems utilize group domain of interpretation (GDOI) as designated by International Electrotechnical Commission (IEC) power utility standards IEC 61850 and IEC 62351. However, GDOI cannot protect against novel threats, such as IoT device‐level attacks that can modify device firmware and configuration files to create command and control malicious communication. As a consequence, the attacks can compromise substations with potentially catastrophic consequences. With this in mind, this article proposes a permissioned/private blockchain‐based authentication framework that provides a solution to current security threats such as the IoT device‐level attacks. Our work improves the GDOI protocol applied in critical IoT applications by achieving decentralized and distributed device authentication. The security of our proposal is demonstrated against known attacks as well as through formal mechanisms via the joint use of the AVISPA and SPAN tools. The proposed approach adds negligible authentication latency, thus ensuring appropriate scalability as the number of nodes increases.

Teaching Immunotherapy at College of Pharmacy-Therapy-based Learning

Abstract Different types of immunotherapies employed as chemotherapy work in different ways. Some immunotherapy treatments help the immune system stop or slow the growth of cancer cells. Immunology course for Pharmacy year 2 students is taught at College of Pharmacy Chicago State University. The perquisites for the course were Medical Physiology, Biochemistry and Pathophysiology. The students had no prior knowledge of basic immunology. The way the course was designed was that they learn about the current available immunotherapy and then were taken back to the basic concepts of immunology to make sense of that module. We divided Pharmacy Immunology course into five modules, i.e., 1) Monoclonal antibodies and immune checkpoint inhibitors, 2) Non-specific immunotherapies, 3) Oncolytic virus therapy, 4) T-cell therapy, 5) Cancer vaccines. Although the students were taught immunotherapies in two-hour long workshops as discussion in teams, they were also tested for their conceptual knowledge of basic immunology. The assessment results consistently showed a higher level of learning according to Bloom’s taxonomy. The exams were designed to test critical thinking and application and synthesis of knowledge of the students. The assessment data suggested that student performance was significantly improved as compared to previous years of Pharmacy year 2 students who were taught similar content without using this teaching methodology.

Effects of hot extrusion texture on anisotropy in microstructure and mechanical properties of spray formed Al–Zn–Mg–Cu 7055 aluminum alloy

The Al–Zn–Mg–Cu 7055 aluminum alloy was meticulously fabricated through spray forming (SF) technology, followed by hot extrusion and T76 treatment. This comprehensive process allowed for a thorough investigation of its microstructural characteristics, mechanical properties, and fatigue behavior. Post-treatment analysis revealed that the SF-7055 aluminum alloy exhibited no notable segregation or porosity defects, ensuring its exceptional quality and reliability. The microstructural analysis revealed a uniform distribution of refined grains and dispersed precipitates, contributing to the material's strength. Mechanical testing demonstrated that the hot extruded SF-7055 aluminum alloy exhibits superior tensile strength and elongation, with significant anisotropy in mechanical properties favoring the extrusion direction (ED) over the transverse direction (TD). Fatigue testing under varying cyclic stresses revealed an extended fatigue life in the ED-oriented specimens, attributed to the texture-induced resistance to crack initiation and propagation. The study underscores the effectiveness of SF and hot extrusion in enhancing the SF-7055 aluminum alloy's performance, making it a viable candidate for critical aerospace applications. The findings provide valuable insights into the relationship between processing, microstructure, and properties, offering a pathway to the design and fabrication of high-performance aluminum alloys.

Experimental investigation of electrochemical and mechanical properties of stainless steel 309L at different built orientation by cold metal transfer assisted wire arc additive manufacturing

In this article, a multi layered wall of stainless steel 309 L (ER309L) material was WAAM printed over a substrate, at optimized parameters using gas metal arc welding as heat input source assisted by cold metal transfer machine, aiming to evaluate mechanical and electrochemical properties at different built orientations i.e., 0°, 30°, 45°, 60°, and 90°. Wall samples were cut-out by wire cut electric discharge machine to compare their properties. Microstructural properties and phases were confirmed by Scanning Electron Microscopy, Energy-Dispersive Spectrometry (EDS) and X-ray diffractometry (XRD), as they change on melting and solidification along the building direction. Columnar dendrite and vertical ferritic grains were observed through an optical microscope. SEM images suggests the presence of Widmanstatten austenite laths forming δ/γ duplex phase. Element distribution by EDS was further verified by Schaeffler’s diagram. XRD pattern confirms various elements and their phase compositions. The tensile strength and yield strength of samples decreased with increase in orientation angle, which is attributed to decrease in the inter-layer length which restricts the slipping of grains and reestablishment of the residual forces., tableau software was used to theoretically predict values for 45° sample as it broke outside the gauge length. electrochemical properties and corrosion behaviour were studied by electrochemical impedance spectroscopy (EIS) and Potentiodynamic polarization (PDP) where in highest Rct=170 Ω and lowest corrosion rate, 0.56 mils per year (MPY) was observed for 45° is attributed to diffusion of chromium to the metal-solution interface forming a passive film. The corrosion rate range 0.48–2.7 MPY, advocates SS309L have a uniform passive film. The properties studied certifies that WAAM printed stainless steel component can be exploited for critical applications.

An Improved Binary Dragonfly Fault-Tolerant Sensor Scheduling Based Approach for Target Tracking in Wireless Sensor Networks

Abstract: Target tracking in wireless sensor networks (WSNs) is a critical application, often relying on node scheduling based on trajectory prediction. However, traditional approaches may lead to target loss due to prediction errors, requiring resourceintensive target recovery mechanisms and potentially resulting in data loss. To address this challenge, we propose an improved binary dragonfly fault-tolerant sensor scheduling (BD-FTSS) approach aimed at enhancing target tracking performance in WSNs. Our approach focuses on minimizing target loss probability while reducing energy consumption through a low-power scheduling mechanism. The BD-FTSS approach begins by designing a fault-tolerant domain to expand the scheduling range of candidate nodes. By considering factors such as remaining energy, sensing coverage, and overlapping coverage, we aim to activate the minimum number of sensors necessary to cover the fault-tolerant domain effectively.

Microstructural and mechanical analysis of heat-treated Zn-Al alloy and Zn-Al/ZrB2 composites

This study explores the impact of heat treatment on Zn-Al alloy and Zn-Al/ZrB2 composites. Specifically, heat treatment at 250 °C for 3 h resulted in a significant refinement of grain structure and notable hardness improvement, showcasing an optimal combination of microstructural and mechanical properties. However, higher temperatures led to increased grain size and reduced hardness for both alloy and composites. These findings offer promising insights for optimizing processing parameters, enhancing the performance of Zn-Al based composites in critical engineering applications.

Medical Supplies Delivery Autonomous Drone with Security

Abstract: Drones and UAVs have gained a lot of attention in the recent years. Being fast and efficient, they have the capability to carry out a multitude of tasks efficiently and quickly. One of the critical applications of drones is in the field of healthcare is the delivery of medical supplies. In case of severe natural calamities, only a drone is suitable for providing the basic essential supplies that can save the lives of constrained survivors. In rural and underdeveloped areas, a drone is ideal for delivery. The usage of a medical drone is not restricted as it can easily be used in day-to-day urban and sub-urban areas. It is an efficient method to deliver medicines and aid to patients and those in need.

TRAFFIC LIGHT DETECTION WITH YOLO MODELS

The accurate detection and recognition of traffic lights are paramount in the realm of autonomous driving systems and intelligent traffic management. This study leverages the comprehensive cinTA_v2 Image Dataset on Robotflow, specifically designed for traffic light detection, to evaluate the performance of advanced You Only Look Once (YOLO) models, including YOLOv7l, YOLOv8n, YOLOv8s, and YOLOv8m. Through meticulous training and evaluation, we systematically analyze the models' ability to accurately detect and classify traffic light states (green, red, and yellow) under a variety of challenging conditions. Our findings reveal significant improvements in precision, recall, and mean Average Precision (mAP) across the models, with YOLOv8m demonstrating superior overall performance, especially in terms of mAP50-95, reflecting its enhanced capability in detecting small and partially obscured traffic lights. The study not only showcases the effectiveness of YOLO models in a critical application within the autonomous driving domain but also highlights the potential for further advancements in traffic light detection technologies. By discussing the challenges, limitations, and future directions, this work contributes to the ongoing efforts to improve road safety and efficiency through the application of cutting-edge artificial intelligence techniques.

Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer.

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

Early-Stage Ice Detection Utilizing High-Order Ultrasonic Guided Waves.

Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing the aerodynamic properties of these structures. While various approaches have been proposed to address icing effects, including chemical solutions, pneumatic systems, and heating systems, these solutions are often costly and limited in scope. To enhance the cost-effectiveness of ice protection systems, reliable information about current icing conditions, particularly in the early stages, is crucial. Ultrasonic guided waves offer a promising solution for ice detection, enabling integration into critical structures and providing coverage over larger areas. However, existing techniques primarily focus on detecting thick ice layers, leaving a gap in early-stage detection. This paper proposes an approach based on high-order symmetric modes to detect thin ice formation with thicknesses up to a few hundred microns. The method involves measuring the group velocity of the S1 mode at different temperatures and correlating velocity changes with ice layer formation. Experimental verification of the proposed approach was conducted using a novel group velocity dispersion curve reconstruction method, allowing for the tracking of propagating modes in the structure. Copper samples without and with special superhydrophobic multiscale coatings designed to prevent ice formation were employed for the experiments. The results demonstrated successful detection of ice formation and enabled differentiation between the coated and uncoated cases. Therefore, the proposed approach can be effectively used for early-stage monitoring of ice growth and evaluating the performance of anti-icing coatings, offering promising advancements in ice detection and prevention for critical applications.

SENTINEL GUARD: A FATIGUE DETECTION SYSTEM WITH MULTI-CHANNEL ALERT INTEGRATION

The fatigue detection system is a critical application aimed at enhancing safety in various domains by addressing the risks associated with drowsiness related incidents. Leveraging advanced technologies such as machine learning, computer vision, and real-time communication, the system offers a comprehensive solution for monitoring and alerting users to signs of fatigue in real-time. Through the integration of pre-trained models, such as the shape_predictor_68_face_landmarks.dat, the system accurately detects facial landmarks and tracks eye movements, enabling the identification of drowsiness indicators. Upon detection, the system generates alerts via email and WhatsApp, accompanied by audible alarm buzzers to prompt immediate user intervention. The inclusion of live location information in alerts provides additional context, facilitating timely assistance and support. Furthermore, the system's architecture allows for future enhancements, including the integration of multi-sensor data fusion techniques and collaboration with industry stakeholders to promote standardized protocols and widespread adoption. Overall, the fatigue detection system represents a significant step towards mitigating the risks of drowsinessrelated accidents and improving safety across various applications Key Words: Fatigue detection, Machine learning, CNN, Real time alerts, Facial Expression analysis, Open CV

Kink-mediated high strength and large ductility via nanocrystallization in a tough titanium alloy

Titanium (Ti) alloys, which are highly promising as structural materials in critical industrial applications, generally require high strength and ductility, particularly high fracture toughness. However, the conventional approach of enhancing strength through mechanical processing to induce dislocations often leads to a compromised ductility known as the strength-ductility trade-off. Here, we develop a new strategy of the nanocrystallized kinks to overcome this issue in Ti-11V metastable β-Ti alloys via combining industrially used cold forging (CF) and cold rolling (CR) processes. Deformation kinks are firstly activated by CF, and subsequently they are fragmented into nanograins during CR, architecting nanocrystallized kinks in the coarse-grained matrix. This unique microstructure effectively balances the strength-ductility conflict, endowing this Ti-V binary alloy with high yield strength ∼1200 MPa, appreciable ductility ∼17 % and high fracture toughness ∼ 52.0 MPa·m½, which is superior to numerous multielement engineering Ti alloys. This design strategy of nanocrystallized kinks can be extended to other engineering materials, e.g., Mg and Zr alloys, for advanced performance at large industrial scales.

Quasi-Isotropy Structure and Characteristics of the Ultrasonic-Assisted WAAM High-Toughness Al Alloy

Wire Arc Additive Manufacturing (WAAM) has emerged as a highly promising method for the production of large-scale metallic structures; nonetheless, the presence of microstructural inhomogeneities, anisotropic properties, and porosity defects within WAAM Al alloys has substantially hindered their broader application. To surmount these obstacles, ultrasonic-assisted WAAM was applied in the fabrication of thin-wall structures utilizing 7075 Al alloy. This study investigates the effects of ultrasonic-assisted Wire Arc Additive Manufacturing (WAAM) on the structural and mechanical properties of 7075 Al alloy specimens. Microstructural analysis showed a significant refinement in grain distribution, with the average grain size notably reduced, enhancing the material’s homogeneity. Porosity across the specimens was quantified, showing a decrease in values from the upper (0.02151) to the middle (0.01347) and lower sections (0.01785), correlating with the rapid cooling effects of WAAM. Mechanical testing revealed that ultrasonic application contributes to a consistent hardness pattern, with measurements averaging 70.71 HV0.1 horizontally and 71.23 HV0.1 vertically, and significantly impacts tensile strength; the horizontally oriented specimen exhibited a tensile strength of 236.03 MPa, a yield strength of 90.29 MPa, and an elongation of 31.10% compared to the vertically oriented specimen which showed reduced mechanical properties due to the presence of defects such as porosity and cracks. The fracture morphology analysis confirmed a predominantly ductile fracture mode, supported by the widespread distribution of dimples on the fracture surface. The integration of ultrasonic vibrations not only refined the grain structure but also modified the secondary phase distribution, enhancing the quasi-isotropic properties of the alloy. These results underline the potential of ultrasonic-assisted WAAM in improving the performance of the 7075 Al alloy for critical applications in the aerospace and automotive industries, suggesting a promising direction for future research and technological advancement in additive manufacturing processes.

NRPerson: A Non-Registered Multi-Modal Benchmark for Tiny Person Detection and Localization

In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements.

Acoustic Emission in Ceramic Matrix Composites

Abstract The integration of ceramic matrix composites (CMCs) into safety-critical applications, such as turbine engines and aerospace structures, necessitates a sound understanding of their expected damage evolution under in-service conditions and real-time health-monitoring methods to assess their damage state. The measurement of acoustic emissions (AEs), the transient elastic waves emitted during damage formation, offers an enhanced capability for evaluating damage evolution and structural health in CMCs due to its high sensitivity, accurate temporal resolution, and relative ease of use compared to other nondestructive evaluation (NDE) techniques. Recent advances in numerical simulation methods and data-driven model development, in combination with improved multimodal experimental characterization methods and sensor hardware, are rapidly advancing AE to a mature technique for damage quantification. This review discusses the fundamental principles of acoustic emissions, provides practical guidelines on their experimental characterization and analysis, and offers perspectives on the current state-of-the-art.