Future Systems Research Articles

Relay Protection Device Reliability Assessment Through Radiation, Fault Injection and Fault Tree Analysis.

Relay protection devices must operate continuously throughout the year without anomalies. With the integration of advanced technology and process chips in secondary equipment, new risks need to be addressed to ensure the reliability of these relay protection devices. One such risk is the impact of α-particles inducing single event effects (SEEs) on the secondary equipment. To date, there has been limited assessment of the effects of α-particles on relay protection devices from a system perspective. This study evaluates the impact of SEE on relay protection devices through a Monte Carlo simulation, which is verified by α-particle radiation, fault injection, and fault tree analysis. It discusses the influence of SEEs with and without hardening measures in place. Additionally, this study examines the soft error probability when the target processor runs both general workloads and specific application workloads. The current research proposes a low-cost and effective reliability assessment method for secondary equipment considering single event effects. The findings provide new insights for the enhancement of future electric power grid systems.

Preamble Design and Noncoherent ToA Estimation for Pulse-Based Wireless Networks-on-Chip Communications in the Terahertz Band.

The growing demand for high-speed data transfer and ultralow latency in wireless networks-on-chips (WiNoC) has spurred exploration into innovative communication paradigms. Recent advancements highlight the potential of the terahertz (THz) band, a largely untapped frequency range, for enabling ultrafast tera-bit-per-second links in chip multiprocessors. However, the ultrashort duration of THz pulses, often in the femtosecond range, makes synchronization a critical challenge, as even minor timing errors can cause significant data loss. This study introduces a preamble-aided noncoherent synchronization scheme for time-of-arrival (ToA) estimation in pulse-based WiNoC communication operating in the THz band (0.02-0.8 THz). The scheme transmits the preamble, a known sequence of THz pulses, at the beginning of each symbol, allowing the energy-detection receiver to collect and analyze the energy of the preamble across multiple integrators. The integrator with maximum energy output is then used to estimate the symbol's ToA. A preamble design based on maximum pulse energy constraints is also presented. Performance evaluations demonstrate a synchronization probability exceeding 0.98 for distances under 10 mm at a signal-to-noise ratio of 20 dB, with a normalized mean squared error below 10-2. This scheme enhances synchronization reliability, supporting energy-efficient, high-performance WiNoCs for future multicore systems.

Incidence of neutrophil extracellular traps (NETs) in different membrane oxygenators: pilot in vitro experiments in commercially available coated membranes.

Neutrophil extracellular traps (NETs) were detected in blood samples and in cellular deposits of oxygenator membranes during extracorporeal membrane oxygenation (ECMO) therapy and may be responsible for thrombogenesis. The aim was to evaluate the effect of the base material of gas fiber (GF, polymethylpentene) and heat exchange (HE) membranes and different antithrombogenic coatings on isolated granulocytes from healthy volunteers under static culture conditions. Contact of granulocytes with membranes from different ECMO oxygenators (with different surface coatings) and uncoated-GFs allowed detection of adherent cells and NETotic nuclear structures (normal, swollen, ruptured) using nuclear staining. Flow cytometry was used to identify cell activation (CD11b/CD62L, oxidative burst) of non-adherent cells. Uncoated-GFs were used as a reference. Within 3h, granulocytes adhered to the same extent on all surfaces. In contrast, the ratio of normal to NETotic cells was significantly higher for uncoated-GFs (56-83%) compared to all coated GFs (34-72%) (p < 0.001) with no difference between the coatings. After material contact, non-adherent cells remained vital with unchanged oxidative burst function and the proportion of activated cells remained low. The expression of activation markers was independent of the origin of the GF material. In conclusion, the polymethylpentene surfaces of the GFs already induce NET formation. Antithrombogenic coatings can already reduce the proportion of NETotic nuclei. However, it cannot be ruled out that NET formation can induce thrombotic events. Therefore, new surfaces or coatings are required for future ECMO systems and long-term implantable artificial lungs.

Weather resilience of the future Swiss electricity system with very high shares of variable renewable energy sources

Weather resilience of the future Swiss electricity system with very high shares of variable renewable energy sources

License Plate Detection Methods Based on OpenCV

---Since the technology was discovered with the automobile, this advancement in computer vision detection technology has slowly become part of the intelligent traffic management system. This technology can be used to divide vehicle images into different parts and outline specific regions for further identification across many systems. It is also highly applied in intelligent traffic management and video surveillance for automobiles among other applications. This paper introduces a new state-of-the-art License Plate Detection system using the CV2 Algorithm. Existing approaches have been highly prone to substantial sensitivity of the changes in illumination, not well defined and rather complex backgrounds themselves together with the plates which deterd recognition. Generally, the proposed future system is expected to exhibit high accuracy improvement and the cost of recognition for the present system may offset the problems that the current system has. All these considerations are taken into account when designing the proposed system. The proposed system based on CV2 Algorithm is supposed to exhibit considerable efficiency and robustness in handling noisy data. In this context, we gave our working model the analytical results to prove that our proposed model is much more beyond the current system since it uses the Python programming language. KEYWORDS--- OpenCV2 Algorithm, OCR (Optical Character Recognition system), Automatic license plate recognition (ALPR), BINARIZATION, Gray threshold (GT), EDGE DETECTION, NUMBER PLATE LOCALIZATION, DESKEWING, SPYDER3.

Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection. We introduce the concept of "noise-bias cascade" to explain their interconnected nature. While current AI models handle noise well, bias remains a significant obstacle in achieving practical performance in these models. Our analysis spans the entire AI development lifecycle, from data collection to model deployment. To effectively mitigate bias, we assert the need to implement additional measures such as rigorous study design; appropriate statistical analysis; transparent reporting; and diverse research representation. Furthermore, we strongly recommend the integration of uncertainty measures during model deployment to ensure the utmost fairness and inclusivity. These comprehensive recommendations aim to minimize both bias and noise, thereby improving the performance of future medical decision support systems.

Emergency Manager Preferences for Rapidly Updating Severe Weather Warnings

Abstract Threats-in-motion (TIM) is a technological innovation that would allow NWS forecasters to expand severe thunderstorm and tornado warnings in area and/or time, instead of issuing a series of new warnings. TIM allows warnings to “move” with the storm and provide more equitable lead time for locations downstream of the initial warning. While conceptual research demonstrates this value for example cases, research in NOAA’s Hazardous Weather Testbed revealed that motion instabilities (uncertainties in storm speed or direction, time intervals between forecaster intervention, and combinations of each) could also arise with this system that potentially limit usefulness or effectiveness of the product. To understand the potential trade-offs between the current and future systems, it is important to understand how core partners, such as emergency managers (EMs), would use TIM. In this study, 21 EMs from across the country participated in a simulated severe weather activity where they examined multiple TIM scenarios and provided feedback through surveys and focus groups. The TIM scenarios included visualizations of how TIM might behave under different situations: changes in storm speed, direction, and combinations of each. The simulation revealed that although EMs were optimistic about TIM, they had concerns about warning dissemination and public response. Specifically, the EMs were concerned about how the moving warning polygon could put locations in and out of warnings multiple times over relatively short periods of time, causing confusion from public safety and alerting standpoints.

Optimal operation strategies for freight transport with electric vehicles considering wireless charging lanes

Unlike traditional recharging at fixed stations, wireless charging lanes equipped on the road network enable electric vehicles (EVs) to recharge while in motion, reducing the time required for EV recharging. Indeed, the availability of wireless charging lanes allows the urban freight transport service operators to consider adjusting EVs routing plan to do EV recharging in transit so that the total operational costs can be further reduced. Envisioning that wireless charging lanes would be practically constructed on the road network, this study aims to investigate how the optimal EV routing plan can be determined in a pickup and delivery problem in the future electrified mobility system. The problem is formulated into a mixed-integer programming model, and we propose a branch-and-price algorithm to solve it. Additionally, a large neighborhood search heuristic is incorporated within the column generation framework to effectively tackle the sub-problems. Computational experiments on test instances highlight the effectiveness of our proposed algorithm and demonstrate the potential of wireless charging lanes to reduce total operational costs.

Versatile carrier-free binary nanodrug based on metformin/epigallocatechin gallate nanoparticles: exploring its properties and potential in cancer treatment.

Epigallocatechin gallate (EGCG), an important active component extracted from green tea, has attracted much attention due to its multiple biological activities such as antioxidant, antibacterial, anti-inflammatory, and antitumor effects. Meanwhile, metformin (Met), a classic drug for the treatment of type 2 diabetes, exhibits additional benefits such as hypoglycemic, antioxidant, anti-inflammatory, and antitumor effects. However, metformin often causes gastrointestinal reactions when used alone, affecting patients' quality of life. In view of this, we proposed an innovative technique for the fabrication of a carrier-free, dual-loaded nanodrug, Met-EGCG nanoparticles (Met-EGCG NPs), via self-assembly. The method for preparing Met-EGCG NPs is simple, rapid and cost-effective. In addition, the carrier-free Met-EGCG NPs nanodrug inherits the strong antioxidant capacity, good biocompatibility and excellent aggregation-induced fluorescence effect of EGCG, and even offer significant advantages in enhancing drug solubility, stability, and bioavailability, while effectively reducing the occurrence of side effects. Moreover, this Met-EGCG NPs nanodrug exhibits a synergistic therapeutic effect of EGCG and metformin, thereby significantly enhancing overall therapeutic efficacy, and demonstrates excellent potential in anti-cancer applications. This study not only successfully prepared Met-EGCG NPs but also experimentally verified their superior performance, opening a new path for the application of EGCG in drug therapy. This carrier-free, dual-loaded drug delivery nanosystem based on Met-EGCG NPs offers potential for drug combination therapy, promising to play a more critical role in the biomedical field and providing new insights and guidance for the development of future multidrug delivery systems.

Neuromorphic computing at scale.

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficient computational systems, often for applications with size, weight and power constraints. With this research field at a critical juncture, it is crucial to chart the course for the development of future large-scale neuromorphic systems. We describe approaches for creating scalable neuromorphic architectures and identify key features. We discuss potential applications that can benefit from scaling and the main challenges that need to be addressed. Furthermore, we examine a comprehensive ecosystem necessary to sustain growth and the new opportunities that lie ahead when scaling neuromorphic systems. Our work distils ideas from several computing sub-fields, providing guidance to researchers and practitioners of neuromorphic computing who aim to push the frontier forward.

A framework for anomaly classification in Industrial Internet of Things systems

Introducing the Industrial Internet of Things (IIoT) into traditional industrial processes has marked a new era of enhanced connectivity and productivity. By integrating advanced sensors, communication technologies, and data analysis, IIoT enables real-time monitoring, proactive maintenance, and increased operational efficiency. However, this increased complexity and interconnectivity also introduce new challenges in maintaining system dependability and safety. Considering these issues, this work presents an IIoT Anomaly Classification Framework designed to detect and categorize anomalies such as failures and attacks. The research addresses the critical need for robust anomaly detection and classification in IIoT systems by providing a comprehensive and scalable solution adaptable to various industrial contexts. The framework comprises two main components: an anomaly detection model and an anomaly classification model. The anomaly detection model operates unsupervised, continuously monitoring system data to identify deviations from normal behavior patterns. At the same time, the anomaly classification model categorizes these anomalies based on historical data using machine learning algorithms. The proposed framework has been tested in a realistic IIoT environment, demonstrating its effectiveness and practicality. During the cross-validation process, a precision of 0.95, recall of 0.88, and F1-score equal to 0.91 were obtained. This research contributes significantly to IIoT, offering a valuable tool for improving industrial operations and laying the groundwork for future anomaly classification and system resilience advancements.

Simulating Neuromorphic Behavior in Memory Devices with Special Ions: Insights into Device Performance and Predictive Modeling.

The development of neuromorphic memory, which mimics the computational processes of the brain, offers exciting prospects for applications in advanced computing. This studyassesses the potential of metal-ion-containing polyvinyl acetate (PVAc) fiber as neuromorphic memory devices. To assess the synaptic plasticity and hysteresis characteristics of the manufactured devices, they underwent a thorough electrical characterization process that included currentvoltage (I-V) testing and pulse stress analysis. With long-term depression (LTD) under negative stress (-6V) and long-term potentiation (LTP) under positive stress (+6V), the results showed that PVAc + metal-ion devices display dual-direction plasticity. The plasticity zones showed good adaptability in simulating neuronal learning and forgetting mechanisms, extending up to approximately 300 seconds for LTP and 500 seconds for LTD.These results demonstrate that PVAc + metal-ion devices can be used in neuromorphic applications, especially for energyefficient adaptive memory systems in robotics, artificial neural networks, and edge computing. Although there is promise for neuromorphic applications with these devices, further work is required to overcome their sensitivity to external factors. This work opens the door for future neuromorphic systems and provides important insights into device performance.

Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum

Recent developments in conductive and dielectric multi-material additive manufacturing have generated significant interest for their potential to enhance the performance of electronic devices. Lights-out digital manufacturing, a sophisticated multi-material printing technique, facilitates the seamless sintering of silver nanoparticles and acrylate inks, enabling the creation of functional electronic devices. In this study, the LDM process is utilized to prototype intricate 3D metasurface designs. A comprehensive analysis of the properties of the printed materials confirms the practicality of this multi-material printing approach. The research examines various element distributions within multi-metal layer structures, including carbon, phosphorus, oxygen, sulfur, and fluorine, to investigate the interfaces between conductive and dielectric structures. In addition, the conductivities of silver nanoparticle inks at different curing temperatures are studied to identify optimal printing conditions. Both the conductive and dielectric inks exhibit precisely controlled particle sizes and exceptional stability, which are critical for accurate additive manufacturing. Using this sophisticated printing solution, the paper presents a broadband 3D metasurface engineered to independently manipulate two distinct orbital angular momentum states in the V-band (60–70 GHz) and W-band (79–90 GHz), suitable for high-speed wireless communications. The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. The structure of the dual-band metasurface features four distinct silver layers yet is fabricated on a single ultra-thin planar substrate, demonstrating its potential for applications in future wireless communication systems.

Stability of future low-inertia power systems with different grid-forming control schemes

Stability of future low-inertia power systems with different grid-forming control schemes

Disparities In The Outcomes In The Waitlist And After Transplantation According To Transplant Region: Implications For A Future Allocation System

Disparities In The Outcomes In The Waitlist And After Transplantation According To Transplant Region: Implications For A Future Allocation System

Flow shop manufacturing layout as low carbon approach for application in bonding wire drawing industry

Modern manufacturing environments are yet to fulfil the evergrowing competitive supply and demand due to the limitation of conventional production approaches. A low-carbon approach incorporating the change from process to product-centric manufacturing systems could conserve energy while fulfilling supply and demand requirements in a competitive market. Current study had conducted a case study in a bonding wire factory that operates on process-oriented system with job shop layout. Preliminary data collection had assessed the existing practices on the factory's shop floor, and simulation model using WITNESS software was developed using this data. A job shop to flow shop conversion framework was developed with the integration of factors such as shop floor layout, order release, and dispatch rule play crucial roles in the conversion process of the production system. Based on the application of the conversion framework encompassing six future production system models, CL-SPT model which uses flow shop as layout, CONLOAD as order release and shortest processing time as the dispatch rule is selected as the best performing and economical production system. This model enhanced the output and average flow time by 2.7% and 27.7%, respectively, in tandem with low-carbon requisites while retaining machine utilization performance equivalent to the conventional system.

The hydrogen-water collision: Assessing water and cooling demands for large-scale green hydrogen production in a warming climate

Hydrogen is expected to play a critical role in future energy systems, projected to have an annual demand of 401-660 Mt by 2050. With large-scale green hydrogen projects advancing in water-scarce regions like Australia, Chile, and the Middle East and North Africa, understanding water requirements for large-scale green hydrogen production is crucial. Meeting this future hydrogen demand will necessitate 4,010 to 6,600 GL of demineralised water annually for electrolyser feedwater if dry cooling is employed, or an additional 6,015 to 19,800 GL for cooling water per year if evaporative cooling is employed. Using International Panel of Climate Change 2050 climate projections, this work evaluated the techno-economic implications of dry vs. evaporative cooling for large-scale electrolyser facilities under anticipated higher ambient temperatures. The study quantifies water demands, costs, and potential operational constraints, showing that evaporative cooling is up to 8 times cheaper to implement than dry cooling, meaning that evaporative cooling can be oversized to accommodate increased cooling demand of high temperature events at a lower cost. Furthermore, of the nations analysed herein, Chile emerged as having the lowest cost of hydrogen, owing to the lower projected ambient temperatures and frequency of high temperature events.

Exploring Patient-Related Contextual Factors and Personal Reflections About the Managing Cancer and Living Meaningfully (CALM) Intervention for Adults With Advanced Cancer in Metropolitan and Non-Metropolitan Southern Alberta: A Mixed Methods Study.

The evidence-based Managing Cancer and Living Meaningfully (CALM) psychotherapeutic intervention was designed to address the complex needs of those with advanced cancer. Ample evidence supports the efficacy of CALM therapy; less is known about the patient-specific factors that influence initiation and continuation of CALM sessions. To gain understanding of patient-specific factors and referral routes that influence initiation and continuation of CALM. An Interpretive Description framework and concurrent triangulation mixed-methods design was used to analyse baseline patient-specific variables for prediction of engagement (number of sessions) in CALM following recruitment from cancer centres, palliative care services, and community cancer care organisations across Southern Alberta, Canada. Patient input (n=10) occurred through semi-structured interviews exploring experiences with advanced cancer, CALM referral and engagement. Among consented individuals (n=69), those directly referred by healthcare providers (HCPs) and self-referred (total n=32), engaged in more CALM sessions (M=4.97, SD=3.51) than those referred indirectly (M=3.19, SD=2.26, p<0.05), particularly younger participants (< 65years) and those with longer life expectancy (> 10months). Participants chose CALM based on experiences of distress, wanting to talk openly, and expecting benefit. Greater patient engagement in the CALM intervention following HCPs' direct referrals may be based on trust in the HCP-patient relationship, and accurately prognosticating sufficient physical well-being for participation and benefit. Future health systems research may evaluate systematic programing with offering CALM referrals following an advanced cancer diagnosis.

Graphene/F16CuPc synaptic transistor for the emulation of multiplexed neurotransmission

We demonstrate a bipolar graphene/F16CuPc synaptic transistor (GFST) with matched p-type and n-type bipolar properties, which emulates multiplexed neurotransmission of the release of two excitatory neurotransmitters in graphene and F16CuPc channels, separately. This process facilitates fast-switching plasticity by altering charge carriers in the separated channels. The complementary neural network for image recognition of Fashion-MNIST dataset was constructed using the matched relative amplitude and plasticity properties of the GFST dominated by holes or electrons to improve the weight regulation and recognition accuracy, achieving a pattern recognition accuracy of 83.23%. These results provide new insights to the construction of future neuromorphic systems.

Design strategies and applications of cyanine dyes in phototherapy.

Cyanine dyes have been widely used in phototherapy in recent years due to their excellent optical properties and diverse modifiable structures. This review provides detailed descriptions of the basic structures of various cyanines and their derivatives as well as their optical properties. It summarizes the strategies for constructing cyanine dyes for phototherapy and discusses their structure-effect relationship. Furthermore, a comprehensive classification and summary of the applications of cyanine dyes in phototherapy are presented. Importantly, this review also addresses both the advances made in this field as well as the challenges that need to be overcome. We hope that these profound insights into phototherapy using cyanine dyes will facilitate the design of future systems for clinical applications based on these compounds.