Critical Design Aspects Research Articles

Electron Cyclotron Resonance Thruster Technology: A Pathway to Efficient Spacecraft Propulsion

Electron Cyclotron Resonance (ECR) thrusters are emerging as a promising technology for efficient spacecraft propulsion, utilizing the phenomenon of electron cyclotron resonance to generate thrust. This comprehensive review synthesizes key advancements, design strategies, and ongoing challenges within the field. ECR thrusters operate by heating electrons in a magnetized plasma using microwave energy, leading to high ionization rates and favorable thrust-to-power ratios. Unlike traditional propulsion systems, ECR thrusters offer significant advantages, including higher specific impulse and reduced fuel consumption, making them ideal for long-duration space missions. The paper delves into various critical aspects of ECR thruster design, such as antenna configurations, gas injection methods, and magnetic field optimization, highlighting how these factors influence overall performance. It also discusses the latest experimental findings and theoretical models that have addressed issues like efficiency, lifetime, and power transfer. Furthermore, the review explores future directions, emphasizing the need for advancements in materials and automated impedance matching to enhance reliability and thrust generation capabilities.Through this analysis, the paper aims to present a holistic understanding of ECR thrusters, underscoring their potential to become a competitive and sustainable option for future space exploration.

Key Factors in the Design of Urban Underground Metro Lines

Designing sustainable underground metro lines in dense urban environments is a highly challenging task that requires the collaboration of numerous stakeholders and consultants to make crucial decisions influenced by several factors. While it is impossible to address every issue influencing the decision-making process, identifying key factors and their interdependencies is essential for optimal design. This study focuses on six critical aspects of the reference design of metro systems: (1) track alignment, (2) tunneling strategy, (3) station typology, (4) operations and maintenance, (5) procurement strategy, and (6) environmental aspects. Amongst these aspects, we identify track alignment as the primary driving factor that influences the other factors. We analyze the decision between shallow and deep alignments as an engineering choice that necessitates balancing conflicting factors and constraints. Our contribution lies in mapping these factors and their dependencies, thus offering policymakers, project managers, and designers a framework to navigate the design process. Our discussion also provides guidance to public agencies in tendering for design teams more efficiently. Drawing from lessons learned by experienced design managers, this study aims to fill the gap in the literature by offering a generalist perspective on metro design.

High-Performance Processing Electronics for Cooler Temperature Sensors in Infrared Payloads

Remote Sensing Infra-Red (IR) imaging sensors operate at very low temperatures (40–100 K). Generally, these types of imaging sensors are integrated with cryo-coolers. These sensors have an inbuilt 2N2222 diode-based cooler temperature sensor (CTS). This paper presents details of low-noise, high=precision processing electronics developed for CTS. Critical design aspects, simulation and hardware results of the system are discussed in this paper. Developed electronics systems achieve ≈5mK resolution and the power dissipation recorded is < 200 mW.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

Emerging Development of Wireless Localization Technologies Aided with Reconfigurable Intelligent Surfaces: A Comprehensive Survey

Wireless localization technologies have undergone a paradigm shift with the advent of the emerging trend which is the Reconfigurable Intelligent Surfaces (RISs). This paper presents a comprehensive survey of the state-of-the-art RIS-aided localization techniques, exploring the transformative impact of intelligent surfaces on location-based services. The survey comprehensively reviews existing wireless localization methods, ranging from the localization techniques in the wireless communications generations. It addition the challenges posed by conventional localization methods are discussed, including accuracy and coverage issues. In response to these challenges, the incorporation of RIS is explored as a promising paradigm to enhance the precision and reliability of wireless localization. The core of the paper focuses on the integration of RIS into localization systems, highlighting how these RISs can be strategically deployed to enhance accuracy, mitigate multipath effects, integration in different propagation environment and solve non feasible localization problems. The survey encompasses a wide range of applications, including outdoor positioning, indoor positioning, and Internet of Things (IoT) devices, showcasing the versatility of RIS-aided localization across various scenarios. Also, critical design aspects are examined, including propagation regions, hardware requirements, deployment strategies, necessary measurements, multiplexing, and signalling systems.

Deep attention network for identifying ligand-protein binding sites

One of the critical aspects of structure-based drug design is to choose important druggable binding sites in the protein's crystallography structures. As experimental processes are costly and time-consuming, computational drug design using machine learning algorithms is recommended. Over recent years, deep learning methods have been utilized in a wide variety of research applications such as binding site prediction. In this study, a new combination of attention blocks in the 3D U-Net model based on semantic segmentation methods is used to improve localization of pocket prediction. The attention blocks are tuned to find which point and channel of features should be emphasized along spatial and channel axes. Our model's performance is evaluated through extensive experiments on several datasets from different sources, and the results are compared to the most recent deep learning-based models. The results indicate the proposed attention model (Att-UNet) can predict binding sites accurately, i.e. the overlap of the predicted pocket using the proposed method with the true binding site shows statistically significant improvement when compared to other state-of-the-art models. The attention blocks may help the model focus on the target structure by suppressing features in irrelevant regions.

Design, development, and performance of a versatile graphene epitaxy system for the growth of epitaxial graphene on SiC.

A versatile graphene epitaxy (GrapE) furnace has been designed and fabricated for the growth of epitaxial graphene (EG) on silicon carbide (SiC) in diverse growth environments ranging from high vacuum to atmospheric argon pressure. Radio-frequency induction enables heating capabilities up to 2000 °C, with controlled heating ramp rates achievable up to 200 °C/s. The details of critical design aspects and temperature characteristics of the GrapE system are discussed. The GrapE system, being automated, has enabled the growth of high-quality EG monolayers and turbostratic EG on SiC using diverse methodologies, such as confinement-controlled sublimation (CCS), open configuration, polymer-assisted CCS, and rapid thermal annealing. This showcases the versatility of the GrapE system in EG growth. Comprehensive characterizations involving atomic force microscopy, Raman spectroscopy, and low-energy electron diffraction techniques were employed to validate the quality of the produced EG.

Design and demonstration of an ultra-thin planar microscale capillary pumped loop for electronics thermal management

With new requirements for enhanced thermal management, the minimum size of modern compact electronic devices and the maximum allowable processing speeds of the underlying microprocessor chips have become fundamentally limited by Joule heating. Miniaturizing a conventional loop heat pipe (LHP) or capillary pumped loop (CPL) to enable an ultra-thin high-heat-flux thermal transport system can lead to a paradigm shift in electronics thermal management. However, the complex thermodynamics of a CPL flow loop and the altered behavior of surface tension forces and thermal transport effects at small length scales pose several challenges to the actual implementation of a planar microscale CPL. Here we report on the design aspects of a micro-CPL device fabricated on a 500μm-thick silicon wafer, and experimentally examine the process of evaporation and two-phase flow in the device. An optical study of liquid evaporation in two distinct planar evaporator topologies during device startup reveals the critical roles played by enhanced surface tension forces and increased parasitic heat flows. Critical microfluidic device design aspects that can ensure the successful startup and operation of these ultra-thin thermal management devices are thereby revealed. Device evaporation experiments reveal successful device startup under evaporative heat fluxes of 10–20 W/cm2.

Use of Lithium-ion batteries in environmentally friendly vehicles

The concern for the environment and energy has led to a significant increase in research on electric vehicles (EVs) and hybrid electric vehicles (HEVs). The main challenges that need to be addressed to popularize these advanced vehicles are related to the battery. Since the early 1990s, we have been actively promoting research and development on batteries for environmentally friendly vehicle applications. Initially, the focus was on lithium-ion batteries as a potential solution to the critical battery issue mentioned earlier. Through extensive theoretical studies and numerous experimental demonstrations, we have successfully demonstrated that the potential of lithium-ion batteries exists and can be realized. This has opened up new possibilities and values that cannot be achieved with conventional batteries. This article aims to clarify the specific performance requirements of advanced batteries for EVs or HEVs, with a particular emphasis on the critical aspects of thermal design and construction for ensuring system stability. Furthermore, it highlights the significant improvements made in the power output of lithium-ion batteries for their application in HEVs.

Post-COVID-19 approach to teaching an undergraduate laboratory class focused on experimental design and data interpretation.

To best prepare students for the real-world research environment, key skills, including experimental design, data analysis, communication of results, and critical thinking, should be key components of undergraduate science courses. Furthermore, the impact of the COVID-19 pandemic on in-person teaching has resulted in a need to develop courses that enable flexible learning. This paper details the laboratory component of a senior-level toxicology class that was developed to emphasize all these skills and allow for flexible learning. The aim of the laboratory class was for students to determine how curcumin protected against acetaminophen-induced hepatoxicity. To stimulate critical thinking, students were required to choose a maximum of four experiments from the six on offer. Before conducting an experiment, students stated a hypothesis and selected the appropriate treatment groups. Once an experiment was completed, students were given access to a complete dataset, on which they performed statistical analysis and drew conclusions. Students who were unable to attend the laboratory session in person were able to complete the required pre-lab work and access the dataset. Following each experiment, students could write a lab summary, and receive thorough feedback. The final assessment was a written manuscript of their findings as well as a chance to respond to reviewer comments. This teaching approach prioritized the critical thinking, analysis, and experimental design aspects of scientific research. Overall, this structure was well received by students and it could easily be adapted for use on other life science courses.

Development of 500[kWh] Lithium-Ion Battery Energy Storage System with 2[MVA] Power Conditioning System for Utility Interconnection

Photovoltaic or wind turbine system installed around world has been increased dramatically in the last decade. Unfortunately, these systems are uncontrollable and dependent on environmental conditions i.e., sun or wind. As a consequence, increasing connections of those systems to the grid can lead to the stability problem of the utility network or even the failure because of their uncontrollability. Battery energy storage system(BESS) can be used to control the output fluctuations of renewable sources. BESS consists of lithium-ion battery and power conditioning system(PCS). This paper gives critical aspects of design and control for a large capacity of battery system and PCS. Test results are also given as justification of design and control technology

A versatile pressure-cell design for studying ultrafast molecular-dynamics in supercritical fluids using coherent multi-pulse x-ray scattering.

Supercritical fluids (SCFs) can be found in a variety of environmental and industrial processes. They exhibit an anomalous thermodynamic behavior, which originates from their fluctuating heterogeneous micro-structure. Characterizing the dynamics of these fluids at high temperature and high pressure with nanometer spatial and picosecond temporal resolution has been very challenging. The advent of hard x-ray free electron lasers has enabled the development of novel multi-pulse ultrafast x-ray scattering techniques, such as x-ray photon correlation spectroscopy (XPCS) and x-ray pump x-ray probe (XPXP). These techniques offer new opportunities for resolving the ultrafast microscopic behavior in SCFs at unprecedented spatiotemporal resolution, unraveling the dynamics of their micro-structure. However, harnessing these capabilities requires a bespoke high-pressure and high-temperature sample system that is optimized to maximize signal intensity and address instrument-specific challenges, such as drift in beamline components, x-ray scattering background, and multi-x-ray-beam overlap. We present a pressure cell compatible with a wide range of SCFs with built-in optical access for XPCS and XPXP and discuss critical aspects of the pressure cell design, with a particular focus on the design optimization for XPCS.

The statistical design and analysis of pandemic platform trials: Implications for the future.

The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.

Fatigue-sensitive feature extraction, failure prediction and reliability-based design optimization of the hyperloop tube

Hyperloop research focuses on investigating the design and limitations of its complex subsystems. However, the existing literature overlooks the fatigue failure characteristics of the Hyperloop tube, leaving future engineers without informed estimates of its reliability. To address this gap, this study examined the occurrence and prediction of fatigue failure in the Hyperloop system. The findings revealed that both the underground and above-ground configurations showed resistance to fatigue failure, with the underground system showing greater resilience. Additionally, sensitivity analysis highlighted support spacing, tube ultimate tensile strength, and tube radius as the most influential design variables affecting fatigue sensitivity. Moreover, a reliability-based design cost optimization was performed, taking into account demand uncertainty and utilizing insights from previous analyses to determine ideal design parameters. This research sheds light on critical design aspects of the Hyperloop tube that require intensified attention to effectively mitigate the risk of fatigue failure.

Understanding the interplay of light, color, and interior design in healthcare spaces

Healthcare facilities have evolved from strictly functional to therapeutic places, integrating spiritual and psychological components of health. Design issues must be given specific attention to establish a therapeutic atmosphere that promotes successful therapy and stress alleviation. Color and light have a tremendous influence on the human mind and body, according to extensive studies, making them critical aspects of healthcare facility design. This study’s approach is to contribute to the construction of more effective therapeutic settings by investigating the effects of color and light on human wellness and providing design alternatives. So, it tries to provide a complete design paradigm that combines the strategic use of color and light in healthcare facility interior design. Because healthcare institutions play an important role in improving general well-being, this approach can help to create more effective healing settings. To provide the theoretical framework and collect data, this study uses a combination of library studies and descriptive research. The research initially investigates the notion of color and light, then explains their impact on physical and mental health disorders, as well as their use in therapeutic settings. The study concludes with the creation of a conceptual model and recommended design solutions for healthcare facilities.

Evaluation of Tungsten—Steel Solid-State Bonding: Options and the Role of CALPHAD to Screen Diffusion Bonding Interlayers

Critical aspects of innovative design in engineering disciplines like infrastructure, transportation, and medical applications require the joining of dissimilar materials. This study investigates the literature on solid-state bonding techniques, with a particular focus on diffusion bonding, as an effective method for establishing engineering bonds. Welding and brazing, while widely used, may pose challenges when joining materials with large differences in melting temperature and can lead to mechanical property degradation. In contrast, diffusion bonding offers a lower temperature process that relies on solid-state interactions to develop bond strength. The joining of tungsten and steel, especially for fusion reactors, presents a unique challenge due to the significant disparity in melting temperatures and the propensity to form brittle intermetallics. Here, diffusion characteristics of tungsten–steel interfaces are examined and the influence of bonding parameters on mechanical properties are investigated. Additionally, CALPHAD modeling is employed to explore joining parameters, thermal stability, and diffusion kinetics. The insights from this research can be extended to join numerous dissimilar materials for specific applications such as aerospace, automobile industry, power plants, etc., enabling advanced and robust design with high efficiency.

Design of progressive fuzzy polynomial neural networks through gated recurrent unit structure and correlation/probabilistic selection strategies

This study focuses on two critical design aspects of a progressive fuzzy polynomial neural network (PFPNN): the influence of the gated recurrent unit (GRU) structure and the implementation of fitness-based candidate neuron selection (FCNS) through two probabilistic strategies. The primary objectives are to enhance modeling accuracy and to reduce the computational load associated with nonlinear regression tasks. Compared with the existing fuzzy rule-based modeling architecture, the proposed dynamic model consists of the GRU structure and the hybrid fuzzy polynomial architecture. In the initial two layers of the PFPNN, we introduce three types of polynomial and fuzzy rules into the GRU neurons (GNs) and fuzzy polynomial neurons (FPNs), which can effectively reveal potential complex relationships in the data space. The synergy of the FCNS strategies and the ℓ2 regularization learning method is to design a progressive regression model adept at melding the GRU structure with a self-organizing architecture. The proposed GRU structure and polynomial-based neurons significantly improve the modeling accuracy for time-series datasets. The rational utilization of FCNS strategies can reinforce the network structure and discover the potential performance of neurons of the network. Furthermore, the inclusion of ℓ2 norm regularization provides additional stability to the proposed model and mitigates the overfitting issue commonly encountered in many existing learning methods. We validated the proposed neural networks using six time-series, four machine learning, and two real-world datasets. The PFPNN outperformed other models in the comparison studies in 83.3% of the datasets, emphasizing its superiority in terms of developing a stable deep structure from diverse candidate neurons and reducing computational overhead.

Geometry-based graphical methods for solar control in architecture: A digital framework

The form of a building is among the most critical design aspects concerning building energy consumption. Form-based passive design strategies, like solar control, can significantly reduce heating and cooling demands if implemented early in the design process. In this sense, there is an evident need for tools that can adequately support designers in their decisions. This paper aims to illustrate how geometry-based graphical methods (GGM) can provide effective support in the conceptual design stage. The paper introduces a novel digital framework for designing and analysing shading devices that leverages geometrical models and graphical methods. The digital implementation of GGM allows extending their applicability to three-dimensional and non-planar geometries. A comprehensive review of existing methods and tools for the design of shading devices lays the ground for the proposed digital framework, which is then demonstrated through two case studies. The results show that the diagrammatic nature of GGM facilitates a better and more direct understanding of the relationship between form and performance.

Current status of the end-of-substructure (EoS) card project for the ATLAS strip tracker upgrade using final ASICs

In the context of the high-luminosity upgrade of the LHC and ATLAS, the microstrip-tracking detector will be redesigned. The main building blocks are substructures with multiple sensors and their electronics. Each substructure will have a single interface to the off-detector system, the so-called End-of-Substructure (EoS) card. Its physical realisation is a set of printed circuit boards (PCBs). The PCB integrates ASICs and hybrids, which multiplex or demultipex the data and transmit with a rate up to 10 Gb/s or receive with a rate up to 2.5 Gb/s on optical fibres. These active parts are developed at CERN and are known as lpGBT and VTRx+. The EoS card integrates the active parts with the required electronics for the specified operation and within the mechanical constraints of the detector. In this paper critical design aspects such as the low-impedance powering scheme and the PCB setup are described. The EoS card has reached its final state for a series production, including the required setups for quality control. The achieved transmission quality on the 10 Gb/s links is presented.

Model-scale experiments of passive pitch control for tidal turbines

Tidal currents are renewable and predictable energy sources that could prove fundamental to decrease dependency from fossil fuels. Tidal currents, however, are highly unsteady and non uniform, resulting in undesirable load fluctuations on the blades and the drive train of turbines. A passive morphing blade concept capable to reduce the load fluctuations without affecting the mean loads has recently been formulated and demonstrated with numerical simulations (Pisetta et al., 2022). In this paper, we present the first demonstration of this morphing blade concept, through experimental tests on a 1.2-m diameter turbine. We show that fluctuations in the root-bending moment, thrust and torque are consistently reduced over a broad range of tip-speed ratios. This work also highlights some critical design aspects of morphing blades. For instance, it is showed that the friction resistance can substantially decrease the effectiveness of the system and thus must be minimised by design. Overall this paper demonstrates for the first time the effectiveness of morphing blades for tidal turbines, paving the way to the future development of this technology.