Realistic Conditions Research Articles

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

Ceramic matrix composites for corrosion-resistant next-generation nuclear reactor systems: A conceptual review of enhancements in durability against molten salt attack

Ceramic matrix composites (CMCs) are emerging as a key material class for enhancing corrosion resistance in next-generation nuclear reactor systems, particularly in high-temperature molten salt environments. These environments, critical for advanced nuclear reactors such as molten salt reactors (MSRs), present severe challenges, including aggressive chemical attacks that degrade traditional structural materials over time. This conceptual review explores the development and application of CMCs to improve the durability and corrosion resistance of nuclear reactors exposed to molten salt attacks. CMCs, which consist of ceramic fibers embedded in a ceramic matrix, offer significant advantages, such as high thermal stability, mechanical strength, and improved corrosion resistance compared to conventional materials. This review examines how CMCs can be tailored to withstand harsh operational conditions, with a focus on the selection of ceramic phases, fiber-matrix interactions, and innovative fabrication techniques that enhance their protective capabilities. Key challenges addressed include the optimization of composite design to resist molten salt corrosion, the effects of temperature on the material properties, and the long-term stability of CMCs under extreme conditions. Advances in surface treatments, coatings, and the development of hybrid CMC systems are also discussed, highlighting their potential to further enhance durability. The review outlines the use of advanced characterization techniques, such as high-temperature corrosion testing and in situ microscopy, to evaluate CMC performance in molten salt environments. Additionally, it identifies knowledge gaps in current research, emphasizing the need for long-term studies on CMC behavior under realistic reactor conditions. This review concludes by proposing future research directions and technological advancements required to integrate CMCs into next-generation nuclear reactor designs, aiming to improve system reliability and operational safety.

Dynamic performance investigations of a full-scale unanchored thin-walled steel fluid storage tank via shake table tests

Theoretical models proposed in the literature and seismic design codes make the basis of analysis and the design procedures of fluid storage tanks. These models are derived based on many simplified and approximate assumptions. Under realistic conditions, however, different factors cause violation of these assumptions, causing the fluid flow and fluid–structure interaction (FSI) to diverge from theories. In this research work, 38 swept-sine tests and 63 seismic ground motions were applied to a full-scale highly flexible unanchored thin-walled stainless steel fluid tank. The experimental natural frequencies of the system for three different aspect ratios of 2.1, 2.8, and 3.5 were calculated and compared with the theoretical ones. Damping ratios for different modes were calculated using the half-power bandwidth analysis. Experimental results revealed discrepancies between the theoretical and experimentally detected natural frequencies, especially for the convective mode. Closer matches were found for the aspect ratios of 2.8 and 3.5, for the impulsive mode. Acceleration amplification factors at different heights of the shell were calculated which showed a nonlinear behaviour with a descending trend from the base to the mid-height and then an ascending one towards the fluid surface. Maximum acceleration amplification factor occurred at the surface of the fluid. The axial strains around the base are maximum and decrease bi-linearly towards the upper heights of the shell. Effects of the input excitation frequency content over the structural responses of the system were examined. Frequency characteristics of the input excitation considerably affected the maximum acceleration amplification factors and axial strains in the shell.

Transmission electron microscopy of epitaxial semiconductor materials and devices

Abstract The transmission electron microscope (TEM) is a powerful imaging, diffraction and spectroscopy tool that has revolutionized the field of microscopy. It has contributed to numerous breakthroughs in various scientific disciplines. TEM-based techniques can offer atomic resolution as well as elemental analysis, which benefit the study of epitaxial semiconductors and their related optoelectronic devices on the atomic scale. The design and optimization of the device performance depend on three key factors: the control of strain at nanometer scale, control of the formation and propagation of defects as well as the control of local electronic properties. Manipulation and optimization are only possible if the key factors can be characterized precisely. Herein, the TEM techniques for strain analysis, defect characterization and bandgap evaluation are reviewed and discussed. Lately, with the development of in-situ TEM techniques, researchers have been able to observe dynamic processes and study the behaviour of materials and devices under realistic conditions (in gaseous atmosphere or in liquids, at elevated or cryogenic temperatures, under strain, bias or illumination) in real-time with extremely high spatial resolution. This review explores the impact and significance of in-situ TEM in the field of semiconductors.

Backdoor Attacks in Peer-to-Peer Federated Learning

Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets. While federated learning (FL) mitigates to some extent these privacy risks, it relies on a trusted aggregation server for training a shared global model. Recently, new distributed learning architectures based on Peer-to-Peer Federated Learning (P2PFL) offer advantages in terms of both privacy and reliability. Still, their resilience to poisoning attacks during training has not been investigated. In this paper, we propose new backdoor attacks for P2PFL that leverage structural graph properties to select the malicious nodes, and achieve high attack success, while remaining stealthy. We evaluate our attacks under various realistic conditions, including multiple graph topologies, limited adversarial visibility of the network, and clients with non-IID data. Finally, we show the limitations of existing defenses adapted from FL and design a new defense that successfully mitigates the backdoor attacks, without an impact on model accuracy.

High-Precision Disparity Estimation for Lunar Scene Using Optimized Census Transform and Superpixel Refinement

High-precision lunar scene 3D data are essential for lunar exploration and the construction of scientific research stations. Currently, most existing data from orbital imagery offers resolutions up to 0.5–2 m, which is inadequate for tasks requiring centimeter-level precision. To overcome this, our research focuses on using in situ stereo vision systems for finer 3D reconstructions directly from the lunar surface. However, the scarcity and homogeneity of available lunar surface stereo datasets, combined with the Moon’s unique conditions—such as variable lighting from low albedo, sparse surface textures, and extensive shadow occlusions—pose significant challenges to the effectiveness of traditional stereo matching techniques. To address the dataset gap, we propose a method using Unreal Engine 4 (UE4) for high-fidelity physical simulation of lunar surface scenes, generating high-resolution images under realistic and challenging conditions. Additionally, we propose an optimized cost calculation method based on Census transform and color intensity fusion, along with a multi-level super-pixel disparity optimization, to improve matching accuracy under harsh lunar conditions. Experimental results demonstrate that the proposed method exhibits exceptional robustness and accuracy in our soon-to-be-released multi-scene lunar dataset, effectively addressing issues related to special lighting conditions, weak textures, and shadow occlusion, ultimately enhancing disparity estimation accuracy.

Device optimization of all-perovskite triple-junction solar cells for realistic irradiation conditions

Device optimization of all-perovskite triple-junction solar cells for realistic irradiation conditions

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom-up and top-down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom-up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom-up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

Optimizing energy density in dielectric elastomer generators: a reliability-dependent metric

Abstract Dielectric elastomer generators (DEGs) are soft transducers capable of converting mechanical energy into electrostatic energy. Increasing the mechanical stretch amplitude and the electric field imposed to the DEG leads to higher energy conversion at the cost of a reduced lifetime. Here, mechanical fatigue and electrical degradation were assessed on a silicone-based DEG, and the outcome was used to build an electro-mechanical reliability model. A novel metric, termed levelized energy density, has been introduced to carefully balance the conflicting objectives of high energy output and long-term reliability. Through a multi-dimensional anaylsis of this index, the optimal operating parameters (stretch amplitude and electric field) that maximize energy conversion can be derived. Energy densities reported in literature are generally obtained after pushing the DEG close to their intrinsic limits for a limited number of cycles. In our approach, more realistic values in the endurance domain are presented, which typically leads to a 9-fold decrease in energy density for a design life of 1 million cycles. This article not only addresses the challenge of optimizing DEG performance but also emphasizes the importance of considering realistic operational conditions to enhance reliability, ultimately contributing to the practical and sustainable deployment of these soft transducers in various applications.

Operando XANES Reveals the Chemical State of Iron-Oxide Monolayers During Low-Temperature CO Oxidation.

We have used grazing incidence X-ray absorption near edge spectroscopy (XANES) to investigate the behavior of monolayer FeOxfilms on Pt(111) under near ambient pressure CO oxidation conditions with a total gas pressure of 1 bar. Spectra indicate reversible changes during oxidation and reduction by O2 and CO at 150ºC, attributed to a transformation between FeO bilayer and FeO2 trilayer phases. The trilayer phase is also reduced upon heating in CO+O2, consistent with a Mars-van-Krevelen type mechanism for CO oxidation. At higher temperatures, the monolayer film dewets the surface, resulting in a loss of the observed reducibility. A similar iron oxide film prepared on Au(111) shows little sign of reduction or oxidation under the same conditions. The results highlight the unique properties of monolayer FeO and the importance of the Pt support in this reaction. The study furthermore demonstrates the power of grazing-incidence XAFS for in situ studies of these model catalysts under realistic conditions.

A comprehensive numerical investigation of bi-dimensional analysis of PVT system using square channel absorber

Abstract This paper offers a comprehensive numerical investigation of PVT solar thermal performance using the Finite Difference Method (FDM) for both longitudinal bi-dimensional transient and steady state analysis, which is the unique aspect of this study. Likewise, a thorough analysis of the use of two distinct cooling absorber PVT solar collectors is conducted under realistic climatic conditions. The current modeling methodology can be considered a useful tool that enables rapid bi-dimensional PVT system analysis. The findings show that, in comparison to the PVT tube configuration (587 kWh/m2, 178 kWh/m2), the direct flow square channel has more yearly thermal and electrical gain outputs (608 kWh/m2, 196 kWh/m2). Furthermore, the design of directly flow channels has the advantages of easier integration and effective conduction heat transfer between the fluid and absorber panel. A good agreement between the measured and predicted data was obtained once the current results were compared with theoretical and experimental data in the literature. 

Chronic oral toxicity protocol for adult solitary bees (Osmia bicornis L.): Reduced survival under long-term exposure to a “bee-safe” insecticide

Pollinators are essential for crop productivity. Yet, in agricultural areas, they may be threatened by pesticide exposure. Current pesticide risk assessments predominantly focus on honey bees, with a lack of standardized protocols for solitary bees. This study addresses this gap by developing a long-term oral exposure protocol tailored for O. bicornis. We conducted initial trials to determine optimal container sizes and feeding methods, ensuring high survival rates and accurate syrup consumption measurements. A validation test involving five laboratories was then conducted with the insecticide Flupyradifurone (FPF). Control mortality thresholds were set at ≤ 15% at 10 days. Three laboratories achieved ≤10%, demonstrating the protocol's effectiveness in maintaining healthy test populations. The seasonal timing of experiments influenced control mortality, underscoring the importance of aligning tests with the natural flight period of the population used. Our findings revealed dose-dependent effects of FPF on syrup consumption, showing stimulatory effects at lower concentrations and inhibitory effects at higher ones. The 10-day median lethal daily dose (LDD50) of FPF for O. bicornis (531.92 ng/bee/day) was 3.4-fold lower than that reported for Apis mellifera (1830 ng/bee/day), indicating Osmia's higher susceptibility. Unlike other insecticides, FPF did not exhibit time-reinforced toxicity. This study introduces a robust protocol for chronic pesticide exposure in solitary bees, addressing a critical gap in current risk assessment. Based on its low risk to honey bees and bumblebees, FPF is approved for application during flowering. However, our results suggest that it may threaten Osmia populations under realistic field conditions. Our findings underscore the need for comparative toxicity studies to ensure comprehensive protection of all pollinators and the importance of accounting for long term exposure scenarios in risk assessment. By enhancing our understanding of chronic pesticide effects in solitary bees, our study should contribute to the development of more effective conservation strategies and sustainable agricultural practices.

Comparison of chamber beam geometry robustness to mispointing, imbalance and target offset for direct-drive laser fusion facilities

Abstract This study focuses on the optimization of beam chamber geometry designs for future direct-drive laser facilities. It provides a review of leading target chamber geometries, with a particular emphasis on random errors. Through comprehensive solid-sphere illuminations and analysis, we identify an optimized beam geometry design, highlighting its robustness and performance under realistic experimental conditions. Three major sources of random errors are evaluated, closely linked to experimental evaluations at OMEGA. The findings underscore the importance of optimizing the irradiation system alongside beam pattern considerations to enhance the efficiency and reliability of inertial confinement fusion experiments. We conclude that for a desired illumination uniformity of 1% in the presence of system errors, the split icosahedron design is the most robust. However, for a 0.3% uniformity goal, the charged-particle, icosahedron, and t-sphere methods exhibit similar performance.

Reply to Comment by Chang on “Anticyclonic Suppression of the North Pacific Transient Eddy Activity in Midwinter”

AbstractChang (2024, https://doi.org/10.1029/2024gl110011) challenged the methodology proposed recently by Okajima et al. for evaluating cyclonic and anticyclonic contributions to Eulerian eddy statistics and atmospheric energetics based on the local flow curvature. He argued that using the local wind curvature to separate energetic contributions from cyclones and anticyclones is not physically meaningful. Here we argue that his claims are based on an unrealistic assumption of monopolar relative vorticity in an entire storm‐track domain and a meridionally uniform zonal background flow atypical to midlatitudes. We also demonstrate that the error in attributing eddy statistics to cyclones and anticyclones is significantly smaller than his estimation. Rather, we further demonstrate that the curvature‐based methodology effectively eliminates the shear influence to identify cyclonic and anticyclonic regions, which is dismissed in his argument. We conclude that the curvature‐based methodology is beneficial in evaluating distinct cyclonic and anticyclonic contributions to atmospheric energetics in realistic conditions.

A comparative analysis of face and object perception in 2D laboratory and virtual reality settings: insights from induced oscillatory responses.

In psychophysiological research, the use of Virtual Reality (VR) for stimulus presentation allows for the investigation of how perceptual processing adapts to varying degrees of realism. Previous time-domain studies have shown that perceptual processing involves modality-specific neural mechanisms, as evidenced by distinct stimulus-locked components. Analyzing induced oscillations across different frequency bands can provide further insights into neural processes that are not strictly phase-locked to stimulus onset. This study uses a simple perceptual paradigm presenting images of faces and cars on both a standard 2D monitor and in an immersive VR environment. To investigate potential modality-dependent differences in attention, cognitive load, and task-related post-movement processing, the induced alpha, theta and beta band responses are compared between the two modalities. No evidence was found for differences in stimulus-dependent attention or task-related post-movement processing between the 2D conditions and the realistic virtual conditions in electrode space, as posterior alpha suppression and re-synchronization of centro-parietal beta did not differ between conditions. However, source analysis revealed differences in the attention networks engaged during 2D and 3D perception. Midfrontal theta was significantly stronger in laboratory conditions, indicating higher cognitive load than in the VR environment. Exploratory analysis of posterior theta showed stronger responses in VR, possibly reflecting the processing of depth information provided only by the 3D material. In addition, the theta response seems to be generated by distinct neuronal sources under realistic virtual conditions indicating enhanced involvement of semantic information processing and social cognition.

Methodology and Database for the Quantification of the Technical Recyclability of Electrical and Electronical Equipment Demonstrated on a Smartphone Case Study

Assessing a given product’s design and its recyclability using mass flow analysis based on the material separation and recovery rates of individual recycling processes under realistic conditions can support design decisions promoting better recyclability. EN 45555 defines the calculation of the technical recyclability of electrical and electronic equipment (EEE). However, the lack of specific recycling rates for material or processes often leads to either too small or too high recyclability values. Herein, an extensive database of such recycling rates is presented. Moreover, the quality of recycling is considered. The typical classification into “recycled” and “lost” is expanded into four categories, namely “circular”, “recycled”, “alternate material recovery” and “lost”. The recycling rate database includes yields for all four categories and covers 30 materials for 14 recycling processes relevant in waste EEE (WEEE) treatment. These data enable a detailed calculation of the recyclability of various EEE for multiple recycling scenarios covering the entire WEEE recycling chain. Fraunhofer IZM performed an internal critical review of the data. The recycling rates database can act as a solid foundation for comparing the recyclability of various electronics in different scenarios and recyclability indices. For example, the recyclability of typical smartphones is investigated comparing different dismantling and recycling scenarios highlighting the potential of both database and methodology.

Molecular Simulation of Cyclohexane in Nanoporous Materials: Adsorption of Conformers and Coadsorption with Water and Carbon Dioxide.

Molecular simulation is used to investigate the adsorption of an organic molecule and its different conformers into various nanoporous adsorbents. In more detail, we perform grand canonical Monte Carlo simulations to determine the adsorption isotherms for cyclohexane with its three conformers (chair, boat, and twisted boat) at three different temperatures into a molecular model of active carbons and two metal-organic frameworks (MOFs) (Cu-BTC and Al-Fum). By considering the adsorption of each conformer separately, we show that the adsorption isotherms are weakly dependent on the molecular conformation. When considering the concomitant adsorption of the three conformers under realistic conditions (to verify the population of each conformer in bulk mixtures), we show that the chair conformer is predominantly adsorbed in each material. However, such favorable adsorption mostly reflects the fact that this conformer has the largest population in the bulk mixture under the same thermodynamic conditions. On the contrary, with an increase in the cyclohexane gas pressure, the adsorption of boat and twisted boat conformers increases, while that of the chair conformer decreases as packing (entropy) effects become predominant during confinement. The adsorption of cyclohexane in the active carbon and MOF materials is also considered in the presence of water and CO2 atmospheres. In the case of the Al-fumarate material, the material is found to be strongly organophilic so that water and CO2 adsorption are negligible, and cyclohexane adsorption is not affected by competitive adsorption under any considered thermodynamic condition. On the contrary, for the active carbon and Cu-BTC material, competitive adsorption between cyclohexane and water or CO2 is observed even if cyclohexane adsorption remains preferred. While the different molecules coexist in the adsorbed phase at low pressures, increasing the cyclohexane pressure beyond 103 Pa promotes cyclohexane adsorption concomitantly with water and/or CO2 desorption.

EM Radiation Thermal Effects Simulation Study on a Realistic Female Model at Some Frequencies

The purpose of research is to develop a computer simulation based mobile phone safety testing scheme that is well suited for testing any mobile phone antenna at different communication frequencies and various realistic scenarios. It has been shown how feasible the study of realistic models is, what difficulties may occur, what effects, moments appear in the modeling process. The aim of the study is also to show the need for changes in the standard of mobile phone safety testing and to investigate the possibilities of implementing these changes. It is preferable for manufacturers to perform testing using human realistic models through numerical calculations and conduct safety compliance testing under realistic conditions to produce more realistic picture. Computer modeling gives the possibility to consider various realistic models, realistic scenarios and different tissue dielectric properties according to the frequency. It was investigated how mobile phone antenna radiation parameters (S11 reflection coefficient) change according to the various positions of the hand and fingers at standard communication 2100 MHz and 3700 MHz frequencies; were estimated Specific Absorption Rate (SAR) values in the human head and their dependence on S11 parameters were studied. Based on the obtained results, it has been shown that the preferred S11 behavior should be one of the indicators in the phone safety standards. For this, manufacturers should carry out mobile phone antenna matching/S11 parameter testing with free space.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

Automatic estimation of the wind turbine noise with recurrent neural networks

There is growing interest in the development of renewable energies, particularly wind power. However, wind turbines generate noise that can affect the sound environment of nearby residents. This study focuses on the isolation of wind farm noise (WFN) from the surrounding ambient noise. Our method is based on a Recurrent Neural Network (RNN) Architecture that captures temporal dependencies in the acoustic signal. This proposal is compared to Non-Negative Matrix Factorization (NMF) that has shown first promising results on a previous study on simulated sound scenes. Our approach relies on Long Short Term Memory (LSTM) RNN, conducted using an end-to-end trained model and a Gated Recurrent Unit (GRU). The training and testing dataset is constructed by superimposing measured background noise with synthesized wind turbine noise, effectively simulating realistic environmental conditions. This study aims to reduce the acoustic impact of wind turbines on communities and attempt to control turbine operation using advanced machine learning techniques.