Experimental Setup Research Articles

Interplay between Dynamic Phase and Geometric Phase Determines the Circular Dichroism and Helical Dichroism.

Understanding the interaction between light and chiral nanostructures is of fundamental importance, yet the principles governing chiral interactions have remained largely phenomenological. In this work, we present a chiral field-mode (FM) matching model to quantify the circular dichroism (CD) and helical dichroism (HD) of chiral plasmonic nanostructures interacting with beams of different spin-orbit states. The chiral FM matching model posits that among the inherent resonance modes within the nanostructure, the most efficiently excited mode is the one that matches the external field structure by possessing one more node along the vibration direction, with the field structure itself being determined by the interaction between the geometric phase and dynamic phase through a Doppler-like effect. The geometric phase in this model is well-defined by the product of the winding angle of the nanohelix and the angular momenta of light, including both its spin and orbital components. Thus, the beams of different spin-orbit states excite the specific resonance mode possessing one more node compared with the field structure, resulting in the spin-related CD and orbit-related HD. This model is extended to various chiral nanocomplexes, demonstrating how the field structure determines mode excitation and offering a comprehensive explanation for the CD and HD observed in various experimental setups. This model offers insights into the CD and HD microscopy in chiral nanostructures, contributing to the advancement of the fundamental theory of chiral nanophotonics.

Abstract 4124828: The Methylation Landscape Of Human Aortic And Mitral Valvular Interstitial Cells

Valvular interstitial cells (VICs), the valves’ predominant cell type, are crucial in ensuring proper valve function, structural integrity, tissue repair and valve homeostasis. DNA methylation regulates gene expression profiles and a matched aortic to mitral human VIC methylation analysis will further our understanding of aortic- and mitral-specific VIC biology and pathobiology. This is the first study analyzing methylation profiles of non-diseased, matched human aortic and mitral VICs. We analyzed the differential methylation fingerprint of 12 non-diseased aortic and mitral VICs (10 males:2 females; 42–64 years) extracted from de-endothelialized leaflets using reduced representation bisulfite sequencing (RRBS) on HiSeq2500. Analysis of methylation levels of 12,676 genome-wide promoters revealed 651 differentially methylated (DM) promoters. The top 3 DM promoters were linked to TBX4 (meth diff = -28.9%, P = 5.2E-135) essential for valve morphogenesis, to EXOC3L2 (meth diff = 25.7%, P = 1.3E-92) and NT5DC2 (meth diff = -20.3%, P = 7.1E-69) potentially implicated in ECM remodeling, the former via vesicle trafficking and the latter through nucleotide metabolism. Functional classification and network analysis showed that the genes associated with the DM promoters were enriched for WNT-, TGFβ-, FGF-, EGF- and PDGF- pathways involved in physiological processes such as valve development, repair and VIC regulation, as well as pathological processes such as calcification. Additional enrichment was detected for integrin- and cadherin- pathways involved in cell-cell-ECM interactions and endothelin- and VEGF pathways involved in hemodynamic regulation. This work provides the first insight into the differential methylation regulation patterns of human aortic and mitral VICs. Understanding how aortic and mitral VICs are differentially epigenetically regulated will extend our understanding of their role in aortic- and mitral-specific leaflet homeostasis, susceptibility to disease processes and will contribute to dynamic innovative experimental setup designs, animal-free human-based modeling frameworks, tissue engineering and drug identification approaches.

Potential hazards of octinoxate (ethylhexyl methoxycinnamate) exposure in the seagrass Posidonia oceanica (L.) Delile: Experimental evidence

Interest has risen in recent years to assess the fate and impacts of organic ultraviolet (UV) filters in aquatic environments, after being recognized as contaminants of emerging concern. Octinoxate, also known as ethylhexyl methoxycinnamate (EHMC), is among the widely used UV filters in sunscreen formulations. Its potential toxicological effects in aquatic biota have led to precautionary actions in some jurisdictions, such as restricting and even banning its presence in commercial sunscreens. However, research data is lacking for effective regulation of sunscreen ingredients in Mediterranean countries. Our study evaluated the response of the seagrass Posidonia oceanica to EHMC under a short-term experimental setup, recreating summer conditions in aquaria subjected to different environmentally relevant concentrations for the Mediterranean Sea (minimum: 30.57 ng L−1, intermediate: 710 ng L−1 and maximum: 1420 ng L−1) and a control (no addition). Negative responses were evidenced in the physiology of P. oceanica due to EHMC exposure, with decreased gross primary production rates, loss of leaf chlorophyll content and inhibition of the overall nitrogen fixation associated with the seagrass tissues. Elevated oxidative stress biomarkers in P. oceanica under EHMC addition (i.e., catalase activity, polyphenols concentrations and alkaline phosphatase activity in leaves) reflect the activation of antioxidant defenses to counteract oxidative damages possibly induced in cellular components by the presence of the contaminant. Our results may conservatively imply the high ecological risk imposed to P. oceanica by maximum concentrations of EHMC in Mediterranean coastal waters. Moreover, they contribute as experimental evidence of the potentially detrimental effects of sunscreen pollution in a keystone species, such as P. oceanica, which is the main structural component of a priority habitat for conservation in the Mediterranean Sea, currently affected by multiple stress factors leading to its recession in recent years.

A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models

Abstract. In ocean or Earth system model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional, or even basin-wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is particularly relevant for semi-enclosed seas such as the Baltic Sea. To achieve low biases in riverine freshwater inflow in large-scale climate applications, a bias correction is required that can be applied in periods where runoff observations are not available and that allows spatial transferability of its correction factors. In order to meet these requirements, we have developed a three-quantile bias correction that includes different correction factors for low-, medium-, and high-percentile ranges of river runoff over Europe. Here, we present an experimental setup using the Hydrological Discharge (HD) model and its high-resolution (1/12°) grid. First, bias correction factors are derived at the locations of the downstream stations with available daily discharge observations for many European rivers. These factors are then transferred to the respective river mouths and mapped to neighbouring grid boxes belonging to ungauged catchments. The results show that the bias correction generally leads to an improved representation of river runoff. Especially over northern Europe, where many rivers are regulated, the three-quantile bias correction provides an advantage compared to a bias correction that only corrects the mean bias of the river runoff. Evaluating two NEMO (Nucleus for European Modelling of the Ocean) model simulations in the German Bight indicated that the use of the bias-corrected discharges as forcing leads to an improved simulation of sea surface salinity in coastal areas. Although the bias correction is tailored to the high-resolution HD model grid over Europe in the present study, the methodology is suitable for any high-resolution model region with a sufficiently high coverage of river runoff observations. It is also noted that the methodology is applicable to river runoff based on climate hindcasts, as well as on historical climate simulations where the sequence of weather events does not match the actual observed history. Therefore, it may also be applied in climate change simulations.

Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach

Machine learning has become increasingly important in biomechanics. It allows to unveil hidden patterns from large and complex data, which leads to a more comprehensive understanding of biomechanical processes and deeper insights into human movement. However, machine learning models are often trained on a single dataset with a limited number of participants, which negatively affects their robustness and generalizability. Combining data from multiple existing sources provides an opportunity to overcome these limitations without spending more time on recruiting participants and recording new data. It is furthermore an opportunity for researchers who lack the financial requirements or laboratory equipment to conduct expensive motion capture studies themselves. At the same time, subtle interlaboratory differences can be problematic in an analysis, due to the bias that they introduce. In our study, we investigated differences in motion capture datasets in the context of machine learning, for which we combined overground walking trials from four existing studies. Specifically, our goal was to examine whether a machine learning model was able to predict the original data source based on marker and GRF trajectories of single strides, and how different scaling methods and pooling procedures affected the outcome. Layer-wise relevance propagation was applied to understand which factors were influential to distinguish the original data sources. We found that the model could predict the original data source with a very high accuracy (up to >99%), which decreased by about 15 percentage points when we scaled every dataset individually prior to pooling. However, none of the proposed scaling methods could fully remove the dataset bias. Layer-wise relevance propagation revealed that there was not only one single factor that differed between all datasets. Instead, every dataset had its unique characteristics that were picked up by the model. These variables differed between the scaling and pooling approaches but were mostly consistent between trials belonging to the same dataset. Our results show that motion capture data is sensitive even to small deviations in marker placement and experimental setup and that small inter-group differences should not be overinterpreted during data analysis, especially when the data was collected in different labs. Furthermore, we recommend scaling datasets individually prior to pooling them which led to the lowest accuracy. We want to raise awareness that differences in datasets always exist and are recognizable by machine learning models. Researchers should thus think about how these differences might affect their results when combining data from different studies.

Development and Assessment of a Batch Type Ohmic Heating System for Milk Processing

This study aims to develop and assess the performance of a batch-type ohmic heating system for the thermal processing of cow and buffalo milk, particularly investigating whether the ohmic system affects the nutritional and microbial quality of the milk. An experimental setup was fabricated with a 7-liter capacity, utilizing an agitator as one electrode and the vessel surface as the other. The system was tested for heating milk from 32°C to target temperatures of 75°C and 92°C. Key processing parameters, including agitator speed and applied voltage, were optimized for both milk types. The physicochemical and microbial properties of the ohmically heated milk were evaluated, along with performance metrics such as heating rates, power consumption, and system efficiency. The developed ohmic heating system achieved heating rates of 4.29°C/min for cow milk and 2.85°C/min for buffalo milk. Cow milk demonstrated lower energy consumption, requiring less time and voltage compared to buffalo milk, with system performance coefficients of 58% for cow milk and 50% for buffalo milk. However, fouling on the agitator surface was identified as a challenge for industrial applications. Importantly, the ohmic heating process preserved the nutritional content while effectively reducing microbial load, confirming its potential for dairy processing. In conclusion, the batch-type ohmic heating system exhibited high efficiency and rapid heating capabilities for both cow and buffalo milk, indicating its viability for commercial use. Future research should focus on addressing fouling issues and further optimizing the system, enhancing the overall potential of ohmic heating technology in the dairy industry.

Development and Assessment of a Miniaturized Test Rig for Evaluating Noise Reduction in Serrated Blades Under Turbulent Flow Conditions

The implementation of serrated stator blades in axial compressor and fan stages offers significant advantages, such as enhanced performance and reduced noise levels, making it a practical and cost-effective solution. This study explores the impact of serrated blade design on noise reduction under specific engine operating conditions. A small-scale experimental test setup with a turbulence-inducing grid was designed for testing multiple grid sizes in order to identify the most promising configuration which replicates rotor–stator interaction. Numerical simulations and early experimental tests in an anechoic chamber using a four-blade cascade configuration at an airflow speed of 50 m/s revealed a small but notable noise reduction in the 1–6 kHz range for a partially matched grid–blade geometry. Serrated blades demonstrated an overall sound pressure level reduction of 1.5 dB and up to 12 dB in tonal noise, highlighting the potential of cascade configurations to improve acoustic performance in gas turbine applications.

Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.

Human socio-technical evolution through the lens of an abstracted-wheel experiment: A critical look at a micro-society laboratory study.

Micro-society experimental setups are increasingly used to infer aspects of human behavioural evolution. A key part of human society today is our dependence on, and use of, technology-whether simple (such as a knife) or complex (such as the technology that underpins AI). Previously, two groups of researchers used an abstracted-wheel experiment to explore the evolution of human technical behaviour, reaching fundamentally different outcomes. Whereas one group saw their results as indicating social learning only (void of causal understanding), the other inferred non-social technical reasoning as part of human technical behaviour. Here we report on the third generation of the micro-society abstracted-wheel experiment. We argue that causal reasoning is inseparable from both social learning and technical reasoning, and that these traits probably co-evolved into the current human socio-technical niche. Based on our outcomes, we present a critical assessment of what this experiment may (or may not) reveal about the evolution of human technical behaviour. We show that the abstracted-wheel experiment reflects behavioural output only, instead of testing for cognition. It is therefore limited in its ability to inform on aspects of human cognitive evolution, but it can provide useful insights into the interrelatedness of social learning, technical reasoning, and causal reasoning. Such a co-evolutionary insight has the potential to inform on aspects of human socio-technical evolution throughout the Pleistocene.

Functions of a Complex Variable with a Large Parameter and Construction of Regions

The paper describes an experimental setup for studying the solubility of substances in SCF media and the implementation of a supercritical fluid extraction process using a pure and modified polar solvent additive. The advantages and disadvantages of this installation are analyzed, ways of improving it are indicated, leading to achieving a more accurate GFR flow rate, maintaining the necessary constant pressure in the first extract collector for fractionation of a certain component of the mixture and eliminating multiple regulation of the GFR flow rate.

Study on the Effect of Dry and Wet Cycle Time Ratio on the Corrosion Behavior of 2198 Aluminum–Lithium Alloy

ABSTRACTThe corrosion of 2198 aluminum–lithium alloy in a marine atmospheric environment was simulated using a wet–dry alternating experimental setup. Weight‐loss measurement methods, electrochemical impedance spectroscopy (EIS), scanning electron microscopy/energy‐dispersive spectroscopy (SEM/EDS), macroscopic morphology testing, three‐dimensional morphology testing, and X‐ray photoelectron spectroscopy (XPS) were used to study the wet–dry alternating corrosion behaviors and mechanisms of 2198 aluminum–lithium alloys under simulated marine atmospheric environment. The results indicate that corrosion weight loss increases with decreasing wet‐to‐dry ratios, accompanied by an increase in the corrosion rate. Various data suggest that the primary corrosion products are composed of Al2O3, AlO(OH), and AlCl3. The reduction in the wet‐to‐dry ratio leads to the enrichment of Cl⁻ within the corrosion products, extending the contact time between the substrate and the medium, thereby intensifying the hydrolysis of Al³⁺. This exacerbates the dissolution at pitting sites, which in turn erodes and breaks down the corrosion product film, accelerating the overall corrosion process.

Revealing single-neuron and network-activity interaction by combining high-density microelectrode array and optogenetics.

The synchronous activity of neuronal networks is considered crucial for brain function. However, the interaction between single-neuron activity and network-wide activity remains poorly understood. This study explored this interaction within cultured networks of rat cortical neurons. Employing a combination of high-density microelectrode array recording and optogenetic stimulation, we established an experimental setup enabling simultaneous recording and stimulation at a precise single-neuron level that can be scaled to the level of the whole network. Leveraging our system, we identified a network burst-dependent response change in single neurons, providing a possible mechanism for the network-burst-dependent loss of information within the network and consequent cognitive impairment during epileptic seizures. Additionally, we directly recorded a leader neuron initiating a spontaneous network burst and characterized its firing properties, indicating that the bursting activity of hub neurons in the brain can initiate network-wide activity. Our study offers valuable insights into brain networks characterized by a combination of bottom-up self-organization and top-down regulation.

Acoustic sub-wavelength imaging via a virtual super-lens

Overcoming the diffraction limit has been a long-lasting pursuit for researchers owing to the great potential it offers in going beyond the fundamental resolution restriction in imaging processes. In acoustics, meta-lenses have been a promising way to achieve sub-wavelength imaging, the practical application of which, however, has been limited by expensive material manufacturing, complex system setup, and material loss. Here, we propose a set of procedures equivalent to a virtual super-lens that selectively amplifies the evanescent wave components in the measured acoustic field spectrum, thereby enabling super-resolution imaging without any auxiliary setups or purposely designed super-lens. The proposed virtual super-lens is experimentally verified by considering the imaging of an irregularly shaped sample with sub-wavelength features. We further demonstrate the robustness of the high-quality imaging performance remains acceptable with some environment background noises. In the light of the simple experimental setup involved, our proposed method is flexible and can be readily applied to various practical imaging scenarios.

Charged atomization characteristics and influencing factors analysis of aviation kerosene/ethanol blended fuel

ABSTRACT In this study, an experimental apparatus for charged atomization was established to explore the charged atomization modes of blended aviation kerosene/ethanol fuels and the corresponding evolution of atomization modes for E0 (100% aviation kerosene), E10 (10% ethanol + 90% aviation kerosene), E30 (30% ethanol + 70% aviation kerosene), and E50 (50% ethanol + 50% aviation kerosene) blends with varying applied voltages. Furthermore, the influence of the ethanol blending ratio, electrode separation distance, fuel flow rate, nozzle diameter, and other influencing factors on the charged atomization characteristics was investigated. Experimental results within the 0 to 10 kV voltage range revealed that as the voltage increased, the E0 fuel transitioned from droplet formation to spindle shape without exhibiting atomization, while the E10, E30, and E50 fuels displayed a sequential progression of atomization modes including droplet, micro-droplet, pulsed-jet, cone-jet, deflected-jet, and multi-jet configurations. As the ethanol blending ratio increases, the critical voltage required for the transition to atomization mode decreases, while the atomization cone angle gradually increases. The average atomization cone angles are 38.98°, 49.97°, and 66.83° for E10, E30, and E50, respectively. When the nozzle diameter is reduced from 1.05 mm to 0.3 mm, the charged atomization cone angle does not change significantly. With increasing fuel flow, the critical voltage for the atomization mode transition increases progressively, and the charged atomization cone angle also increases. The average atomization cone angles are 65.80°, 88.45°, 93.04°, 103.63°, and 110.97°, respectively.

Determination of ultrafine particle number emission factors from building materials in standardized conditions.

When comparing the particle emissivity for different materials and/or mechanical activities, a serious methodological issue emerges due to the dynamic nature of solid aerosols. Particle size distribution and concentration depend on initial particle emission that constantly evolves due to aerodynamic collisions. In this context, we propose a methodological approach and an experimental setup that enables to assess the release of fine/ultra-fine particles maintaining a steady-state inhalable mass concentration, here chosen at the Swiss occupational exposure level value for biopersistent granular particles (OEL: 10mg/m3) in a controlled ventilation chamber. As a case study, this methodological protocol was tested in the occupational exposure scenario in which a series of insulating materials based on silica aerogel and conventional mortar and concrete were subjected to handling or sawing. Once the OEL was reached, the particle size distribution and morphology of the aerosols were characterized using direct reading instruments (scanning mobility sizer, aerosol photometer) and electron microscopy (SEM and TEM) analyses. As a main result, the presence of silica aerogel in the mortar did not modify the emission profile for submicronic particles during sawing in comparison to the bulk mortar. Emission factors for ultra-fine particles were found to be 88×106 and 81×106 particles/µg of inhalable dust for the aerogel mortar and bulk mortar, respectively. For concrete sawing, the number concentration of submicronic particles at the OEL is one order of magnitude greater. The aerogel-glass-wool handling generated similar particle number concentration at the OEL with ultra-fine particle emission factors of 647×106 particles/µg of inhalable dust, in comparison to 758×106 particles/µg of inhalable dust during dry concrete sawing. In conclusion, the methodology introduced in this work provides standardized particle emission factors for comparing materials and activities, while establishing a link between particle number emissions and occupational exposure limits.

Neural Dynamics of Creative Movements During the Rehearsal and Performance of “LiveWire"

This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of “LiveWire” - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.

Evaluation of the effectiveness of Fenton and photo-Fenton processes for degradation and mineralisation of the model pollutant caffeine

ABSTRACT This study investigates caffeine degradation and mineralisation using Fenton and photo-Fenton processes through a 2k Factorial Design. Key parameters varied include Fe2+ concentration (2 and 10 mg/L), H₂O₂ concentration (50 and 500 mg/L), light intensity (0 and 96 W), and reaction time (0 and 60 min) to optimise degradation. The experimental setup featured a 1 L glass contact column in a UV-controlled chamber with an initial caffeine concentration of 20 mg/L. Results revealed that Fenton was inefficient, achieving only 12% degradation and 9% mineralisation, with dimethylparabanic acid as the main by-product. Conversely, photo-Fenton attained 88% degradation and 40% mineralisation, demonstrating the significant influence of UV light. Energy consumption varied from 3.33 to 50 kWh.m−3. Optimal conditions identified were 10 mg/L Fe2+, 50 mg/L H₂O₂, 96 W UV intensity, and a 60-minute reaction time, highlighting the effectiveness of advanced photo-Fenton oxidation for caffeine treatment.

Exploring Cool Pavement Technologies: A Lab-Based Experimental Analysis of Temperature and Reflectivity

Urban heat islands (UHI) have become an increasingly pressing issue owing to the global warming trend and a steady growth of urban areas. Revegetation, shading, or the reduction of sealed surfaces is not always possible in cities. Therefore, one way to fight UHIs is through adapting currently used pavements. This study assesses the thermal behavior of 12 different pavement materials in a custom-made test stand. The materials include mastic asphalt ( MA 8, MA 8 white), porous asphalt ( PA 4, PA 4 abraded, PA 4 white, PA 8, PA 8 abraded, PA 8 white), asphalt concrete ( AC 11 transparent, AC 8 yellow), and semi-flexible pavements ( SFP 8, SFP 8 zeolite). The albedo was assessed using pyranometer. Sensors and thermal imaging were used to measure temperatures during two simulated heat cycles. Both the temperatures underneath the test specimens and at the surface were considered, as well as the cooling times of the materials. The results showed high maximum temperatures for PA and MA, as well as low maxima for the white painted materials, with differences in maxima of up to 15°C. Porous asphalt, like PA 8, cools faster than MA 8 from roughly 47°C to 28°C, taking 3.7 h compared with MA 8’s 5.1 h. This highlights the importance of considering cooling times and regional solar phases in experimental setups. Through the wide range of tested materials, a significant linear correlation between the measured albedo and surface temperature was proven. Ultimately, a foundation for thermal comparability between these materials was established.

High‐resolution perfusion imaging in rodents using pCASL at 9.4 T

AbstractAdequate perfusion is critical for maintaining normal brain function and aberrations thereof are hallmarks of many diseases. Pseudo‐Continuous Arterial Spin Labeling (pCASL) MRI enables noninvasive quantitative perfusion mapping without contrast agent injection and with a higher signal‐to‐noise ratio (SNR) than alternative methods. Despite its great potential, pCASL remains challenging, unstable, and relatively low‐resolution in rodents – especially in mice – thereby limiting the investigation of perfusion properties in many transgenic or other relevant rodent models of disease. Here, we address this gap by developing a novel experimental setup for high‐resolution pCASL imaging in mice and combining it with the enhanced SNR of cryogenic probes. We show that our new experimental setup allows for optimal positioning of the carotids within the cryogenic coil, rendering labeling reproducible. With the proposed methodology, we managed to increase the spatial resolution of pCASL perfusion images by a factor of 15 in mice; a factor of 6 in rats is gained compared to the state of the art just by virtue of the cryogenic coil. We also show that the improved pCASL perfusion imaging allows much better delineation of specific brain areas, both in healthy animals as well as in rat and mouse models of stroke. Our results bode well for future high‐definition pCASL perfusion imaging in rodents.

Experimental investigation of natural gas leakage and dispersion characteristics in utility tunnels under the effects of real facility layout and forced ventilation

Natural gas leakage occurring in underground utility tunnels usually poses a significant threat to public safety. In order to prevent unexpected fires and explosions, the evolution mechanism of natural gas leakage and dispersion in utility tunnels is urgently needed. In this study, an experimental apparatus was built to facilitate the understanding of leakage and dispersion dynamics in utility tunnels. Methane with a purity of 99.9% is used as a surrogate for natural gas. A schlieren imaging system with a Z-shaped optical path was designed to visualize the high-speed gas jet. The concentration distribution, alarm time, dilution efficiency, and gas jet image were analyzed under the effect of various facility layouts, ventilation, and leakage rates. The results show that the gas leakage and dispersion process can be divided into three zones: upwind zone, leak zone, and downwind zone, characterized by complicated airflow collision, high-speed get jets, and stable dilution dispersion respectively. Scenario analysis highlights the significance of key facilities, such as cable brackets, in experimental and numerical modeling due to their impact on dispersion trajectories. Higher ventilation rates prove beneficial in reducing peak concentration, hazardous areas, and enhancing purge efficiency. Conversely, higher leakage rates exacerbate the likelihood and severity of gas explosions. Alarm time exhibits a V-shaped distribution relative to the leak point, while purge time correlates positively with sensor positions. These findings are of practical importance in enhancing quantitative risk assessment and designing mitigation strategies for gas leakage accidents, which helps to improve the safety-risk-control capabilities of utility tunnels.