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Comparison of Deuterium Metabolic Imaging with FDG PET in Alzheimer Disease
No full textBackground: The approval of amyloid-targeting therapies has made it increasingly important to differentiate Alzheimer disease (AD) from other causes of dementia. Dysfunctional glucose metabolism is a recognized pathophysiological element in AD that may be visualized with spectroscopic MRI of deuterated glucose and its metabolites, also known as deuterium metabolic imaging (DMI). Purpose: To explore the potential of DMI as a diagnostic tool for AD. Materials and Methods: In this prospective cross-sectional study, participants with newly diagnosed AD and age-matched controls were recruited from April to October 2023. DMI was performed with a 3-T system equipped with a proton/deuterium head coil following oral consumption of 75 g of deuterated glucose. Clinical fluorodeoxyglucose (FDG) PET data were acquired from patient records for comparison. The predefined primary outcome, the ratio between lactate and glutamine plus glutamate (Glx) at DMI, was analyzed using age-corrected linear mixed-effect models. Results: Ten participants with AD (mean age, 72 years ± 6 [SD]; six women) and five age-matched healthy controls (mean age, 68 years ± 7; four men) were included. The primary analysis revealed no evidence of a difference in the ratio of lactate to Glx between participants with AD and controls (P = .24 across all regions of interest). Exploratory analyses revealed that participants with AD had reduced signals for medial temporal lactate (0.7 ± 0.2 vs 0.5 ± 0.1, P = .04) and Glx (0.5 ± 0.03 vs 0.48 ± 0.05, P = .03) compared with controls. Finally, a strong correlation (r = 0.73) was observed between DMI and FDG PET. Conclusion: This study did not find evidence to support a shift from oxidative to anaerobic metabolism in AD. Exploratory analyses revealed a decrease in glucose metabolism in the medial temporal lobe. In extension hereof, a similar distribution of low DMI metabolism and decreased FDG PET glucose uptake was observed.</p
A Parametric Study of an Indirect Evaporative Cooler Using a Spray Dryer Model
Get PDFIndirect evaporative coolers (IECs) are becoming a viable alternative to the more energy-intensive traditional HVAC systems for space cooling, especially in arid regions. In this work, a recently developed computational model of an IEC was used to conduct a parametric study. The model employs a spray dryer model to track the flow path and evaporation rate of droplets. The key parameters investigated were the temperature of the droplets, a bypass effect where the amount of exhaust air and water was reduced to as low as 10%, and the length of the heat exchanger. The results suggest that the wet bulb efficiency could be increased from the previously observed 35% to 72.5% if the water temperature is decreased to 16 °C. In order to drastically increase the performance, the heat exchanger length should be increased from 50 cm to 100 cm, which could still end up in a more compact design overall as fewer plates are required. The bypass study resulted in peak performance when 40% of the secondary air flow was used as working air in conjunction with a proportional reduction in water usage. Overall, the computational model has been employed in an attempt to reduce the bulkiness, increase the efficiency and reduce the water consumption of such a system
Frequency-stable beamforming over wireless MIMO channels
Get PDFThe Wigner–Smith (WS) matrix provides a powerful framework for characterizing the delay properties of scattering processes in time-invariant propagation environments. When the underlying scattering matrix is unitary, the WS matrix is guaranteed to be Hermitian, with real-valued eigenvalues known as proper delay times. These eigenvalues quantify the delay experienced by narrowband wave packets shaped according to the corresponding WS eigenvectors (also known as "principal modes"). Originally introduced in quantum mechanics to describe collision-induced delays in particle scattering, the WS matrix has since found broader applications. We explore its relevance for wireless multiple-input multiple-output (MIMO) systems using antenna arrays on both sides of the radio link. In particular, we highlight that the principal modes of the WS matrix exhibit robustness to small frequency shifts, even for the strongly non-unitary scattering matrices commonly encountered in wireless communication scenarios
Modelling of self-heating and parametric study based on CFD in a 3D biomass pile
No full textLarge-scale biomass piles in an open-air environment are prone to self-heating, which can induce spontaneous combustion under certain conditions. This poses significant safety risks for the storage and utilization of biomass fuels. Developing a prediction tool for self-heating within stored biomass is a cost-effective strategy to mitigate these risks. This study proposes a robust and reliable modeling tool to predict self-heating in biomass storage piles. A computational fluid dynamics (CFD) model has been developed using Ansys Fluent, incorporating the three primary mechanisms of biomass self-heating (i.e., chemical, physical, and microbial activities). These mechanisms are mathematically formulated and integrated into the CFD model through user-defined functions. CFD simulations are performed for a well-defined biomass storage scenario to validate the model, using experimental data from the literature. The simulation results show good agreement with the measured temporal evolution of temperatures in the pile. Within the initial 30 days, intense microbial activity leads to significant heat generation from metabolic processes, causing a rapid temperature rise within the pile from 29 °C to 62 °C. Subsequently, as the temperature peaks, microbial activities diminish, leading to a decrease in temperature. With no significant contribution from low-temperature oxidation and physical processes, the heat released from the biomass pile is negligible. Based on this investigation, we further explore various cases to examine the factors that affect the self-heating of biomass piles by altering the external environment or the characteristics of the biomass piles and particles themselves. The results indicate that these factors have varying degrees of influence on the biomass pile, whether in the first or second stage of the self-heating process. When the external ambient temperature exceeds 20 °C or the biomass particles are smaller, the high temperature inside the biomass pile is sustained for an extended period, which is highly unfavourable for its safe storage. The accurate simulation of the self-heating process in biomass piles is significant for safe biomass storage. Such virtual testing will not only enhance our understanding of self-heating in biomass piles but also facilitate the formulation of practical guidelines for the secure storage of solid biomass fuels.</p
Direct Numerical Simulation of Drag Model of Cylindrical Biomass Particles With High Aspect Ratio
No full textBiomass particles are commonly utilized for co-firing in pulverized coal boilers for power generation, exhibiting characteristics of large particle sizes and elongated cylindrical shapes. However, the literature lacks relevant drag models associated with such particles. This study focuses on commonly encountered biomass straw particles (elongated cylinders with an aspect ratio of L/D=9:1) and employs the open-source computational fluid dynamics software OpenFOAM to conduct a direct numerical simulation of the fixed particle swirling characteristics and drag model. The investigation spans a broad range of Reynolds numbers (10≤Re ≤800) and incident angles (0°≤θ≤90°), yielding a new correlation equation for the drag coefficient (CD). The results show that: 1) Within the Reynolds number range of (500≤ Re≤800) and incident angles θ≤15°, the swirling flow around the cylinder exhibits instability. The projected area of the cylinder starts decreasing after reaching its maximum at θ=15°. Changes in incident angle lead to a smaller recirculation zone formation on the leeward side, thereby reducing the pressure difference on the surface of the particle drag. CD decreases with an increase in the incident angle, and the elevation of Reynolds number diminishes the differential pressure drag, leading to a decreasing trend in CD. 2) For elongated cylindrical particles, the widely used correlation equation by Hölzer and Sommerfeld tends to overestimate the drag coefficient at intermediate angles within the range of 0°≤θ≤90°, and this overestimation intensifies with an increase in Reynolds number. For long cylindrical particles, the general correlation equation of Hölzer and Sommerfeld overestimates the drag coefficient of the intermediate angle of 0°≤θ≤90°, and the degree of which increases with the increase of the Reynolds number. 3) The newly derived drag correlation equation exhibits an average relative error of 1.8% and a mean square error (MSE) of 6.7×10−2 when compared to the original data. This equation provides a more accurate drag model for the gas-solid two-phase dynamics of large biomass particles in elongated cylinders.</p
Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
No full textManufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making their verification a critical quality assessment link. A fundamental challenge is to decouple electrochemical interactions for establishing a quantitative mapping from electrochemical parameters to macro battery performance. Here, we show that the proposed physics-informed learning model can quantify and visualize temporally resolved thermodynamic and kinetic parameters from field accessible electric signals, facilitating a non-destructive degradation pattern decoupling. The lifetime trajectory prediction is 25 times faster than the traditional capacity calibration test while retaining a 95.1% average accuracy across temperatures, underpinned by projected electrochemical data from early cycle observations which have not yet been established. We rationalize this predictability to the interpretation of statistical insights from material-agnostic featurization, excited by a multistep charging scheme with different current intensities and their switching conditions. The waste management of defective prototypes is enabled by statistically and non-destructively interpreting internal electrochemical states, demonstrating a 19.76 billion USD defective material recycling market by 2060. This paper highlights the potential of revisiting electrochemical degradation behaviors using physicsinformed learning and dynamic current excitations, facilitating next-generation battery manufacturing, reuse, and recycling sustainability.</p
Optimal battery charging of electric flying cars considering quantified safety and economic costs
No full textSafe and efficient charging of lithium-ion batteries is crucial for the economic viability and convenience of electric flying cars in urban air mobility applications. This article proposes a multi-objective charging optimization framework for large-format batteries based on a physics-data enhanced battery electrothermal-degradation model, a charging economic model, and a battery safety model. Specifically, the coupled battery model emulates the battery's electrical, thermal, and degradation behavior during charging. The economic model evaluates the equivalent cost of battery degradation and the charging electricity cost. The safety model quantifies the battery's state of safety and imposes the constraints for safe charging. The multi-objective charging optimization was achieved with an improved dynamic multi-swarm particle swarm optimization algorithm. Four typical charging strategies were investigated in detail, and the potential influencing factors of optimal battery charging were analyzed. Numerous simulation results indicate that the proposed balanced charging strategy, which achieves the expectant charging goal within 10 min, mitigates degradation by 12 %, reduces cost by 5.56 %, and improves safety by 12.37 % compared to the minimum-time charging.</p
Comparative efficacy of intravitreal anti-VEGF therapy for neovascular age-related macular degeneration:A systematic review with network meta-analysis
No full textThe aim of this review was to evaluate the comparative efficacy of intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy for neovascular AMD. We searched 12 literature databases for randomised clinical trials (RCT) on anti-VEGF therapy for neovascular AMD and extracted data on: change from baseline to 12 months in best-corrected visual acuity (BCVA) and central retinal thickness (CRT), and cumulative number of injections at 12 months. The reference for comparison was monthly ranibizumab. Comparisons were made using network meta-analyses. Forty-nine RCTs including 23 257 eyes of 23 257 patients were included. No anti-VEGF drug or treatment regimen provided a better BCVA response compared to the reference. For CRT, small but statistically significant improvements over the reference were observed for brolucizumab 3 mg (−27.9 μm) or 6 mg (−38.1 μm) in loading dose (LD) then every 8–12 weeks, aflibercept 8 mg in LD then every 12 (−26.9 μm) or 16 weeks (−32.1 μm), faricimab 6 mg in LD then treat-and-extend (−18.1 μm) and aflibercept 2 mg in LD then every 8 weeks (−11.3 μm). For the cumulative number of injections, a range of anti-VEGF drugs and treatment regimens provided a statistically significant and clinically meaningful reduction compared to the reference. When results are considered simultaneously, faricimab 6.0 mg or aflibercept 8.0 mg in a treatment regimen with an LD followed by either a treat-and-extend regimen or a fixed 12- or 16-week regimen appears to provide the optimal balance between visual outcomes, anatomical outcomes and the lowest treatment burden. However, studies of the long-term efficacy of newer anti-VEGF drugs are warranted.</p
No snorkel, no sample, no worries – the camera’s already rolling
Get PDFUnderwater camera observations are a game-changer for marine biologists. They allow scientists to study marine life in its natural habitat, with minimal disturbance. Cameras provide continuous, repeatable data that can capture both ecological snapshots and long-term ecological trends. From species identification to behaviour and habitat monitoring, the possibilities are vast.Integrating AI with underwater cameras enhances accuracy and detection in marine research. AI can analyse recordings more efficiently than the human eye, identifying species, behaviours, human footprints, and environmental changes that might be missed. By automating tasks such as species classification and behavioural monitoring, AI allows scientists to focus on interpreting the data rather than sifting through hours of video.What marine biologist researchMonitor species diversity: identifying fish, invertebrates, and other marine life.Study behaviour: foraging, mating, predator-prey interactions, and movement.Assess habitats and their conditions: structure, health, and coverage.Track human impacts: fishing activity, pollution, and installation or removal of barriers.<br/