Constituents of Human Particle, Microbial and Chemical Emissions and Exposures in Indoor Environments: An experimental overview

This study presents the preliminary findings on the human contribution to particle, microbe and gas-phase chemical burden of indoor air, as well as the effect of ozone on malondialdehyde (MDA) levels, a biomarker of lipid peroxidation

Validation of four resistivity mixing models on field time lapse geoelectrical measurements from fine-grained soil undergoing freeze-thaw cycles

Resistivity mixing models relate porosity, phase composition and specific resistivities of ground materials to their bulk (effective) electrical properties. These models were typically derived for calculating hydrocarbon saturation from geophysical logs. In permafrost monitoring applications, they have been used to link ground electrical response to its phase composition, with focus on unfrozen water vs. ice content, and to derive changes in ground ice content from repeated resistivity acquisitions. Such quantitative interpretations rely on validity of the mixing models in a context different from the one they were derived in. To increase the reliability of the permafrost forecasts that are based on repeated resistivity surveys, we undertook validation of four selected resistivity mixing model formulations: i) the original Archie's law, ii) the Archie's law with an ice-content dependent cementation exponent m (Archie-M), iii) a modification of the Archie's law for multiple conducting phases (Archie-N), and iv) the geometric mean model (GM). The model application context was permafrost monitoring and fate forecasting on natural fine-grained soil undergoing cycles of freezing and thawing, based on indirect (geophysical), in-situ time-lapse resistivity measurements. The purpose of the calibrated resistivity models was to derive the phase composition of the ground from in-situ resistivity measurements, with acceptable quantitative reliability, notably with respect to the amount and changes of ice and water content. In our validation framework, daily temperature-dependent soil phase distribution was converted into an effective resistivity distribution of the ground using each of the four resistivity mixing models. From the effective resistivity model, an apparent resistivity response was forward calculated and compared to time-lapse field apparent resistivity measurements from a permafrost monitoring field site. The performance metrics were i) the root mean square error between the forward-calculated and field-measured apparent resistivities throughout the freeze-thaw season, ii) the percentage of field apparent resistivity data explained by each resistivity model, and iii) the plausibility of the calibrated model parameter estimates. We found that despite different current conducting mechanisms involved in each of the resistivity mixing model formulations, the quantitative performance of the four evaluated models was very similar. The four models typically reproduced the field-measured resistivity variations within one to two standard deviations (STD) of the field measurements, depending on the time of the year and depth in the soil profile. In the active layer, the Archie-M model most consistently reproduced the field data within 1 STD throughout the freezing and frozen periods of the year (September – May). Meanwhile, the GM best matched the actual values of resistivities during freezing. The GM also recovered porosities of the three model layers close to the true values measured on borehole samples. All the tested models were challenged by accurately simulating the thawing period – overestimating resistivities in the temperature range from −5 °C to −2 °C and underestimating them between −2 °C and thawing point. Consequently, the choice between the models should depend on the specifics of a particular application, such as available calibration data, desired parameters or ground properties to resolve, sensitivity of the modeling framework etc. An application-specific validation of several resistivity mixing models and quantification of performance of the chosen resistivity model may be called for. Additionally, the possibility of using different mixing model and water content parameterizations should be investigated, to adequately address complex ground resistivity structures and phase change processes typical of permafrost ground.</p

Life cycle assessment of lithium ion battery from water-based manufacturing for electric vehicles

Lithium ion batteries produced using the water-based manufacturing processes, as a greener technology, have great potential to be used in future electric vehicles (EVs). A cradle-to-grave life cycle assessment model configured for actual EV applications has been developed for the water-based manufactured lithium nickel manganese cobalt oxide (NMC)-graphite battery pack. Experimental and modeling results cover raw material extraction and processing, water-based battery manufacturing processes, battery usage during EV driving, and direct recycling at End-of-Life. The ReCiPe method is employed to investigate the environmental impacts of the water-based battery pack and benchmark it against the impacts of a conventional N-methyl-2-pyrrolidone (NMP)-based battery pack with the same mass. The cradle-to-grave energy consumption of the studied water-based battery pack is 0.976 MJ/km EV driving, equivalent to a 4.5% reduction over the NMP-based battery pack. Aside from energy usage, we find reductions in all environmental impact categories (3.0%∼85%) compared to the conventional battery pack

Does the outside view affect the luminous and thermal perception? A preliminary study

This study explores whether differences in urban views affect thermal and visual perception. Experimental sessions were conducted in two identical office rooms with controlled temperatures, naturally lit but with different window views. Split into two groups by temperature (between subjects), the participants were exposed to two window views (within subjects). The results of this preliminary study indicate that the thermal and visual perception were not significantly different between the window views

Flexible operation, optimisation and stabilising control of a quench cooled ammonia reactor for power-to-ammonia

This paper discusses the operation of an ammonia reactor for a Power-to-Ammonia (P2A) plant. We develop a dynamic model for an ammonia reactor system consisting of a three-bed quench cooled adiabatic reactor and a feed-effluent heat exchanger. The reactor bed model is formulated as a differential algebraic equations (DAE) system. We use the thermodynamic software Thermolib for rigorous modeling of the thermodynamic functions in the high pressure ammonia reactor. We present a case study of an ammonia synthesis loop in a P2A plant connected to a 250 MW renewable energy source with a capacity factor of 0.4. Static optimization and stability analysis are performed for the reactor system, which located the optimal operating point close to instability. The dynamic simulations confirm the unstable operating regions as severe oscillations arise. A fluctuating energy supply from renewable sources requires the ammonia reactor to operate over a wide operating window from 20%–120% of nominal capacity. We formulate a realistic strategy for varying the supply of H2 and (load) to the synthesis loop depending on the available energy. Open-loop simulations show that varying the synthesis feed flow cause oscillations in the ammonia reactor system. Therefore, we propose a regulatory control structure for stabilising the ammonia reactor. The optimisation algorithm determines the reactor set-point state by updating at changes to the synthesis loop load. Hereby, we achieved fast control and close tracking of the set-points for the ammonia reactor

Ce³⁺-based phosphor converter enabling laser lighting to attain both high CRI and high luminous efficacy

Eu2+ doped-CaAlSiN3 possesses broad red emission, enabling a phosphor-converted lighting device to achieve a high color rendering index (CRI) and proper color temperature. However, CaAlSiN3:Eu2+ exhibits relatively slow decay (∼1 μs) and intense re-absorption of luminescence from green/yellow emitters, thereby causing optical saturation and reducing the luminous efficacy. Here, we fabricated a novel phosphor converter comprising Lu3Al5O12:Ce3+ and Y1.3Gd1.6Al5O12:Ce3+ powders. The typical sample, when excited by a blue laser, exhibited a high luminous efficacy of 231 lm/W and a high saturation threshold of 22.2 W/mm2, resulting in a high luminous exitance of 695 lm/mm2. Importantly, the phosphor converter produced a broad emission band that included sufficient cyan and red components, resulting in a full width at half maximum (FWHM) of 134 nm and a high CRI of 81. With this excellent balance between CRI and luminous efficacy, the reported phosphor converter can significantly expand the range of laser lighting applications.</p

Co-enhancing effects of zero valent iron and magnetite on anaerobic methanogenesis of food waste at transition temperature (45 °C) and various organic loading rates

Deoiling of food waste (FW) after hydrothermal pretreatment occurs at high temperatures, and more energy is required for substrate cooling before the anaerobic digestion (AD) process. AD at the transition temperature (for example 45 °C) is good for energy saving and carbon emission reducing when treating deoiling FW. However, the metabolic activity of methanogens must increase at the transition temperatures. This study proposes the use of zero-valent iron (Fe0) and magnetite (Fe3O4) to boost CH4 yield from deoiling FW. The results showed a co-enhancing effect on CH4 yield upgradation when using Fe0 and Fe3O4 simultaneously, and the highest CH4 yield reached 536.23 mLCH4/gVS, which was 67.5 % higher than that of Fe0 alone (320.14 mLCH4/gVS). In addition, a high organic loading was favorable for increasing the CH4 yield from deoiling FW. Microbial diversity analysis suggested that the dominant methanogenic pathway at 45 °C was hydrogenotrophic methanogenesis. Herein, a potential metabolic pathway analysis revealed that the co-enhancing effects of Fe0 and Fe3O4 enhanced syntrophic methanogenesis and possibly boosted electron transfer efficiency

Democratizing uncertainty quantification

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.</p

A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection

Electricity theft is a critical issue in smart grids, leading to significant financial losses for utilities and compromising the stability and reliability of the power system. Existing centralized methods for electricity theft detection raise privacy and security concerns due to the need for sharing sensitive customer data. To address these challenges, we propose HeteroFL, a novel heterogeneous federated learning framework for privacy-preserving electricity theft detection in smart grids. HeteroFL enables retailers to collaboratively train a global model without sharing their private data, while accounting for the class imbalance problem prevalent in electricity theft datasets. We introduce a data partitioning and aggregation scheme that assigns different weights to classes, ensuring a balanced contribution and representation of each class in the global model. In addition, our framework leverages the CKKS homomorphic encryption scheme to perform secure computations on encrypted parameters and employs a CNN-LSTM model to capture the spatial and temporal dependencies in electricity consumption patterns. We evaluate HeteroFL using a real-world smart grid dataset and demonstrate its effectiveness and efficiency in detecting energy theft. Furthermore, we analyze the robustness and perform ablation studies to validate the framework's stability and identify the contributions of its key components. Although the impact of approximation errors introduced by the CKKS scheme on the CNN-LSTM model's performance requires further investigation, our framework presents a promising solution for privacy-preserving and accurate electricity theft detection in smart grids using heterogeneous federated learning.</p

A laboratory and theoretical framework for systematic non-equilibrium turbulence studies

The cornerstone assumption of equilibrium of the small and intermediate scales in the classical view of turbulence (K41 - the combined efforts of Kolmogorov, Batchelor and Richardson) is under ever increased scrutiny. Although the K41 based models do appear to apply well to some flows, there exist many important flows that are problematic for these turbulence models. In particular, it is interesting to note that the most challenging applications appear to have one thing in common - rapid changes of the flow in the mean in time and/or space. It is thus interesting to systematically investigate what the bounds of validity of the classical K41-view of turbulence are, if any. And if the K41-picture of turbulence does indeed break down, what are the non-linear spectral energy transfer mechanisms that lead to nonequilibrium turbulence behavior (local vs. non-local)? Does the non-linear energy exchange between scales divert from the classically assumed Richardson cascade? And is the constancy of the spectral flux across the inertial range interrupted? In order to answer these questions, a new facility for the systematic study of non-equilibrium turbulence in a controlled setting has been established along with an accompanying theoretical framework that is tailored for addressing these specific issues