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The use of hydrogen generated from renewable energy sources is expected to become an essential component of a low-carbon, environmentally friendly energy supply, spurring the worldwide development of hydrogen technologies. This title provides practical, expert-driven information on modern sensors for hydrogen and other gases as well as physical parameters essential for safety and process control in hydrogen technologies. It illustrates how sensing technologies can ensure the safe and efficient implementation of the emerging global hydrogen market.

The increase in the spectral width of an initially monodisperse population of cloud droplets in homogeneous isotropic turbulence is investigated by applying a finite-difference fluid flow model combined with either Eulerian bin microphysics or a Lagrangian particle-based scheme. The turbulence is forced applying a variant of the so-called linear forcing method that maintains the mean turbulent kinetic energy (TKE) and the TKE partitioning between velocity components. The latter is important for maintaining the quasi-steady forcing of the supersaturation fluctuations that drive the increase in the spectral width. We apply a large computational domain (643 m3), one of the domains considered in Thomas et al. (2020). The simulations apply 1 m grid length and are in the spirit of the implicit large eddy simulation (ILES), that is, with small-scale dissipation provided by the model numerics. This is in contrast to the scaled-up direct numerical simulation (DNS) applied in Thomas et al. (2020). Two TKE intensities and three different droplet concentrations are considered. Analytic solutions derived in Sardina et al. (2015), valid for the case when the turbulence integral timescale is much larger than the droplet phase relaxation timescale, are used to guide the comparison between the two microphysics simulation techniques. The Lagrangian approach reproduces the scalings relatively well. Representing the spectral width increase in time is more challenging for the bin microphysics because appropriately high resolution in the bin space is needed. The bin width of 0.5 µm is only sufficient for the lowest droplet concentration (26 cm−3). For the highest droplet concentration (650 cm−3), an order of magnitude smaller bin size is barely sufficient. The scalings are not expected to be valid for the lowest droplet concentration and the high-TKE case, and the two microphysics schemes represent similar departures. Finally, because the fluid flow is the same for all simulations featuring either low or high TKE, one can compare point-by-point simulation results. Such a comparison shows very close temperature and water vapor point-by-point values across the computational domain and larger differences between simulated mean droplet radii and spectral width. The latter are explained by fundamental differences in the two simulation methodologies, numerical diffusion in the Eulerian bin approach and a relatively small number of Lagrangian particles that are used in the particle-based microphysics.

This paper presents a novel methodology to use direct numerical simulation (DNS) to study the impact of isotropic homogeneous turbulence on the condensational growth of cloud droplets. As shown by previous DNS studies, the impact of turbulence increases with the computational domain size, that is, with the Reynolds number, because larger eddies generate higher and longer-lasting supersaturation fluctuations that affect growth of individual cloud droplets. The traditional DNS can only simulate a limited range of scales because of the excessive computational cost that comes from resolving all scales involved, that is, from large scales at which the turbulent kinetic energy (TKE) is introduced down to the Kolmogorov microscale, and from following every single droplet. The novel approach is referred to as the “scaled-up DNS”. The scaling up is done in two parts, first by increasing both the computational domain and the Kolmogorov microscale and second by using super-droplets instead of real droplets. To ensure proper dissipation of TKE and scalar variance at small scales, molecular transport coefficients are appropriately scaled up with the grid length. For the scaled-up domains, say, meters and tens of meters, one needs to follow billions of real droplets. This is not computationally feasible, and so-called super-droplets are applied in scaled-up DNS simulations. Each super-droplet represents an ensemble of identical real droplets, and the number of real droplets represented by a super-droplet is referred to as the multiplicity attribute. After simple tests showing the validity of the methodology, scaled-up DNS simulations are conducted for five domains, the largest of 643 m3 volume using a DNS of 2563 grid points and various multiplicities. All simulations are carried out with vanishing mean vertical velocity and with no mean supersaturation, similarly to past DNS studies. As expected, the supersaturation fluctuations as well as the spread in droplet size distribution increase with the domain size, with the droplet radius variance increasing in time t as t1∕2 as identified in previous DNS studies. Scaled-up simulations with different multiplicities document numerical convergence of the scaled-up solutions. Finally, we compare the scaled-up DNS results with a simple stochastic model that calculates supersaturation fluctuations based on the vertical velocity fluctuations updated using the Langevin equation. Overall, the results document similar scaling to previous small-domain DNS simulations and support the notion that the stochastic subgrid-scale model is a valuable tool for the multi-scale simulation of droplet spectral evolution applying a large-eddy simulation model.

Thermodynamic soundings for pre-monsoon and monsoon seasons from the Indian subcontinent are analysed to document differences between convective environments. The pre-monsoon environment features more variability for both near-surface moisture and free-tropospheric temperature and moisture profiles. As a result, the level of neutral buoyancy (LNB) and pseudo-adiabatic convective available potential energy (CAPE) vary more for the pre-monsoon environment. Pre-monsoon soundings also feature higher lifting condensation levels (LCLs). LCL heights are shown to depend on the availability of surface moisture, with low LCLs corresponding to high surface humidity, arguably because of the availability of soil moisture. A simple theoretical argument is developed and showed to mimic the observed relationship between LCLs and surface moisture. We argue that the key element is the partitioning of surface energy flux into its sensible and latent components, that is, the surface Bowen ratio, and the way the Bowen ratio affects surface buoyancy flux. We support our argument with observations of changes in the Bowen ratio and LCL height around the monsoon onset, and with idealized simulations of cloud fields driven by surface heat fluxes with different Bowen ratios.

A large convection cloud chamber has been proposed for exploring aerosol–cloud–drizzle interactions under well‐controlled turbulent conditions. Recent theoretical and numerical studies suggest that a convection cloud chamber with two heated and two cooled sidewalls can significantly enhance the liquid water content and thus benefit drizzle initiation. However, a chamber with such a sidewall configuration develops stable stratification and extremely weak turbulence therein. In this study, we conduct large‐eddy simulations of a tall convection chamber with five different sidewall configurations consisting of alternating warm and cold patches. For each configuration, the total surface area of warm patches equals that of cold patches, resulting in the same expected cloud‐free supersaturation based on a flux budget model. Results show that changing the sidewall configuration, while keeping all other factors constant, can substantially enhance turbulent mixing and improve the uniformity of thermodynamic and cloud microphysical properties in the bulk region of the chamber. In addition, turbulence strength is positively correlated with liquid water content and negatively correlated with cloud droplet number concentration, consistent with theoretical predictions. Our results highlight the advantage of building a large cloud chamber using modular patches with individually controllable temperature and humidity to achieve well‐mixed conditions. Plain Language Summary Theoretical and numerical studies have suggested that cloud droplets in a tall convection cloud chamber with a height of about 10 m can collide with each other to form drizzle drops naturally—a key, but still not fully understood, process for warm rain precipitation. Due to the large height‐to‐width ratio, a previous study shows that a tall convection chamber with two hot sidewalls and another two cold sidewalls is needed to achieve high supersaturation and liquid water content that can benefit drizzle initiation. However, such a sidewall configuration would lead to weak turbulence and stable stratification in the bulk region of the chamber. This study shows that using tiled sidewalls consisting of alternating hot and cold patches can enhance turbulent mixing and the uniformity of cloud microphysical properties, while keeping other factors constant. In addition, increasing turbulent strength increases liquid water content but decreases cloud droplet number concentration, and their scaling relationships from the simulation are consistent with theoretical predictions. Key Points We conduct large‐eddy simulations of a tall convection chamber with tiled sidewalls configured with alternating warm and cold patches Changing tiled sidewall patterns alone can enhance turbulent mixing and microphysical uniformity in a convection cloud chamber Enhanced mixing correlates positively with liquid water content but negatively with number concentration, matching theoretical predictions

Background Given the current emphasis on networks as vehicles for innovation and change in health service delivery, the ability to conceptualise and measure organisational enablers for the social construction of knowledge merits attention. This study aimed to develop a composite tool to measure the organisational context for evidence-based practice (EBP) in healthcare. Methods A structured search of the major healthcare and management databases for measurement tools from four domains: research utilisation (RU), research activity (RA), knowledge management (KM), and organisational learning (OL). Included studies were reports of the development or use of measurement tools that included organisational factors. Tools were appraised for face and content validity, plus development and testing methods. Measurement tool items were extracted, merged across the four domains, and categorised within a constructed framework describing the absorptive and receptive capacities of organisations. Results Thirty measurement tools were identified and appraised. Eighteen tools from the four domains were selected for item extraction and analysis. The constructed framework consists of seven categories relating to three core organisational attributes of vision, leadership, and a learning culture, and four stages of knowledge need, acquisition of new knowledge, knowledge sharing, and knowledge use. Measurement tools from RA or RU domains had more items relating to the categories of leadership, and acquisition of new knowledge; while tools from KM or learning organisation domains had more items relating to vision, learning culture, knowledge need, and knowledge sharing. There was equal emphasis on knowledge use in the different domains. Conclusion If the translation of evidence into knowledge is viewed as socially mediated, tools to measure the organisational context of EBP in healthcare could be enhanced by consideration of related concepts from the organisational and management sciences. Comparison of measurement tools across domains suggests that there is scope within EBP for supplementing the current emphasis on human and technical resources to support information uptake and use by individuals. Consideration of measurement tools from the fields of KM and OL shows more content related to social mechanisms to facilitate knowledge recognition, translation, and transfer between individuals and groups.

This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a nonturbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large-eddy simulation within a triply periodic computational domain of 64 3 m 3 . We consider two monodisperse CCN. Small CCN have a dry radius of 0.01 μ m and a corresponding activation (critical) radius and critical supersaturation of 0.6 μ m and 1.3%, respectively. Large CCN have a dry radius of 0.2 μ m and feature activation radius of 5.4 μ m and critical supersaturation 0.15%. CCN are assumed in 200-cm −3 concentration in all cases. Mean cloud-base updraft velocities of 0.33, 1, and 3 m s −1 are considered. In the nonturbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a nonzero spectral width above the cloud base, of the order of 1 μ m, especially in the case of small CCN and weak mean cloud-base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud-base activation.

All-trans retinoic acid (atRA) regulates gene expression and is used to treat acute promyelocytic leukemia. Attempts to use atRA in breast cancer without a stratification strategy have resulted in limited overall effectiveness. To identify biomarkers for the treatment of triple-negative breast cancer (TNBC) with atRA, we characterized the effects of atRA on the tumor growth of 13 TNBC cell lines. This resulted in a range of effects that was not predictable based on previously hypothesized predictors of response, such as the levels of atRA nuclear shuttling proteins fatty acid binding protein 5 (FABP5) and cellular retinoic acid binding protein 2 (CRABP2). Transcriptional profiling revealed that atRA induced distinct gene expression changes in the sensitive versus resistant cell lines that were mostly independent of the presence of retinoic acid response elements (RAREs) or peroxisome proliferator response elements (PPREs). Given the importance of DNA methylation in regulating gene expression, we hypothesized that differential DNA methylation could predict the response of TNBCs to atRA. We identified over 1400 sites that were differentially methylated between atRA resistant and sensitive cell lines. These CpG sites predicted the response of four TNBC patient-derived xenografts to atRA, and we utilized these xenografts to refine the profile and identified that as many as 17% of TNBC patients could benefit from atRA treatment. These data illustrate that differential methylation of specific CpGs may be useful biomarkers for predicting the response of patient tumors to atRA treatment.

Background The implementation of strategies to monitor and enhance treatment fidelity is of paramount importance in trials of complex interventions. A recent framework published by the National Institutes of Health Behavior Change Consortium recommends addressing five areas of treatment fidelity, one of which is delivery of treatment. This study aimed to explore fidelity to treatment delivery of the ICONS intervention (a systematic voiding programme [SVP]). This included exploring the feasibility of a method to assess fidelity to treatment delivery and collecting preliminary evidence of the level of fidelity to SVP delivery in order to inform strategies for improving fidelity in a future trial. Methods Delivery of treatment was recorded by nurses through completion of daily clinical logs, which included: a voiding interval, proposed voiding times and actual voiding times. The a priori method for assessment of fidelity – comparing actual voiding times with proposed voiding times – was trialled on a small amount of data. Due to errors in documentation of the voiding intervals and proposed voiding times it was not possible to assess fidelity directly as planned. A new method was devised, which included identification of ‘key quality indicators’. Results This new approach to assessing fidelity used key quality indicators based upon presence of the data needed to make the comparison between proposed and actual voiding times. The proportion of clinical logs with correct documentation of voiding intervals and proposed voiding times was less than 40 %. For clinical logs with correct documentation, an actual voiding time within 30 min of the proposed voiding time was identified on approximately 55 % of occasions. Conclusions Lessons learnt from this study have implications for the future ICONS definitive trial and for other trials of complex interventions. Implementation of a complex intervention may often deviate from what is intended. While careful consideration should be given to the best method of fidelity assessment, an iterative approach allowing flexibility to adapt pre-planned methods is recommended within feasibility trials. As fidelity to treatment delivery in the ICONS feasibility trial appeared to be relatively low, more attention to implementation strategies will be required in the definitive trial. Trial registration Identifier: ISRCTN08609907 ; date registered: 07/07/2010.

This paper examines the impact of cloud-base turbulence on activation of cloud condensation nuclei (CCN). Following our previous studies, we contrast activation within a nonturbulent adiabatic parcel and an adiabatic parcel filled with turbulence. The latter is simulated by applying a forced implicit large-eddy simulation within a triply periodic computational domain of 643m3. We consider two monodisperse CCN. Small CCN have a dry radius of 0.01μm and a corresponding activation (critical) radius and critical supersaturation of 0.6μm and 1.3%, respectively. Large CCN have a dry radius of 0.2μm and feature activation radius of 5.4μm and critical supersaturation 0.15%. CCN are assumed in 200-cm-3concentration in all cases. Mean cloud-base updraft velocities of 0.33, 1, and 3 m s-1are considered. In the nonturbulent parcel, all CCN are activated and lead to a monodisperse droplet size distribution above the cloud base, with practically the same droplet size in all simulations. In contrast, turbulence can lead to activation of only a fraction of all CCN with a nonzero spectral width above the cloud base, of the order of 1μm, especially in the case of small CCN and weak mean cloud-base ascent. We compare our results to studies of the turbulent single-size CCN activation in the Pi chamber. Sensitivity simulations that apply a smaller turbulence intensity, smaller computational domain, and modified initial conditions document the impact of specific modeling assumptions. The simulations call for a more realistic high-resolution modeling of turbulent cloud-base activation.