Range Of Scales Research Articles

Dynamic diversity is the answer to proxy failure.

We argue that a diverse and dynamic pool of agents mitigates proxy failure. Proxy modularity plays a key role in the ongoing production of diversity. We review examples from a range of scales.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Microscale Chaotic Mixing as a Driver for Chemical Reactions in Porous Media.

Mixing-induced reactions play a key role in a large range of biogeochemical and contaminant transport processes in the subsurface. Fluid flow through porous media was recently shown to exhibit chaotic mixing dynamics at the pore scale, enhancing microscale concentration gradients and controlling mixing rates. While this phenomenon is likely ubiquitous in environmental systems, it is not known how it affects chemical reactions. Here, we use refractive index matching and laser-induced fluorescence imaging of a bimolecular redox reaction to investigate the consequence of pore scale chaotic mixing on the reaction rates. The overestimation of measured reaction rates by the classical macrodispersion model highlights the persistence of incomplete mixing on the pore scale. We show that the reaction product formation is controlled by microscale chaotic mixing, which induces an exponential increase of the mixing interface and of the reaction rates. We derive a reactive transport model that captures experimental results and predicts that chaotic mixing has a first order control on reaction rates across a large range of time scales and Péclet and Damköhler numbers. These findings provide a new framework for understanding, assessing, and predicting mixing-induced reactions and their role on the fate and mobility of environmental compounds in natural porous media.

Illumination matters part IV: blackout and whiteout in flexible ureteroscopy - first report on a phenomenon observed by PEARLS

PurposeTo date, no study has evaluated effects of varying brightness settings on image quality from flexible ureteroscopes submerged in saline. The aim was to evaluate blackout and whiteout occurrences in an in-vitro kidney calyx model.Material and methodsWe evaluated a series of contemporary flexible ureteroscopes including the Storz Flex-Xc and Flex-X2s, Olympus V3 and P7, Pusen 7.5F and 9.2F, as well as OTU WiScope using a 3D-printed enclosed pink in-vitro kidney calyx model submerged in saline. Endoscopic images were captured with ureteroscope tip placed at 5 mm,10 mm and 20 mm distances. The complete range of brightness settings and video capture modes were evaluated for each scope. Distribution of brightness on a grayscale histogram of images was analyzed (scale range 0 to 255). Blackout and whiteout were defined as median histogram ranges from 0 to 35 and 220 to 255, respectively (monitor image too dark or too bright for the human eye, respectively).ResultsBlackout occurred with the P7, Pusen 7.5F, 9.2F and WiScope at all distances, and V3 at 20 mm - with lowest brightness settings. Whiteout occurred with Flex-X2s, V3 and P7 at 5 mm and 10 mm, as well as with V3 and P7 at 20 mm - mostly with highest brightness settings. The Flex-Xc had neither blackout nor whiteout at all settings and distances.ConclusionBlackout or whiteout of images is an undesirable property that was found for several scopes, possibly impacting diagnostic and therapeutic purposes during ureteroscopy. These observations form a guide to impact a urologist’s choice of instruments and settings.

Effects of Pressure and Characteristic Scales on the Structural and Statistical Features of Methane/Air Turbulent Premixed Flames

AbstractIn this study, the highly nonlinear and multi-scale flame-turbulence interactions prevalent in turbulent premixed flames are examined by using direct numerical simulation (DNS) datasets to understand the effects of increase in pressure and changes in the characteristic scale ratios at high pressure. Such flames are characterized by length-scale ratio (ratio of integral length scale and laminar thermal flame thickness) and velocity-scale ratio (ratio of turbulence intensity and laminar flame speed). A canonical test configuration corresponding to an initially laminar methane/air lean premixed flame interacting with decaying isotropic turbulence is considered. We consider five cases with the initial Karlovitz number of 18, 37, 126, and 260 to examine the effects of an increase in pressure from 1 to 10 atm with fixed turbulence characteristics and at a fixed Karlovitz number, and the changes to characteristic scale ratios at the pressure of 10 atm. The increase in pressure for fixed turbulence characteristics leads to enhanced flame broadening and wrinkling due to an increase in the range of energetic scales of motion. This further manifests into affecting the spatial and state-space variation of thermo-chemical quantities, single point statistics, and the relationship of heat-release rate to the flame curvature and tangential strain rate. Although these results can be inferred in terms of an increase in Karlovitz number, the effect of an increase in pressure at a fixed Karlovitz number shows differences in the spatial and state-space variations of thermo-chemical quantities and the relationship of the heat release rate with the curvature and tangential strain rate. This is due to a higher turbulent kinetic energy associated with the wide range of scales of motion at atmospheric pressure. In particular, the magnitude of the correlation of the heat release rate with the curvature and the tangential strain rate tend to decrease and increase, respectively, with an increase in pressure. Furthermore, the statistics of the flame-turbulence interactions at high pressure also show sensitivity to the changes in the characteristic length- and velocity-scale ratios. The results from this study highlight the need to accurately account for the effects of pressure and characteristic scales for improved modeling of such flames.

Solute micro-segregation profile and associated precipitation in cast Al-Mg-Si alloy

ABSTRACT The micro-segregation in the as-cast AA6082 aluminium alloy were investigated across a range of length scales using a combination of analytical electron microscopes. It is found that the micro-segregation bands form an inter-connected network following grain boundaries and inter-dendritic channels. The micro-segregation can be divided into major micro-segregation and minor micro-segregation; the former is mainly on the grain boundaries consisting of iron-bearing intermetallic; the latter occurs both, along the grain boundaries and inter-dendritic channels, consisting mainly of Mg and Si alloying elements. The atomic-scale imaging reveals that in the minor-segregation bands, the supersaturated solute concertation has formed precipitates that had either nucleated heterogeneously on the dislocation network or homogeneously inside the aluminium matrix. The heterogeneously nucleated precipitates in the dislocation lines are composed of a mixture of phases down the precipitation sequence meanwhile, the homogenously grown ones are discrete phases that appear at the early stages of the precipitation sequence.

A fuzzy synthetic evaluation approach to assess the risks associated with municipal waste management: Implications for sustainability

The rapid growth of industry, urban areas, and populations has substantially increased waste production worldwide. The inadequacy of robust waste management systems and limited resources poses significant challenges for many emerging economies, including Bangladesh, resulting in environmental contamination. Despite numerous academic studies on sustainable waste management, there is a noticeable gap in the literature concerning identifying, evaluating, and modeling critical risks associated with adopting sustainable municipal waste management systems, particularly in emerging economies like Bangladesh. This study, therefore, aims to fill this gap by identifying, categorizing, and assessing the crucial risks in implementing sustainable municipal waste management systems. Through an extensive review of existing literature and validation with expert feedback, this study employs a fuzzy synthetic evaluation method-based framework to prioritize the identified risks. The study calculates the probability of occurrence and degree of impact values to provide decision-makers with insights into the severity of different risk categories, aiding in case-by-case attention. Scores assigned on a 1 to 5 scale range from 2.59 to 4.14 for the various risk categories. Notably, ”Industrial challenges related” and ”Community and infrastructure requirements related” emerge as the most significant risk categories among those analyzed. This study contributes to the existing literature by employing fuzzy synthetic evaluation, a methodology rarely used to determine critical risks in municipal waste management. Furthermore, it is poised to offer valuable insights to industrialists and waste management authorities, empowering them to develop responsive strategies and contribute to the sustainable development of cities and urban communities.

Lagrangian characterization of the Southwestern Atlantic from a dense surface drifter deployment

The Southwestern Atlantic (SWA) is characterized by its large Eddy Kinetic Energy as the result of the confluence of two major western boundary currents, the northward flowing Malvinas Current (MC) and the southward flowing Brazil Current. The SWA study was addressed in the literature based on altimetry data, in situ measurements, regional models and ocean reanalysis. The present study constitutes the first effort to sample a portion of the SWA, with a dense drifter array (N = 62) deployment. The drifters, drogued at 15 m depths, were deployed across the MC and the Argentine Continental Shelf along two zonal transects located at 47°S and 47.25°S, between the 8th and the 9th of September 2021. Drifters were set to deliver their position every 10 and 60 minutes, providing accurate Lagrangian trajectories that provide information on a large range of space and time scales of the surface currents. Three regions are clearly identified based on the analysis of the speed of the drifters, of their trajectories and of the spectral density of their velocities: the continental shelf, the slope and the open ocean. The comparison of the trajectories of the drifters with satellite altimetry images shows that, in general, drifters follow mesoscale features that are detectable in satellite altimetry maps. The analysis of the drifter trajectories also allowed us the study of submesoscale features of the flow (1 to 10 km) that are not observable in satellite altimetry data. Comparison with cloud-free, high-resolution color images, shows that drifter trajectories organized by the mesoscale flow might also locally follow sub-mesoscale features. In frontal regions it was found that drifter velocities double satellite altimetry geostrophic velocities, which suggests that the dynamics at those regions is largely dominated by ageostrophic components. The ageostrophic Ekman component might explain the direction of the drifters when strong winds from a given direction prevail for several days and the drifters are not in a region with large sea surface height (SSH) gradients. The joint analysis of drifters’ trajectory and SSH clearly depicts that mesoscale features on the open ocean region control the cross-shelf exchanges between the MC and open ocean regions as well as the strength and width of the MC. Finally, the spatial density distribution of the drifters during the first hours after deployment and within a small eddy also allowed us to characterize the flow in terms of its divergence, vorticity and strain, indicating that the MC is geostrophic and has a jet-like behavior while the eddy is largely ageostrophic and has a dominant vorticity component over strain. We conclude observing that the analysis of a dense array of drifters provides valuable information of the flow that cannot be attained solely based on satellite data.

A new high-order shock-capturing TENO scheme combined with skew-symmetric-splitting method for compressible gas dynamics and turbulence simulation

The high-order shock-capturing scheme is one of the main building blocks for the simulation of the compressible fluid characterized by strong shockwaves and broadband length scales. However, the classical shock-capturing scheme fails to perform long-time stable and non-dissipative simulations since the quadratic invariants associated with the conservation equations cannot be conserved as a result of the inherent numerical dissipation. Additionally, the overall computational cost for classical shock-capturing schemes is quite expensive as a result of the time-consuming local characteristic decomposition and the nonlinear-weights computing process. In this work, based on a new efficient discontinuity indicator, which distinguishes the non-smooth high-wavenumber fluctuations and discontinuities from smooth scales in the wavenumber space, a paradigm of high-order shock-capturing scheme by recasting the non-dissipative skew-symmetric-splitting method with newly optimized dispersion property for smooth flow scales and invoking the nonlinear targeted ENO (TENO) schemes for non-smooth discontinuities is proposed. The resulting TENO-S scheme not only successfully performs long-time stable computations for smooth flows without numerical dissipation, but also recovers the robust shock-capturing capabilities with adaptive numerical dissipation. Without the necessity of parameter tuning case by case, extensive benchmark simulations involving a wide range of flow length scales and strong discontinuities demonstrate that the proposed TENO-S scheme performs significantly better than the straightforward deployment of WENO/TENO-family schemes with better spectral property and higher computational efficiency.

Do China and USA differ in the interrelationship between green bond and ESG markets?

Studying the interrelationship between green bond markets and ESG stock markets is crucial for promoting sustainable development in the face of environmental concerns. This paper explores this interrelationship across a range of time scales and market conditions by conducting wavelet analysis, quantile Granger causality test, and quantile-on-quantile regression (QQR) analysis, using data from January 5, 2015 to December 31, 2021 with 1705 observations of each variable. The results show that the green bond market and the ESG stock market in USA present a strong correlation, especially in response to external events. Additionally, we observe quantile Granger causality form green bond markets to ESG stock markets in both China and USA at all time scales. The QQR results suggest that the impact of green bond markets on ESG stock markets is significant under extreme market conditions in both countries. When both markets are in the lower quantiles, green bond markets would positively impact ESG stock markets in the short-term, but the impact turns out to be negative as the time term extends. Furthermore, the results in China and USA demonstrate some differences. When green bonds market is in the higher quantiles, green bonds markets would negatively impact ESG stock markets in both countries in the short-term. However, unlike that in China, the impact of in USA becomes positive as the term is longer, implying significant differences between the influencing mechanisms of the markets in these two countries.

Towards Energy-Balance Closure with a Model of Dispersive Heat Fluxes

In the last decades the energy-balance-closure problem has been thoroughly investigated from different angles, resulting in approaches to reduce but not completely close the surface energy balance gap. Energy transport through secondary circulations has been identified as a major cause of the remaining energy imbalance, as it is not captured by eddy covariance measurements and can only be measured additionally with great effort. Several models have already been developed to close the energy balance gap that account for factors affecting the magnitude of the energy transport by secondary circulations. However, to our knowledge, there is currently no model that accounts for thermal surface heterogeneity and that can predict the transport of both sensible and latent energy. Using a machine-learning approach, we developed a new model of energy transport by secondary circulations based on a large data set of idealized large-eddy simulations covering a wide range of unstable atmospheric conditions and surface-heterogeneity scales. In this paper, we present the development of the model and show first results of the application on more realistic LES data and field measurements from the CHEESEHEAD19 project to get an impression of the performance of the model and how the application can be implemented on field measurements. A strength of the model is that it can be applied without additional measurements and, thus, can retroactively be applied to other eddy covariance measurements to model energy transport through secondary circulations. Our work provides a promising mechanistic energy balance closure approach to 30-min flux measurements.

Enhancing flood verification using Signal detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors.

Modeling Molecular Hydrogen in Low-metallicity Galaxies

We use a suite of hydrodynamics simulations of the interstellar medium (ISM) within a galactic disk, which includes radiative transfer, a nonequilibrium model of molecular hydrogen, and a realistic model for star formation and feedback, to study the structure of the ISM and H2 abundance as a function of local ISM properties. We show that the star formation rate and structure of the ISM are sensitive to the metallicity of the gas with a progressively smoother density distribution with decreasing metallicity. In addition to the well-known trend of the H I–H 2 transition shifting to higher densities with decreasing metallicity, the maximum achieved molecular fraction in the ISM drops drastically at Z ≲ 0.2 Z ⊙ as the formation time of H2 becomes much longer than a typical lifetime of dense regions of the ISM. We present accurate fitting formulae for both volumetric and projected fH2 measured on different scales as a function of gas metallicity, UV radiation field, and gas density. We show that when the formulae are applied to the patches in the simulated galaxy, the overall molecular gas mass is reproduced to better than a factor of ≲1.5 across the entire range of metallicities and scales. We also show that the presented fit is considerably more accurate than any of the previous fH2 models and fitting formulae in the low-metallicity regime. The fit can thus be used for modeling molecular gas in low-resolution simulations and semi-analytic models of galaxy formation in the dwarf and high-redshift regimes.

The separate-universe approach and sudden transitions during inflation

The separate-universe approach gives an intuitive way to understand the evolution of cosmological perturbations in the long-wavelength limit. It uses solutions of the spatially-homogeneous equations of motion to model the evolution of the inhomogeneous universe on large scales. We show that the separate-universe approach fails on a finite range of super-Hubble scales at a sudden transition from slow roll to ultra-slow roll during inflation in the very early universe. Such transitions are a feature of inflation models giving a large enhancement in the primordial power spectrum on small scales, necessary to produce primordial black holes after inflation. We show that the separate-universe approach still works in a piece-wise fashion, before and after the transition, but spatial gradients on finite scales require a discontinuity in the homogeneous solution at the transition. We discuss the implications for the δN formalism and stochastic inflation, which employ the separate-universe approximation.

Quantifying wake dynamics subjected to stream vegetation patch elongation: The influence of patch-edge vortices

Vortices are generated across a wide range of scales due to the interaction between in-stream vegetation and surrounding flows, significantly influencing hydro-geomorphodynamics in earth surface water systems. Recent advance in vegetation patch hydrodynamics has revealed that the elongation of the middle channel patch can generate complex wake flow patterns by adjusting the bleed flow from the patch and triggering the patch-edge Kelvin–Helmholtz (KH) vortices. With a broader range of experimental configurations, this study reveals how the patch wake mixing is apparently strengthened by the presence of KH vortices, indicated by a larger steady wake velocity, a shorter steady wake length, and a damped energy of wake von Karman vortex. Furthermore, we quantify these characteristic metrics of patch wake behavior with and without the influence of KH vortices. Our findings provide insights into the role of vegetation-induced vortex interactions in regulating mixing processes, thereby promoting informed practices in environmental flows.

COVID-19-Related Emotional Impact and Personal Experiences, Family Income, and Sleep Hygiene of Early Appalachian Adolescents

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has negatively impacted the lifestyles and routines of adolescents, making it difficult to prioritize sleep. This has raised concerns regarding the sleep hygiene of young individuals, as good sleep is important for healthy youth development. Therefore, understanding how the pandemic has disproportionally affected the sleep of specific youth subgroups is essential for developing interventions to alleviate poor sleep and its adverse consequences. Methods: School-based survey data from the Young Mountaineer Health Study (YMHS) was used to create generalized estimating equation models to predict daytime sleepiness and the amount of sleep among early adolescents on weekday and weekend nights. The study included data from 2,322 students collected over three waves ~6 months apart. COVID-19-related emotional impact (scale range: 5–25) was considered the independent variable. Results: The average age of the participants in wave 1 was 11.50 (male, 44.1%). COVID-19-related emotional impact was positively associated with daytime sleepiness (estimate=0.25, <i>p</i><0.001) and increased the odds of receiving less than the recommended average amount of sleep on weekdays (odds ratio [OR]=1.05, <i>p</i><0.001) and weekends (OR=1.04, <i>p</i><0.001). Conclusions: Thus, we recommend targeting proper sleep interventions for individuals deeply affected by the pandemic and young individuals from lower-income families. Moreover, despite a decrease in the emotional impact of COVID-19 over time, its potential negative effects on sleep persisted. Therefore, efforts should focus on educating young individuals regarding the health benefits of proper sleep, with an emphasis on integrating this information into the health curricula of secondary schools in the Appalachian region.

Dual attention-enhanced SSD: A novel deep learning model for object detection

Object detection is a fundamental task in computer vision with significant implications across various applications, including autonomous driving, surveillance, and image understanding. The accurate and efficient detection of objects within images is crucial for enabling machines to interpret visual information and make informed decisions. In this paper, we present an enhanced version of the Single Shot MultiBox Detector (SSD) for object detection, leveraging the concept of dual attention mechanisms. Our proposed approach, named SSD-Dual Attention, integrates dual attention layers into the SSD framework. These dual attention layers are strategically positioned between feature maps and prediction convolutions, enhancing the model's ability to capture informative feature representations across a wide range of object scales and backgrounds. Experimental results on the PASCAL VOC 2007 and 2012 datasets validate the effectiveness of our approach. Notably, SSD-Dual Attention achieves an impressive mean Average Precision (mAP) of 78.1%, surpassing the performance of SSD models enhanced with attention mechanisms such as SSD-ECA, SSD-CBAM, SSD-Non-local attention and SSD-SE attention, as well as the original SSD. These results underscore the enhanced accuracy and precision of our object detection system, marking a substantial advancement in the field of computer vision. Code is available at https://github.com/AlexHunterLeo/Dual-attention-Enhanced-SSD-A-Novel-Deep-Learning-Model-for-Object-Detection

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments.

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

On the non-local nature of turbulent fluxes of passive scalars

The parameterization of fluxes associated with representing unresolved dynamics in turbulent flows, especially in the atmosphere and ocean (which have a vast range of scales), remains a challenging task. This is especially true for Earth system models including complex biogeochemistry and requiring very long simulations. The problem of representing the dependence of the mean flux of a passive tracer in terms of the mean has a very long history; in this study, we take a somewhat different approach. We use a formalism showing that the mean flux will be a functional of the mean gradients, a formalism that can be used to calculate the structure of the functional which is non-local in both space and time. Two-dimensional turbulent simulations are used to explore the weight of nearby (in space or time) gradients. We also use stochastic velocities and iterated maps to show that the results are similar. The functional formalism provides an understanding of when non-locality needs to be considered and when a local eddy diffusivity can be a reasonably good approximation. Furthermore, the formalism provides guidance for the development of data-driven parameterizations.

The Impacts of Igneous Systems on Sedimentary Basins and their Energy Resources

Abstract This introduction briefly outlines the potential impact of igneous processes on sedimentary basins and their energy resources. The associated volume includes contributions across a range of scales; from margin-wide rifting to impacts on reservoir quality. The impact of igneous systems on elements such as tectonics, heat flow, hydrocarbon (and other) fluid charge, structuration and reservoirs are reviewed with a direct impact on hydrocarbon exploration and production, geothermal and carbon capture storage projects. A strong understanding of these commonly overlooked, igneous processes is likely to enable successful exploration for additional energy resources such as frontier hydrocarbons, geothermal heat, hydrogen as well as helium.