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The general observation that secondary consumers ingest highly digestible food and have simple short guts and small abdominal cavities intuitively results in the assumption that mammalian carnivores carry less digesta in their gut compared to herbivores. Due to logistic constraints, this assumption has not been tested quantitatively so far. In this contribution, we estimated the dry matter gut contents (DMC) for 25 species of the order Carnivora (including two strictly herbivorous ones, the giant and the red panda) using the physical 'Occupancy Principle', based on a literature data collection on dry matter intake (DMI), apparent dry matter digestibility (aD DM) and retention time (RT), and compared the results to an existing collection for herbivores. Scaling exponents with body mass (BM) for both carnivores and herbivores were in the same range with DMI similar to BM0.75; aD DM similar to BM0; RT similar to BM0.11 and DMC similar to BM0.88. The trophic level (carnivore vs herbivore) significantly affected all digestive physiology parameters except for RT. Numerically, the carnivore DMI level reached 77%, the RT 32% and DMC only 29% of the corresponding herbivore values, whereas the herbivore aD DM only reached 82% of that of carnivores. Thus, we quantitatively show that carnivores carry less inert mass or gut content compared to herbivores, which putatively benefits them in predator-prey interactions and might have contributed to the evolution towards unguligradism in herbivores. As expected, the two panda species appeared as outliers in the dataset with low aD DM and RT for a herbivore but extremely high DMI values, resulting in DMC in the lower part of the herbivore range. Whereas the difference in DMI and DMC scaling in herbivores might allow larger herbivores to compensate for lower diet quality by ingesting more, this difference may allow larger carnivores not to go for less digestible prey parts, but mainly to increase meal intervals, i.e. not having to hunt on a daily basis.

The refrigerant R134a is to be phasing out soon in automobile air conditioning applications due to its high global warming potential of 1430. Hence, it is essential to identify a sustainable alternative refrigerant to phase out R134a in automobile air conditioners. This paper presents the experimental thermodynamic performance of R430A (composed of R152a and R600a, in the ratio of 76:24, by mass) as a drop-in substitute to replace R134a in automobile air conditioners. The experiments were carried out in an automobile air conditioner test setup equipped with a variable frequency drive electrical motor. During experimentation, the ambient temperature and ambient relative humidity were maintained at 35 ± 1 °C and 65 ± 5%, respectively. The compressor speed was varied in the range between 1000 and 3000 rpm. The results showed that the coefficient of performance of an automobile air conditioner working with R430A was found to be 12–20% higher with 6–11% reduced compressor power consumption when compared to R134a. The R430A has 2–6 °C higher compressor discharge temperature when compared to R134a. The physical stability of the lubricant used in the compressor was retained while operating with R430A. The maximum exergy destruction occurs in the compressor (0.28 kW for R134a and 0.24 kW for R430A) followed by evaporator (0.16 kW for R134a and 0.14 kW for R430A), condenser (0.14 for R134a and 0.12 kW for R430A) and expansion valve (0.043 kW for R134a and 0.039 kW for R430A) at a compressor speed of 1000 ± 10 rpm. The exergy destruction of the system operating with R430A was found to be 12–28% lower when compared to R134a systems due to its favorable thermo-physical properties. The total equivalent warming impact of R430A was found to be lower when compared to R134a by about 47.3%, 35% and 32.4% for LPG, petrol and diesel vehicles, respectively. The results confirmed that R430A is a good drop-in substitute to replace R134a in existing automobile air conditioning systems.

Many mechanisms have been hypothesized to explain Bergmann’s rule - the correlation of body size with latitude. However, it is not feasible to assess the contribution of hypothesised mechanisms by experimental manipulation or statistical correlation. Here, we evaluate two of the principal hypothesised mechanisms, related to thermoregulation and resource availability, using structured experiments in a mechanistic global ecosystem model. We simulated the broad structure of assemblages and ecosystems using the Madingley model, a mechanistic General Ecosystem Model (GEM). We compared emergent modelled biogeographic patterns in body mass to empirical patterns for mammals and birds. We then explored the relative contribution of thermoregulation and resource availability to body mass clines by manipulating the model’s environmental gradients. Madingley produces body size gradients that are in broad agreement with empirical estimates. Thermoregulation and resource availability were both important controls on body mass for endotherms, but only temperature for ectotherms. Our results suggest that seasonality explains animal body mass patterns through a complex set of mechanisms. Process-based GEMs generate broadly realistic biogeographic body mass patterns. Ecologists can use them in novel ways: to explore causality, or for generating and testing hypotheses for large-scale, emergent ecological patterns. At the same time, macroecological patterns are useful for evaluating mechanistic models. Iteratively developing GEMs, and evaluating them against macroecological patterns, could generate new insights into the complex causes of such patterns.

A prominent signal of the Anthropocene is the extinction and population reduction of the megabiota – the largest animals and plants on the planet. However, we lack a predictive framework for the sensitivity of megabiota during times of rapid global change and how they impact the functioning of ecosystems and the biosphere. Here, we extend metabolic scaling theory and use global simulation models to demonstrate that the megabiota (i) are more prone to extinction due to human land use, hunting, and climate change; (ii) their loss has a negative impact on ecosystem metabolism and functioning; and (iii) their continued reduction will significantly decrease biosphere functioning. Analyses of several forest and animal datasets and large-scale simulation models largely support these predictions. Global simulations show that continued loss of large animals could lead to a 44%, 18% and 92% reduction in terrestrial heterotrophic biomass, metabolism, and fertility respectively. Landscapes with megabiota buffer ecosystem functioning, diversity, and likely human health. As a result, policies that emphasize the promotion of large trees and animals will have disproportionate impact on biodiversity, ecosystem processes, and climate mitigation.

Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a “local” version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.

The leaf economic spectrum (LES) describes a set of universal trade-offs between leaf mass per area (LMA), leaf nitrogen (N), leaf phosphorus (P) and leaf photosynthesis that influence patterns of primary productivity and nutrient cycling. Many questions regarding vegetation-climate feedbacks can be addressed with a better understanding of LES traits and their controls. Remote sensing offers enormous potential for generating large-scale LES trait data. Yet so far, canopy studies have been limited to imaging spectrometers onboard aircraft, which are rare, expensive to deploy and lack fine-scale resolution. In this study, we measured VNIR (visible-near infrared (400–1050 nm)) reflectance of individual sun and shade leaves in 7 one-ha tropical forest plots located along a 1200–2000 mm precipitation gradient in West Africa. We collected hyperspectral imaging data from 3 of the 7 plots, using an octocopter-based unmanned aerial vehicle (UAV), mounted with a hyperspectral mapping system (450–950 nm, 9 nm FWHM). Using partial least squares regression (PLSR), we found that the spectra of individual sun leaves demonstrated significant (p < 0.01) correlations with LMA and leaf chemical traits: r2 = 0.42 (LMA), r2 = 0.43 (N), r2 = 0.21 (P), r2 = 0.20 (leaf potassium (K)), r2 = 0.23 (leaf calcium (Ca)) and r2 = 0.14 (leaf magnesium (Mg)). Shade leaf spectra displayed stronger relationships with all leaf traits. At the airborne level, four of the six leaf traits demonstrated weak (p < 0.10) correlations with the UAV-collected spectra of 58 tree crowns: r2 = 0.25 (LMA), r2 = 0.22 (N), r2 = 0.22 (P), and r2 = 0.25 (Ca). From the airborne imaging data, we used LMA, N and P values to map the LES across the three plots, revealing precipitation and substrate as co-dominant drivers of trait distributions and relationships. Positive N-P correlations and LMA-P anticorrelations followed typical LES theory, but we found no classic trade-offs between LMA and N. Overall, this study demonstrates the application of UAVs to generating LES information and advancing the study and monitoring tropical forest functional diversity.

The SpaceHawk is an Earth-based model spacecraft designed to navigate the surface of the moon in a series of “hops”. By design, the nonholonomic dynamics of the SpaceHawk allow the use of full-state feedback linearization. The position and attitude states are decoupled via exact feedback linearization before being stabilized via proportional-derivative (PD) control. As a computationally inexpensive alternative to the exact linearization controller, an approximate linearization controller is designed to exclude the highly nonlinear attitude dynamics. Simulations show the approximate linearization controller performing comprably to the exact linearization controller despite the initial conditions being far from the origin. Satisfactory results are obtained for stability and tracking in the position and attitude states.