Transition Temperature for Weakly Interacting Homogeneous Bose Gases

We apply the nonperturbative optimized linear δ expansion method to the O(N) scalar field model in three dimensions to determine the transition temperature of a dilute homogeneous Bose gas. Our results show that the shift of the transition temperature ΔTc/Tc of the interacting model, compared with the ideal-gas transition temperature, really behaves as γan1/3 where a is the s-wave scattering length and n is the number density. For N=2 our calculations yield the value γ=3.059

Effective Potential and Thermodynamics for a Coupled Two-Field Bose Gas Model

We study the thermodynamics of a two-species homogeneous and dilute Bose gas that is self-interacting and quadratically coupled to each other. We make use of field theoretical functional integral techniques and evaluate the one-loop finite temperature effective potential for this system considering the resummation of the leading order temperature dependent as well as infrared contributions. The symmetry breaking pattern associated to the model is then studied by considering different values of self and inter-species couplings. We pay special attention to the eventual appearance of reentrant phases and/or shifts in the observed critical temperatures as compared to the monoatomic (one-field Bose) case.Comment: 21 pages, 4 eps figure

Seasonal variation and antimicrobial activity of Myrcia myrtifolia essential oils

This work reports the seasonal variation of the composition of leaf volatile oils and the composition of volatile oils from flowers and fruits of Myrcia myrtifolia DC harvested in the sand dunes of Salvador, Bahia, northeastern region of Brazil between 2002 and 2003. The oils were analyzed by GC-FID and GC-MS so that 28 components were identified. alpha-Pinene was predominant in a range from 61.5 to 90.9% in all samples analyzed. The leaf oil collected in October 2002 had their antimicrobial properties tested against six bacteria, two yeasts and five filamentous fungi being active against Staphylococcus aureus, methicilin-resistant Staphylococcus aureus, Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus, and showed strongest activity against Microsporum canis and Trichophyton rubrum. The oil displayed moderate toxicity against Artemia salina showing a LC50 of 479.16 µg mL-1

Higher Order Evaluation of the Critical Temperature for Interacting Homogeneous Dilute Bose Gases

We use the nonperturbative linear \delta expansion method to evaluate analytically the coefficients c_1 and c_2^{\prime \prime} which appear in the expansion for the transition temperature for a dilute, homogeneous, three dimensional Bose gas given by T_c= T_0 \{1 + c_1 a n^{1/3} + [ c_2^{\prime} \ln(a n^{1/3}) +c_2^{\prime \prime} ] a^2 n^{2/3} + {\cal O} (a^3 n)\}, where T_0 is the result for an ideal gas, a is the s-wave scattering length and n is the number density. In a previous work the same method has been used to evaluate c_1 to order-\delta^2 with the result c_1= 3.06. Here, we push the calculation to the next two orders obtaining c_1=2.45 at order-\delta^3 and c_1=1.48 at order-\delta^4. Analysing the topology of the graphs involved we discuss how our results relate to other nonperturbative analytical methods such as the self-consistent resummation and the 1/N approximations. At the same orders we obtain c_2^{\prime\prime}=101.4, c_2^{\prime \prime}=98.2 and c_2^{\prime \prime}=82.9. Our analytical results seem to support the recent Monte Carlo estimates c_1=1.32 \pm 0.02 and c_2^{\prime \prime}= 75.7 \pm 0.4.Comment: 29 pages, 3 eps figures. Minor changes, one reference added. Version in press Physical Review A (2002

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost