Characterization of large area avalanche photodiodes in X-ray and VUV-light detection

The present manuscript reviews our R+D studies on the application of large area avalanche photodiodes (LAAPDs) to the detection of X-rays and vacuum ultraviolet (VUV) light. The operational characteristics of LAAPDs manufactured by Advanced Photonix Inc. were investigated for X-ray detection at room temperature. The optimum energy resolution obtained in four LAAPDs investigated was found to be in the range 10-18% for 5.9 keV X-rays. The observed variations are associated with dark current differences between the several prototypes. LAAPDs have demonstrated high counting rate capability (up to about 10⁵/s) and applicability in diverse areas, mainly low-energy X-ray detection, where LAAPDs selected for low dark current may achieve better performance than proportional counters. LAAPDs were also investigated as VUV photosensors, presenting advantages compared to photomultiplier tubes. X-rays are often used as a reference in light measurements; this may be compromised by the non-linearity between gains measured for X-rays and VUV-light. The gain was found to be lower for X-rays than for VUV light, especially at higher bias voltages. For 5.9 keV X-rays, gain variations of 10% and 6% were measured relative to VUV light produced in argon ( ∼ 128 nm) and xenon ( ∼ 172 nm) for gains of about 200. The effect of temperature on the LAAPD performance was investigated for X-ray and VUV-light detection. Gain variations of more than -4% per oC were measured for 5.9 keV X-rays for gains above 200, while for VUV light variations are larger than -5% per oC. The energy resolution was found to improve with decreasing temperature, what is mainly attributed to dark current. The excess noise factor, another contribution to the energy resolution, was experimentally determined and found to be independent of temperature, increasing linearly with gain, from 1.8 to 2.3 for a 50-300 gain range. The LAAPD response under intense magnetic fields up to 5 Tesla was investigated. While for X-ray detection the APD response practically does not vary with the magnetic field, for 172 nm VUV light a significant amplitude reduction of more than 20% was observed

The Lamb shift in muonic hydrogen

The long quest for a measurement of the Lamb shift in muonic hydrogen is over. Last year we measured the 2S1/2F=1–2P3/2F=2 energy splitting (Pohl et al., Nature, 466, 213 (2010)) in μp with an experimental accuracy of 15 ppm, twice better than our proposed goal. Using current QED calculations of the fine, hyperfine, QED, and finite size contributions, we obtain a root-mean-square proton charge radius of rp = 0.841 84 (67) fm. This value is 10 times more precise, but 5 standard deviations smaller, than the 2006 CODATA value of rp. The origin of this discrepancy is not known. Our measurement, together with precise measurements of the 1S–2S transition in regular hydrogen and deuterium, gives improved values of the Rydberg constant, R∞ = 10 973 731.568 160 (16) m⁻¹ and the rms charge radius of the deuteron rd = 2.128 09 (31) fm

The size of the proton and the deuteron

We have recently measured the 2S1/2⁼¹ − 2P3/2 ⁼ ² energy splitting in the muonic hydrogen atom μp to be 49881.88 (76) GHz. Using recent QED calculations of the fine-, hyperfine, QED and finite size contributions we obtain a root-mean-square proton charge radius of rp = 0.84184 (67) fm. This value is ten times more precise, but 5 standard deviations smaller, than the 2006 CODATA value of rp = 0.8768 (69) fm. The source of this discrepancy is unknown. Using the precise measurements of the 1S-2S transition in regular hydrogen and deuterium and our value of rp we obtain improved values of the Rydberg constant, R∞ = 10973731.568160 (16) m⁻¹and the rms charge radius of the deuteron rd = 2.12809 (31) fm

Fraqueza muscular adquirida na UTI (ICU-AW): efeitos sistêmicos da eletroestimulação neuromuscular

Com os avanços tecnológicos alcançados atualmente na terapia intensiva e maior sobrevida dos pacientes, outros desafios têm surgido para os profissionais de saúde. Dentre alguns, destaca-se a fraqueza muscular adquirida na UTI (ICU-AW), caracterizada por paresia esquelética e respiratória dos músculos promovendo aumento nastaxas de mortalidade e comprometimento da qualidade de vida. Sua incidência varia de 30% a 60% e tem na síndrome da resposta inflamatória sistêmica (SIRS) e na disfunção de múltiplos órgãos (DMO) sua principal etiologia. Outros fatores de risco como a hiperglicemia,o uso de bloqueadores neuromusculares e sedativos, a imobilidade e a própria ventilação mecânica estão entre os mais comuns. Entre as medidas de combate à ICU-AW, está o conceito de mobilização precoce, bem como despertar diário e controle estreito da glicemia. Nesse contexto, a eletroestimulação muscular apresenta-se como recurso de grande valia. Sua principal vantagem está no fato de poder ser empreendida independentemente da cooperação do paciente, epor ser capaz de gerar respostas musculares eficientes, bem como resultados satisfatórios na preservação da massa muscular, condicionamento físico e funcionalidade dos que usam essa ferramenta. Desfechos interessantes têm sido observados em diversos perfis de pacientes, como os de doença pulmonar obstrutiva crônica (DPOC)e traumatismo raquimedular (TRM). No paciente crítico, seu uso tem mostrado redução nos tempos de ventilação mecânica (VM), internação na UTI e maior funcionalidade dos pacientes. A relevância dos efeitos sistêmicos e metabólicos provenientes da eletroestimulação neuromuscular (ENM) tem sido a base para os estudos nos pacientes críticos. Portanto, a ICU-AW é uma realidade no cenário da terapia intensiva e sua prevenção tem dado margem à aparição de novas propostas e ferramentas na prevenção dessas complicações

The Lamb shift in muonic hydrogen 1

Abstract: The long quest for a measurement of the Lamb shift in muonic hydrogen is over. Last year we measured the energy splitting (Pohl et al., Nature, 466, 213 (2010)) in mp with an experimental accuracy of 15 ppm, twice better than our proposed goal. Using current QED calculations of the fine, hyperfine, QED, and finite size contributions, we obtain a rootmean-square proton charge radius of r p = 0.841 84 (67) fm. This value is 10 times more precise, but 5 standard deviations smaller, than the 2006 CODATA value of r p . The origin of this discrepancy is not known. Our measurement, together with precise measurements of the 1S-2S transition in regular hydrogen and deuterium, gives improved values of the Rydberg constant, R ? = 10 973 731.568 16

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

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