Hiding the Higgs at the LHC

We study a simple extension of the standard model where scalar singlets that mix with the Higgs doublet are added. This modification to the standard model could have a significant impact on Higgs searches at the LHC. The Higgs doublet is not a mass eigenstate and therefore the expected nice peak of the standard model Higgs disappears. We analyze this scenario finding the required properties of the singlets in order to make the Higgs "invisible" at the LHC. In some part of the parameter space even one singlet could make the discovery of the SM Higgs problematic. In other parts, the Higgs can be discovered even in the presence of many singlets.Comment: 9 pages, 1 figure. V2- References added. V3- Several examples and one fig. adde

Attosecond time-resolved photoelectron holography

Ultrafast strong-field physics provides insight into quantum phenomena that evolve on an attosecond time scale, the most fundamental of which is quantum tunneling. The tunneling process initiates a range of strong field phenomena such as high harmonic generation (HHG), laser-induced electron diffraction, double ionization and photoelectron holography—all evolving during a fraction of the optical cycle. Here we apply attosecond photoelectron holography as a method to resolve the temporal properties of the tunneling process. Adding a weak second harmonic (SH) field to a strong fundamental laser field enables us to reconstruct the ionization times of photoelectrons that play a role in the formation of a photoelectron hologram with attosecond precision. We decouple the contributions of the two arms of the hologram and resolve the subtle differences in their ionization times, separated by only a few tens of attoseconds

Isolation and characterization of microsatellite loci from two inbreeding bark beetle species (Coccotrypes)

We developed 14 microsatellite markers in Coccotrypes carpophagus and 14 in C. dactyliperda. These loci will be used for studying genetic structure and the level of inbreeding in populations in the Canary Islands and Madeira. As a result of long-term inbreeding, genetic variability is relatively low in these bark beetle species. We found one to five alleles per locus in 29 C. carpophagus and 41 C. dactyliperda from various localities. Eleven of the markers developed for C. carpophagus amplified in C. dactyliperda and seven of the markers developed for C. dactyliperda amplified in C. carpophagus

Is There a Need for Preoperative Imaging of the Internal Mammary Recipient Site for Autologous Breast Reconstruction?

Preoperative imaging of recipient-site vasculatur in autologous breast reconstruction may potentiate improved outcomes through the identification of individual variations in vascular architecture. There are a range of both normal and pathologic states which can substantially affect the internal mammary vessels in particular, and the identification of these preoperatively may significantly affect operative approach. There are a range of imaging modalities available, with ultrasound particularly useful, and computed tomography angiography (CTA) evolving as a useful option, albeit with radiation exposure. The benefits of CTA must be balanced against its risks, which include contrast nephrotoxicity and allergic reactions, and radiation exposure. The radiation risk with thoracic imaging is substantially higher than that for donor sites, such as the abdominal wall, with reasons including exposure of the contralateral breast to radiation (with a risk of contralateral breast cancer in this population 2 to 6 times higher than that of primary breast cancer, reaching a 20-year incidence of 15%), as well as proximity to the thyroid gland. Current evidence suggests that although many cases may not warrant such imaging because of risk, the benefits of preoperative CTA in selected patients may outweigh the risks of exposure, prompting an individualized approach

Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights