Load-bearing capacity of screw-retained CAD/CAM-produced titanium implant frameworks (I-Bridge®2) before and after cyclic mechanical loading

Implant-supported screw-retained fixed dental prostheses (FDPs) produced by CAD/ CAM have been introduced in recent years for the rehabilitation of partial or total endentulous jaws. However, there is a lack of data about the long-term mechanical characteristics. OBJECTIVE: The aim of this study was to investigate the failure mode and the influence of extended cyclic mechanical loading on the load-bearing capacity of these frameworks. MATERIAL AND METHODS: Ten five-unit FDP frameworks simulating a free-end situation in the mandibular jaw were manufactured according to the I-Bridge®2-concept (I-Bridge®2, Biomain AB, Helsingborg, Sweden) and each was screw-retained on three differently angulated Astra Tech implants (30º buccal angulation/0º angulation/30º lingual angulation). One half of the specimens was tested for static load-bearing capacity without any further treatment (control), whereas the other half underwent five million cycles of mechanical loading with 100 N as the upper load limit (test). All specimens were loaded until failure in a universal testing machine with an occlusal force applied at the pontics. Load-displacement curves were recorded and the failure mode was macro- and microscopically analyzed. The statistical analysis was performed using a t-test (p=0.05). RESULTS: All the specimens survived cyclic mechanical loading and no obvious failure could be observed. Due to the cyclic mechanical loading, the load-bearing capacity decreased from 8,496 N±196 N (control) to 7,592 N±901 N (test). The cyclic mechanical loading did not significantly influence the load-bearing capacity (p=0.060). The failure mode was almost identical in all specimens: large deformations of the framework at the implant connection area were obvious. CONCLUSION: The load-bearing capacity of the I-Bridge®2 frameworks is much higher than the clinically relevant occlusal forces, even with considerably angulated implants. However, the performance under functional loading in vivo depends on additional aspects. Further studies are needed to address these aspects

Galactic Core-Collapse Supernovae at IceCube: “Fire Drill” Data Challenges and follow-up

The next Galactic core-collapse supernova (CCSN) presents a once-in-a-lifetime opportunity to make astrophysical measurements using neutrinos, gravitational waves, and electromagnetic radiation. CCSNe local to the Milky Way are extremely rare, so it is paramount that detectors are prepared to observe the signal when it arrives. The IceCube Neutrino Observatory, a gigaton water Cherenkov detector below the South Pole, is sensitive to the burst of neutrinos released by a Galactic CCSN at a level >10σ. This burst of neutrinos precedes optical emission by hours to days, enabling neutrinos to serve as an early warning for follow-up observation. IceCube\u27s detection capabilities make it a cornerstone of the global network of neutrino detectors monitoring for Galactic CCSNe, the SuperNova Early Warning System (SNEWS 2.0). In this contribution, we describe IceCube\u27s sensitivity to Galactic CCSNe and strategies for operational readiness, including "fire drill" data challenges. We also discuss coordination with SNEWS 2.0

All-Energy Search for Solar Atmospheric Neutrinos with IceCube

The interaction of cosmic rays with the solar atmosphere generates a secondary flux of mesons that decay into photons and neutrinos – the so-called solar atmospheric flux. Although the gamma-ray component of this flux has been observed in Fermi-LAT and HAWC Observatory data, the neutrino component remains undetected. The energy distribution of those neutrinos follows a soft spectrum that extends from the GeV to the multi-TeV range, making large Cherenkov neutrino telescopes a suitable for probing this flux. In this contribution, we will discuss current progress of a search for the solar neutrino flux by the IceCube Neutrino Observatory using all available data since 2011. Compared to the previous analysis which considered only high-energy muon neutrino tracks, we will additionally consider events produced by all flavors of neutrinos down to GeV-scale energies. These new events should improve our analysis sensitivity since the flux falls quickly with energy. Determining the magnitude of the neutrino flux is essential, since it is an irreducible background to indirect solar dark matter searches

Searching for IceCube sub-TeV neutrino counterparts to sub-threshold Gravitational Wave events

Since the release of the Gravitational Wave Transient Catalogue GWTC-2.1 by the LIGO-Virgo collaboration, sub-threshold gravitational wave (GW) candidates are publicly available. They are expected to be released in real-time as well, in the upcoming O4 run. Using these GW candidates for multi-messenger studies complement the ongoing efforts to identify neutrino counterparts to GW events. This in turn, allows us to schedule electromagnetic follow-up searches more efficiently. However, the definition and criteria for sub-threshold candidates are pretty flexible. Finding a multi-messenger counterpart via archival studies for these candidates will help to set up strong bounds on the GW parameters which are useful for defining a GW signal as sub-threshold, thereby increasing their significance for scheduling follow-up searches. Here, we present the current status of this ongoing work with the IceCube Neutrino Observatory. We perform a selection of the sub-threshold GW candidates from GWTC-2.1 and conduct an archival search for sub-TeV neutrino counterparts detected by the dense infill array of the IceCube Neutrino Observatory, known as "DeepCore". For this, an Unbinned Maximum Likelihood (UML) method is used. We report the 90% C.L. sensitivities of this sub-TeV neutrino dataset for each selected sub-threshold GW candidate, considering the spatial and temporal correlation between the GW and neutrino events within a 1000 s time window

Recent neutrino oscillation results with the IceCube experiment

The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube’s primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample are presented and compared to the existing worldwide measurements

Angular dependence of the atmospheric neutrino flux with IceCube data

IceCube Neutrino Observatory, the cubic kilometer detector embedded in ice of the geographic South Pole, is capable of detecting particles from several GeV up to PeV energies enabling precise neutrino spectrum measurement. The diffuse neutrino flux can be subdivided into three components: astrophysical, from extraterrestrial sources; conventional, from pion and kaon decays in atmospheric Cosmic Ray cascades; and the yet undetected prompt component from the decay of charmed hadrons. A particular focus of this work is to test the predicted angular dependence of the atmospheric neutrino flux using an unfolding method. Unfolding is a set of methods aimed at determining a value from related quantities in a model-independent way, eliminating the influence of several assumptions made in the process. In this work, we unfold the muon neutrino energy spectrum and employ a novel technique for rebinning the observable space to ensure sufficient event numbers within the low statistic region at the highest energies. We present the unfolded energy and zenith angle spectrum reconstructed from IceCube data and compare the result with model expectations and previous measurements