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This paper presents an experimental evaluation of a new interface configuration for unbonded post-tensioned shear (UPTS) walls. In the proposed configuration, the wall-base interface is shaped in a circular profile. This circular profile presents a major difference from the currently used flat profile at the wall-base interface of rocking UPTS walls. During lateral response, this circular profile induces the wall to predominantly rotate as a rigid body about a fixed point without uplift, and the system dissipates energy through the contact friction that develops at the wall-base interface. This rigid body motion resembles that of a pendulum, thereby designating this system as a pendulum UPTS wall. At this stage, research has demonstrated the many advantages of this system by proof-of-concept testing of a pendulum light-frame wood (LiF) UPTS wall specimen under increasing levels of post-tensioning force. Compared with rocking UPTS walls, experimental results demonstrate that besides performing dam-age-free when subjected to high drift levels, the proof-of-concept pendulum LiF-UPTS wall offers the following promising outcomes: (1) insignificant to no wall uplift, (2) insignificant to no wall base shear sliding, (3) reduced stress concentrations at the wall toes because contact stresses are distributed along the wall-base interface over a larger region, (4) nearly constant post-tensioning forces under high drift levels, which limits post-tensioning losses due to yielding of prestressing bars or tendons, (5) increase in energy dissipation capacity of the system through friction, and (6) decrease in the damping reduction factor and thus, reduction in the lateral displacement and force demands in pendulum LiF-UPTS walls. These research outcomes are likely to translate positively to other shear wall types, namely reinforced concrete (RC) precast pendulum UPTS walls. 

Abstract M15 is a globular cluster with a known spread in neutron-capture elements. This paper presents abundances of neutron-capture elements for 62 stars in M15. Spectra were obtained with the Michigan/Magellan Fiber System spectrograph, covering a wavelength range from ∼4430 to 4630 Å. Spectral lines from Fei, Feii, Sri, Zrii, Baii, Laii, Ceii, Ndii, Smii, Euii, and Dyii were measured, enabling classifications and neutron-capture abundance patterns for the stars. Of the 62 targets, 44 are found to be highly Eu-enhancedr-II stars, another 17 are moderately Eu-enhancedr-I stars, and one star is found to have ans-process signature. The neutron-capture patterns indicate that the majority of the stars are consistent with enrichment by ther-process. The 62 target stars are found to show significant star-to-star spreads in Sr, Zr, Ba, La, Ce, Nd, Sm, Eu, and Dy, but no significant spread in Fe. The neutron-capture abundances are further found to have slight correlations with sodium abundances from the literature, unlike what has been previously found; follow-up studies are needed to verify this result. The findings in this paper suggest that the Eu-enhanced stars in M15 were enhanced by the same process, that the nucleosynthetic source of this Eu pollution was ther-process, and that ther-process source occurred as the first generation of cluster stars was forming. 

In this paper, we provide a simple proof of the Holonomy theorem using a new Lyndon–Chiswell length function on the Karnofsky–Rhodes expansion of a semigroup. Unexpectedly, we have both a left and a right action on the Chiswell tree by elliptic maps. 

Fluorophores covalently bound to azacrown ether ionophores can be assembled into sensitive turn-on chemosensors. The size specificity and electron rich nature of the ionophore's binding domain contributes to both selectivity... 

Abstract Cellulose-based systems are useful for many applications. However, the issue of self-organization under non-equilibrium conditions, which is ubiquitous in living matter, has scarcely been addressed in cellulose-based materials. Here, we show that quasi-2D preparations of a lyotropic cellulose-based cholesteric mesophase display travelling colourful patterns, which are generated by a chemical reaction-diffusion mechanism being simultaneous with the evaporation of solvents at the boundaries. These patterns involve spatial and temporal variation in the amplitude and sign of the helix´s pitch. We propose a simple model, based on a reaction-diffusion mechanism, which simulates the observed spatiotemporal colour behaviour. 

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems.In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.