Machine learning approaches for lateral strength estimation in squat shear walls:A comparative study and practical implications

This study investigated the influence of input parameters on the shear strength of RC squat walls using machine learning (ML) models and finite element method (FEM) analysis. The analyses were conducted on the largest currently available dataset of 639 squat RC walls with a height-to-length ratio of less than or equal to 2.0. The findings suggest that ensemble learning models, specifically XGBoost, CatBoost, GBRT, and RF, are effective in predicting the shear strength of RC short shear walls and using Bayesian Optimization for hyperparameter tuning improves their performance. The axial load had a greater influence on the shear strength than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model significantly outperforms traditional design models such as ACI 318-19, ASCE/SEI 43-05, and Wood 1990. Additionally, reducing the number of input features from 13 to 10, 8, or 6 still yields reliable predictions with high accuracy. The finding suggests that the use of XGBoost models provides not only comparable accuracy to FEM simulations with non-linear pushover analysis but also the first one can predict the lateral strength in the case of incomplete data which could not be done by FEM. A web application incorporating XGBoost model with various input features can provide valuable insights for predicting the lateral strength of squat shear walls in building structures.</p

Background risk of breast cancer and the association between physical activity and mammographic density

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/ by/4.0

Atomic structures of TDP-43 LCD segments and insights into reversible or pathogenic aggregation.

The normally soluble TAR DNA-binding protein 43 (TDP-43) is found aggregated both in reversible stress granules and in irreversible pathogenic amyloid. In TDP-43, the low-complexity domain (LCD) is believed to be involved in both types of aggregation. To uncover the structural origins of these two modes of β-sheet-rich aggregation, we have determined ten structures of segments of the LCD of human TDP-43. Six of these segments form steric zippers characteristic of the spines of pathogenic amyloid fibrils; four others form LARKS, the labile amyloid-like interactions characteristic of protein hydrogels and proteins found in membraneless organelles, including stress granules. Supporting a hypothetical pathway from reversible to irreversible amyloid aggregation, we found that familial ALS variants of TDP-43 convert LARKS to irreversible aggregates. Our structures suggest how TDP-43 adopts both reversible and irreversible β-sheet aggregates and the role of mutation in the possible transition of reversible to irreversible pathogenic aggregation

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Baicalin administration attenuates hyperglycemia-induced malformation of cardiovascular system

In this study, the effects of Baicalin on the hyperglycemia-induced cardiovascular malformation during embryo development were investigated. Using early chick embryos, an optimal concentration of Baicalin (6 μM), was identified which could prevent hyperglycemia-induced cardiovascular malformation of embryos. Hyperglycemia-enhanced cell apoptosis was reduced in embryos and HUVECs in the presence of Baicalin. Hyperglycemia-induced excessive ROS production was inhibited when Baicalin was administered. Analyses of SOD, GSH-Px, MAQE and GABAA suggested Baicalin plays an antioxidant role in chick embryos possibly through suppression of outwardly rectifying Cl(-) in the high-glucose microenvironment. In addition, hyperglycemia-enhanced autophagy fell in the presence of Baicalin, through affecting the ubiquitin of p62 and accelerating autophagy flux. Both Baicalin and Vitamin C could decrease apoptosis, but CQ did not, suggesting autophagy to be a protective function on the cell survival. In mice, Baicalin reduced the elevated blood glucose level caused by streptozotocin (STZ). Taken together, these data suggest that hyperglycemia-induced embryonic cardiovascular malformation can be attenuated by Baicalin administration through suppressing the excessive production of ROS and autophagy. Baicalin could be a potential candidate drug for women suffering from gestational diabetes mellitus

Machine learning approaches for lateral strength estimation in squat shear walls: A comparative study and practical implications

This study investigated the influence of input parameters on the shear strength of RC squat walls using machine learning (ML) models and finite element method (FEM) analysis. The analyses were conducted on the largest currently available dataset of 639 squat RC walls with a height-to-length ratio of less than or equal to 2.0. The findings suggest that ensemble learning models, specifically XGBoost, CatBoost, GBRT, and RF, are effective in predicting the shear strength of RC short shear walls and using Bayesian Optimization for hyperparameter tuning improves their performance. The axial load had a greater influence on the shear strength than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model significantly outperforms traditional design models such as ACI 318-19, ASCE/SEI 43-05, and Wood 1990. Additionally, reducing the number of input features from 13 to 10, 8, or 6 still yields reliable predictions with high accuracy. The finding suggests that the use of XGBoost models provides not only comparable accuracy to FEM simulations with non-linear pushover analysis but also the first one can predict the lateral strength in the case of incomplete data which could not be done by FEM. A web application incorporating XGBoost model with various input features can provide valuable insights for predicting the lateral strength of squat shear walls in building structures

Indication for the disappearance of reactor electron antineutrinos in the Double Chooz experiment

The Double Chooz Experiment presents an indication of reactor electron antineutrino disappearance consistent with neutrino oscillations. A ratio of 0.944 $\pm$ 0.016 (stat) $\pm$ 0.040 (syst) observed to predicted events was obtained in 101 days of running at the Chooz Nuclear Power Plant in France, with two 4.25 GW$_{th}$ reactors. The results were obtained from a single 10 m$^3$ fiducial volume detector located 1050 m from the two reactor cores. The reactor antineutrino flux prediction used the Bugey4 measurement as an anchor point. The deficit can be interpreted as an indication of a non-zero value of the still unmeasured neutrino mixing parameter \sang. Analyzing both the rate of the prompt positrons and their energy spectrum we find \sang = 0.086 $\pm$ 0.041 (stat) $\pm$ 0.030 (syst), or, at 90% CL, 0.015 $<$ \sang $\ <$ 0.16.Comment: 7 pages, 4 figures, (new version after PRL referee's comments

Removing ammonium from water using modified corncob-biochar

© 2016 Elsevier B.V. Ammonium pollution in groundwater and surface water is of major concern in many parts of the world due to the danger it poses to the environment and people's health. This study focuses on the development of a low cost adsorbent, specifically a modified biochar prepared from corncob. Evaluated here is the efficiency of this new material for removing ammonium from synthetic water (ammonium concentration from 10 to 100 mg/L). The characteristics of the modified biochar were determined by Brunauer-Emmett-Teller (BET) test, Fourier transform infrared spectroscopy (FTIR) and Scanning electron microscopy (SEM). It was found that ammonium adsorption on modified biochar strongly depended on pH. Adsorption kinetics of NH4+-N using modified biochar followed the pseudo-second order kinetic model. Both Langmuir and Sips adsorption isotherm models could simulate well the adsorption behavior of ammonium on modificated biochar. The highest adsorption capacity of 22.6 mg NH4+-N/g modified biochar was obtained when the biochar was modified by soaking it in HNO3 6 M and NaOH 0.3 M for 8 h and 24 h, respectively. The high adsorption capacity of the modified biochar suggested that it is a promising adsorbent for NH4+-N remediation from water

Ovarian cancer molecular pathology.

Peer reviewe