Identification of Contact Stiffness between Brake Disc and Brake Pads Using Modal Frequency Analysis

The contact stiffness between brake disc and brake pads is a vital parameter that affects brake NVH performance through increasing the system stiffness and modal frequencies. In order to establish accurate contact behavior between brake parts for further research on precise modeling of disc brakes, a method of identifying the normal contact stiffness of a floating caliper disc brake was developed in this study based on modal frequency testing and finite element analysis. The results showed that contact stiffness increases with brake pressure due to compression of the friction material and increases with the disc mode order at lower-order modes but almost stays invariant at higher-order ones due to contact area variation

Experimental investigation of two-bolt connections for high strength steel members

[EN] This paper presents an experimental research on bearing-type bolted connections consisting of two bolts positioned perpendicular to the loading direction. A total of 24 connections in double shear fabricated from high strength steels with yield stresses of 677MPa and 825MPa are tested. Two failure modes as tearout failure and splitting failure are observed in experiments. The effect of end distance, edge distance, bolt spacing and steel grade on the failure mode and bearing behavior are discussed. For connection design with bolts positioned perpendicular to loading direction, it is further found that combination of edge distance and bolt spacing effectively determines the failure mode and ultimate load. The test results are compared with Eurocode3. An optimal combination of edge distance and bolt spacing as well as related design suggestion is thus recommended.The authors would like to acknowledge the funding support by National Natural Science Foundation of China, Grant No. 51408428.Wang, Y.; Lyu, Y.; Li, G. (2018). Experimental investigation of two-bolt connections for high strength steel members. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 595-600. https://doi.org/10.4995/ASCCS2018.2018.7211OCS59560

Deep learning methods for protein torsion angle prediction

Background: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. Results: We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Conclusions: Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy

Animal personality can modulate sexual conflict over offspring provisioning

Sexual conflict over parental investment is widespread among species with biparental care. Studies have indicated that a high degree of behavioural similarity between the two parents can increase offspring survival; however, it remains unclear how sexual conflict over parental care is resolved. In this study, we examined whether similarity of personality traits between the two parents plays an important role in affecting the provisioning behaviour of each sex in a wild population of the chestnut thrush, Turdus rubrocanus. First, as expected, the mating pairs with more similar personality traits had higher provisioning rates than those pairs with dissimilar traits. Moreover, we found that the similarity of personality traits can modulate the sexual conflict over provisioning in this species, as both parents with more similar partners had relatively higher and less divergent provisioning rates. A partner removal experiment revealed how the sole female or male parent responded when the level of conflict over care increased (the removed partner does not provide any care). The majority of males always reduced their provisioning investment, while females’ decisions depended on the degree of similarity with their partners. Females compensated by provisioning more frequently in pairs of similar personality traits (i.e. accepting a high level of conflict), but reduced their provisioning investment in extremely dissimilar pairs. Our results promote a better understanding of the resolution of sexual conflict over provisioning and highlight the evolutionary significance of mating with similar partners based on certain personality traits

Privacy and Robustness in Federated Learning: Attacks and Defenses

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.Comment: arXiv admin note: text overlap with arXiv:2003.02133; text overlap with arXiv:1911.11815 by other author

A dual ammonia-responsive sponge sensor: preparation, transition mechanism and sensitivity

PDMS-PU (polydimethylsiloxane-polyurethane) sponge decorated with In(OH)(3) (indium hydroxide) and BCP (bromocresol purple) particles is shown to be a room-temperature ammonia sensor with high sensitivity and excellent reproducibility; it can accomplish real-time detection and monitoring of ammonia in the surrounding environment. The superhydrophobic and yellowish In(OH)(3)-BCP-TiO2-based ammoniaresponsive (IBT-AR) sponge changes to a purple superhydrophilic one when exposed to ammonia. Notably, after reacting with ammonia, the sponge can recover its original wettability and color after heating in air. The wettability, color and absorption signal of IBT-AR sponge have been measured for sensing ammonia using the water contact angle, macroscopic observation and UV-vis absorption spectrometry, respectively. The minimum ammonia concentrations that can be detected by the sponge wettability, color and absorption signal are 0.5%, 1.4 ppm and 50 ppb, respectively. This kind of sponge with smart wettability and color is a promising new ammonia detector