Human-induced vibration serviceability of arch pre-stressed concrete truss system

Human-induced vibration has become a serious serviceability problem due to the larger opening of girder, lighter floor systems and longer spans designed and used in practice. Vibration tests were undertaken in laboratory to research the vibrational characteristics of the arch pre-stressed concrete truss (APT) system spanning 16.0 m. Results from ambient vibration, impulse excitations (heel-drop and jumping) and steady-state incentives (walking and running) were presented. Dynamic characteristics such as natural frequencies, damping ratios, mode shapes and acceleration responses were studied and checked against the existing codes. Experimental results show that the investigated APT girder possesses high fundamental frequency and low damping ratio. Moreover, the perception factors based on the root-mean-square acceleration, vibration dose value (VDV) and psychological comfort data were obtained. Lastly, the threshold accelerations and VDVs were suggested for evaluating the human-induced vibration

COMPARISON OF SOME BIOMECHANICS PARAMETERS OF BREASTSTROKE SWIMMERS IN FLUME AND SWIMMING POOL

The purpose of this study was to compare some parameters of breaststroke swimmers in a swimming pool with those for breaststroke swimming in the flume, to search whether there is some difference between two test circumstances of swimming pool and flume in technical parameters. Four male breaststroke swimmers aged between16 and 18 years were studied. Subjects were required to swim in a 25m pool for best or familiar stroke length and tried to decrease stroke rate, and performed at three minute intervals at speeds ranging from 70% to 100% of the best performance of individuals. Subjects were familiarized to flume swimming on the day prior to be tested, then swam at the same speed based upon conversion from pool in swimming flume. According to testing we found that stroke rate, stroke length and efficiency index for pool and swimming flume at corresponding speeds were similar. Of course, there was as expected significant difference in the stroke rate and stroke length used between subjects to swim at the various speeds

Paraoxon Attenuates Vascular Smooth Muscle Contraction through Inhibiting Ca2+ Influx in the Rabbit Thoracic Aorta

We investigated the effect of paraoxon on vascular contractility using organ baths in thoracic aortic rings of rabbits and examined the effect of paraoxon on calcium homeostasis using a whole-cell patch-clamp technique in isolated aortic smooth muscle cells of rabbits. The findings show that administration of paraoxon (30 μM) attenuated thoracic aorta contraction induced by phenylephrine (1 μM) and/or a high K+ environment (80 mM) in both the presence and absence of thoracic aortic endothelium. This inhibitory effect of paraoxon on vasoconstrictor-induced contraction was abolished in the absence of extracellular Ca2+, or in the presence of the Ca2+ channel inhibitor, verapamil. But atropine had little effect on the inhibitory effect of paraoxon on phenylephrine-induced contraction. Paraoxon also attenuated vascular smooth muscle contraction induced by the cumulative addition of CaCl2 and attenuated an increase of intracellular Ca2+ concentration induced by K+ in vascular smooth muscle cells. Moreover, paraoxon (30 μM) inhibited significantly L-type calcium current in isolated aortic smooth muscle cells of rabbits. In conclusion, our results demonstrate that paraoxon attenuates vasoconstrictor-induced contraction through inhibiting Ca2+ influx in the rabbits thoracic aorta

HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning

Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniques such as homomorphic encryption to ensure data security and privacy. Despite these efforts, studies have revealed that data leakage remains a risk in VFL due to the correlations between intermediate representations and raw data. Neural networks can accurately capture these correlations, allowing an adversary to reconstruct the data. This emphasizes the need for continued research into securing VFL systems. Our work shows that hashing is a promising solution to counter data reconstruction attacks. The one-way nature of hashing makes it difficult for an adversary to recover data from hash codes. However, implementing hashing in VFL presents new challenges, including vanishing gradients and information loss. To address these issues, we propose HashVFL, which integrates hashing and simultaneously achieves learnability, bit balance, and consistency. Experimental results indicate that HashVFL effectively maintains task performance while defending against data reconstruction attacks. It also brings additional benefits in reducing the degree of label leakage, mitigating adversarial attacks, and detecting abnormal inputs. We hope our work will inspire further research into the potential applications of HashVFL

A novel RNA in situ hybridization assay for the long noncoding RNA SChLAP1 predicts poor clinical outcome after radical prostatectomy in clinically localized prostate cancer.

Long noncoding RNAs (lncRNAs) are an emerging class of oncogenic molecules implicated in a diverse range of human malignancies. We recently identified SChLAP1 as a novel lncRNA that demonstrates outlier expression in a subset of prostate cancers, promotes tumor cell invasion and metastasis, and associates with lethal disease. Based on these findings, we sought to develop an RNA in situ hybridization (ISH) assay for SChLAP1 to 1) investigate the spectrum of SChLAP1 expression from benign prostatic tissue to metastatic castration-resistant prostate cancer and 2) to determine whether SChLAP1 expression by ISH is associated with outcome after radical prostatectomy in patients with clinically localized disease. The results from our current study demonstrate that SChLAP1 expression increases with prostate cancer progression, and high SChLAP1 expression by ISH is associated with poor outcome after radical prostatectomy in patients with clinically localized prostate cancer by both univariate (hazard ratio = 2.343, P = .005) and multivariate (hazard ratio = 1.99, P = .032) Cox regression analyses. This study highlights a potential clinical utility for SChLAP1 ISH as a novel tissue-based biomarker assay for outcome prognostication after radical prostatectomy

Hijack Vertical Federated Learning Models As One Party

Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties have a group of users in common but own different features. Existing VFL frameworks use cryptographic techniques to provide data privacy and security guarantees, leading to a line of works studying computing efficiency and fast implementation. However, the security of VFL's model remains underexplored.Comment: https://doi.ieeecomputersociety.org/10.1109/TDSC.2024.335808

Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions