Catalytically active NaNbO3 nanorods for sonodynamic cancer therapy

Sonodynamic therapy (SDT) has received a lot of interest due to its deep tissue penetration and lack of invasiveness. However, SDT still prioritizes the creation of highly effective, multifunctional, and biocompatible sonosensitizers to improve the therapeutic efficiency. In this study, sodium niobate (NaNbO3) nanosonosensitizers are rationed synthesized for SDT for the first time. NaNbO3 nanosonosensitizers with semiconductor characteristics are proved to generate large amounts of reactive oxygen species and induce cell apoptosis under ultrasound irradiation. In vitro anti-tumor theranostic results confirm the mitochondrial dysfunction-dependent death pathway. In vivo tumor xenograft evaluation demonstrates that NaNbO3 will massively induce cytotoxicity and tumor eradication under ultrasound irradiation. These results provide the paradigm of the utilization of novel nanosonosensitizers as a therapeutic nanoplatform in treating breast cancer cells.Comment: 11 pages, 6 figure

Undergraduates’ Self-reported Learning Outcomes of General Education Courses: A Case Study of a Chinese Elite University

Based on a conceptual framework of college impact, this article studies the impact of gender, graduation paths, family cultural capital and disciplines on undergraduates’ self-reported learning outcomes of general education courses. The conclusions are as follows: female students report significantly higher learning outcomes of general education courses than male students; students who will enter the labor market after graduation report significantly higher learning outcomes of general education courses than students who will enter graduate school; students majoring in social sciences report higher learning outcomes of general education courses than students from other disciplines. Familial cultural capital has no significant influence on undergraduates’ self-reported learning outcomes of general education courses. This article makes exploratory explanations of the above results from two perspectives

Targeted online password guessing:an underestimated threat

While trawling online/offline password guessing has been intensively studied, only a few studies have examined targeted online guessing, where an attacker guesses a specific victim's password for a service, by exploiting the victim's personal information such as one sister password leaked from her another account and some personally identifiable information (PII). A key challenge for targeted online guessing is to choose the most effective password candidates, while the number of guess attempts allowed by a server's lockout or throttling mechanisms is typically very small. We propose TarGuess, a framework that systematically characterizes typical targeted guessing scenarios with seven sound mathematical models, each of which is based on varied kinds of data available to an attacker. These models allow us to design novel and efficient guessing algorithms. Extensive experiments on 10 large real-world password datasets show the effectiveness of TarGuess. Particularly, TarGuess I~IV capture the four most representative scenarios and within 100 guesses: (1) TarGuess-I outperforms its foremost counterpart by 142% against security-savvy users and by 46% against normal users; (2) TarGuess-II outperforms its foremost counterpart by 169% on security-savvy users and by 72% against normal users; and (3) Both TarGuess-III and IV gain success rates over 73% against normal users and over 32% against security-savvy users. TarGuess-III and IV, for the first time, address the issue of cross-site online guessing when given the victim's one sister password and some PII

An object-oriented energy benchmark for the evaluation of the office building stock

Energy benchmarking is useful for understanding and enhancing building performance. The aim of this research is to develop an object-oriented energy benchmarking method for the evaluation of energy performance in buildings. Statistical analysis of the four-year monitored energy consumption data for office buildings was conducted. The results show that the energy use intensity follows the lognormal distribution with the Shapiro–Wilk normality test. Based on the lognormal distribution, the energy rating system for office buildings has been established. An object-oriented energy use intensity quota determination model has been developed. This research provides practical tools that enable decision-makers to evaluate a building's energy performance and determine the energy benchmark

Inequality trends of health workforce in different stages of medical system reform (1985-2011) in China

Introduction The aim of this study was to identify whether policies in different stages of medical system reform had been effective in decreasing inequalities and increasing the density of health workers in rural areas in China between 1985 and 2011. Methods With data from China Health Statistics Yearbooks from 2004 to 2012, we measured the Gini coefficient and the Theil L index across the urban and rural areas from 1985 to 2011 to investigate changes in inequalities in the distributions of health workers, doctors, and nurses by states, regions, and urban-rural stratum and account for the sources of inequalities. Results We found that the overall inequalities in the distribution of health workers decreased to the lowest in 2000, then increased gently until 2011. Nurses were the most unequally distributed between urban-rural districts among health workers. Most of the overall inequalities in the distribution of health workers across regions were due to inequalities within the rural-urban stratum. Discussions and conclusions Different policies and interventions in different stages would result in important changes in inequality in the distribution of the health workforce. It was also influenced by other system reforms, like the urbanization, education, and employment reforms in China. The results are useful for the Chinese government to decide how to narrow the gap of the health workforce and meet its citizens’ health needs to the maximum extent

DesignGPT: Multi-Agent Collaboration in Design

Generative AI faces many challenges when entering the product design workflow, such as interface usability and interaction patterns. Therefore, based on design thinking and design process, we developed the DesignGPT multi-agent collaboration framework, which uses artificial intelligence agents to simulate the roles of different positions in the design company and allows human designers to collaborate with them in natural language. Experimental results show that compared with separate AI tools, DesignGPT improves the performance of designers, highlighting the potential of applying multi-agent systems that integrate design domain knowledge to product scheme design

Differentially Private ERM Based on Data Perturbation

In this paper, after observing that different training data instances affect the machine learning model to different extents, we attempt to improve the performance of differentially private empirical risk minimization (DP-ERM) from a new perspective. Specifically, we measure the contributions of various training data instances on the final machine learning model, and select some of them to add random noise. Considering that the key of our method is to measure each data instance separately, we propose a new `Data perturbation' based (DB) paradigm for DP-ERM: adding random noise to the original training data and achieving ($\epsilon,\delta$)-differential privacy on the final machine learning model, along with the preservation on the original data. By introducing the Influence Function (IF), we quantitatively measure the impact of the training data on the final model. Theoretical and experimental results show that our proposed DBDP-ERM paradigm enhances the model performance significantly

Zipf’s Law in Passwords

Despite more than thirty years of research efforts, textual passwords are still enveloped in mysterious veils. In this work, we make a substantial step forward in understanding the distributions of passwords and measuring the strength of password datasets by using a statistical approach. We first show that Zipf\u27s law perfectly exists in real-life passwords by conducting linear regressions on a corpus of 56 million passwords. As one specific application of this observation, we propose the number of unique passwords used in regression and the slope of the regression line together as a metric for assessing the strength of password datasets, and prove it in a mathematically rigorous manner. Furthermore, extensive experiments (including optimal attacks, simulated optimal attacks and state-of-the-art cracking sessions) are performed to demonstrate the practical effectiveness of our metric. To the best of knowledge, our new metric is the first one that is both easy to approximate and accurate to facilitate comparisons, providing a useful tool for the system administrators to gain a precise grasp of the strength of their password datasets and to adjust the password policies more reasonably