In 2012, on the occasion of 20th anniversary, National Engineering Research Center for Refractories, registered in Sinosteel LIRR, got ＂ Excellent Achievement Prize＂ from National Development and Reform Commission ...In 2012, on the occasion of 20th anniversary, National Engineering Research Center for Refractories, registered in Sinosteel LIRR, got ＂ Excellent Achievement Prize＂ from National Development and Reform Commission for its demonstration function and outstanding achievments.展开更多
Nearly one in five U.S. workers claims to be in excellent health--despite being overweight, smoking, drinking too much or never exercising, according to a newly published survey. In the nationwide study of 1,450 emplo...Nearly one in five U.S. workers claims to be in excellent health--despite being overweight, smoking, drinking too much or never exercising, according to a newly published survey. In the nationwide study of 1,450 employed adults released by Oxford Health Plans Inc., 17 percent described their health as excellent but displayed not-so-excellent habits. Of those people, 55 percent said they were at least 25 pounds (11 kg) overweight, 31 percent smoked, 21 percent drank at least three glasses of alcohol a day, 29 percent drank at least four cups of coffee or tea, and 36 percent never exercised, it said. In addition, a quarter of them said they were likely to eat fried foods and salty or sugary snacks. "Denial is dangerous when it comes to your health. It exacts a heavy toll down the road," Alan Muney, executive vice president of Oxford, said in a statement. The study also showed people with healthier habits such as frequent exercise and good diet are most motivated at work, ranking 8.9 on a 10-point scale, and most useful on the job, scoring 9 on a 10-point scale. Those with the healthiest habits were the least likely to lose sleep over their jobs and least likely to miss personal or family activities due to work. Of those workers with the most bad habits, 37 percent said they sat at their desks all day, 41 percent took no breaks at work and 18 percent were most likely to lose sleep over work.展开更多
In order to cultivate excellent clinical medical talents with a solid foundation, strong literacy, refined skills, excellent communication abilities, innovative thinking, and a strong focus on practicality, functional...In order to cultivate excellent clinical medical talents with a solid foundation, strong literacy, refined skills, excellent communication abilities, innovative thinking, and a strong focus on practicality, functional experiments have undergone a series of reforms in areas such as constructing new curriculum systems, improving teaching content, updating teaching equipment, introducing new teaching models, and enhancing teaching evaluation systems.展开更多
Objective: To investigate the impact of excellent event management in improving patient safety and nursing staff care satisfaction. Methods: The study was analyzed by retrospective comparison, and routine management f...Objective: To investigate the impact of excellent event management in improving patient safety and nursing staff care satisfaction. Methods: The study was analyzed by retrospective comparison, and routine management from January 2022 to December 2022 was set as the control group, and excellent event management from January 2023 to January 2024 was set as the study group. The differences in nursing outcomes between both groups were compared. Results: The rate of adverse events in the study group (0.61%) was lower than that in the control group (0.96%), and the rate of excellent events in the study group (2.57%) was higher than that in the control group (0.97%) (P < 0.05). Meanwhile, the satisfaction level of nursing safety in the study group reached 98.81%, which was much higher than in the control group (92.21%) (P < 0.05). Conclusion: Nursing excellent event management had a positive impact on improving patient care safety satisfaction, reducing the rate of adverse events, and increasing the rate of reporting excellent events.展开更多
近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数...近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。展开更多
文摘In 2012, on the occasion of 20th anniversary, National Engineering Research Center for Refractories, registered in Sinosteel LIRR, got ＂ Excellent Achievement Prize＂ from National Development and Reform Commission for its demonstration function and outstanding achievments.
文摘Nearly one in five U.S. workers claims to be in excellent health--despite being overweight, smoking, drinking too much or never exercising, according to a newly published survey. In the nationwide study of 1,450 employed adults released by Oxford Health Plans Inc., 17 percent described their health as excellent but displayed not-so-excellent habits. Of those people, 55 percent said they were at least 25 pounds (11 kg) overweight, 31 percent smoked, 21 percent drank at least three glasses of alcohol a day, 29 percent drank at least four cups of coffee or tea, and 36 percent never exercised, it said. In addition, a quarter of them said they were likely to eat fried foods and salty or sugary snacks. "Denial is dangerous when it comes to your health. It exacts a heavy toll down the road," Alan Muney, executive vice president of Oxford, said in a statement. The study also showed people with healthier habits such as frequent exercise and good diet are most motivated at work, ranking 8.9 on a 10-point scale, and most useful on the job, scoring 9 on a 10-point scale. Those with the healthiest habits were the least likely to lose sleep over their jobs and least likely to miss personal or family activities due to work. Of those workers with the most bad habits, 37 percent said they sat at their desks all day, 41 percent took no breaks at work and 18 percent were most likely to lose sleep over work.
文摘In order to cultivate excellent clinical medical talents with a solid foundation, strong literacy, refined skills, excellent communication abilities, innovative thinking, and a strong focus on practicality, functional experiments have undergone a series of reforms in areas such as constructing new curriculum systems, improving teaching content, updating teaching equipment, introducing new teaching models, and enhancing teaching evaluation systems.
文摘Objective: To investigate the impact of excellent event management in improving patient safety and nursing staff care satisfaction. Methods: The study was analyzed by retrospective comparison, and routine management from January 2022 to December 2022 was set as the control group, and excellent event management from January 2023 to January 2024 was set as the study group. The differences in nursing outcomes between both groups were compared. Results: The rate of adverse events in the study group (0.61%) was lower than that in the control group (0.96%), and the rate of excellent events in the study group (2.57%) was higher than that in the control group (0.97%) (P < 0.05). Meanwhile, the satisfaction level of nursing safety in the study group reached 98.81%, which was much higher than in the control group (92.21%) (P < 0.05). Conclusion: Nursing excellent event management had a positive impact on improving patient care safety satisfaction, reducing the rate of adverse events, and increasing the rate of reporting excellent events.
文摘近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。
