Here,p-type polysilicon films are fabricated by ex-situ doping method with ammonium tetraborate tetrahydrate(ATT)as the boron source,named ATT-pPoly.The effects of ATT on the properties of polysilicon films are compre...Here,p-type polysilicon films are fabricated by ex-situ doping method with ammonium tetraborate tetrahydrate(ATT)as the boron source,named ATT-pPoly.The effects of ATT on the properties of polysilicon films are comprehensively analyzed.The Raman spectra reveal that the ATT-pPoly film is composed of grain boundary and crystalline regions.The preferred orientation is the(111)direction.The grain size increases from 16−23 nm to 21−47 nm,by~70%on average.Comparing with other reported films,Hall measurements reveal that the ATT-pPoly film has a higher carrier concentration(>10^(20)cm^(−3))and higher carrier mobility(>30 cm2/(V·s)).The superior properties of the ATT-pPoly film are attributed to the heavy doping and improved grain size.Heavy doping property is proved by the mean sheet resistance(Rsheet,m)and distribution profile.The R_(sheet,m)decreases by more than 30%,and it can be further decreased by 90%if the annealing temperature or duration is increased.The boron concentration of ATT-pPoly film annealed at 950℃ for 45 min is~3×10^(20)cm^(−3),and the distribution is nearly the same,except near the surface.Besides,the standard deviation coefficient(σ)of Rsheet,m is less than 5.0%,which verifies the excellent uniformity of ATT-pPoly film.展开更多
随着移动互联网+的广泛发展,各行各业的线上线下电子商务模式(online to offline,O2O)也应运而生,然而,同质化竞争和数据价值挖掘不足的问题限制了市场的持续向好发展。聚焦于O2O模式下的顾客满意度研究,提出了一种新型的中文文本预测模...随着移动互联网+的广泛发展,各行各业的线上线下电子商务模式(online to offline,O2O)也应运而生,然而,同质化竞争和数据价值挖掘不足的问题限制了市场的持续向好发展。聚焦于O2O模式下的顾客满意度研究,提出了一种新型的中文文本预测模型,命名为W2V-ATT-LSTM。该模型引入Attention机制以提高对重要文本的感知能力,进一步融合W2V和LSTM结构,深度挖掘头部企业真实交易数据进行分析处理、特征选择和模型训练。通过LDA模型进行主题挖掘,深入了解消费者对产品或服务的感受,为企业提供有针对性的改进建议。实验结果显示,W2V-ATT-LSTM模型在公开数据集任务中的准确率(91.4%)、精确率(82.2%)、召回率(81.7%)和F1(81.4%)等指标均优于KNN、贝叶斯、决策树、SVM等传统机器学习算法;在爬虫真实数据集任务中的准确率(94%)、精确率(90%)、召回率(89%)和F1(89%)也优于W2V、LSTM、Bi-LSTM和Bert;在多个公开中文情感分析数据集上的优越性能也表明W2V-ATT-LSTM对于理解和处理自然语言文本具有显著的实际应用价值。在当前竞争激烈的O2O市场,W2V-ATT-LSTM模型能为顾客与商家提供可靠的决策参考,有望帮助企业更好地理解顾客需求,提升服务水平,推动行业良性发展。展开更多
Digital integration within healthcare systems exacerbates their vulnerability to sophisticated ransomware threats, leading to severe operational disruptions and data breaches. Current defenses are typically categorize...Digital integration within healthcare systems exacerbates their vulnerability to sophisticated ransomware threats, leading to severe operational disruptions and data breaches. Current defenses are typically categorized into active and passive measures that struggle to achieve comprehensive threat mitigation and often lack real-time response effectiveness. This paper presents an innovative ransomware defense system, ERAD, designed for healthcare environments that apply the MITRE ATT&CK Matrix to coordinate dynamic, stage-specific countermeasures throughout the ransomware attack lifecycle. By systematically identifying and addressing threats based on indicators of compromise (IOCs), the proposed system proactively disrupts the attack chain before serious damage occurs. Validation is provided through a detailed analysis of a system deployment against LockBit 3.0 ransomware, illustrating significant enhancements in mitigating the impact of the attack, reducing the cost of recovery, and strengthening the cybersecurity framework of healthcare organizations, but also applicable to other non-health sectors of the business world.展开更多
基金support given by the Natural Science Foundation of Nantong(Grant NO.JC2023065)the Research Program of Nantong Institute of Technology(Grant NO.2023XK(B)07).
文摘Here,p-type polysilicon films are fabricated by ex-situ doping method with ammonium tetraborate tetrahydrate(ATT)as the boron source,named ATT-pPoly.The effects of ATT on the properties of polysilicon films are comprehensively analyzed.The Raman spectra reveal that the ATT-pPoly film is composed of grain boundary and crystalline regions.The preferred orientation is the(111)direction.The grain size increases from 16−23 nm to 21−47 nm,by~70%on average.Comparing with other reported films,Hall measurements reveal that the ATT-pPoly film has a higher carrier concentration(>10^(20)cm^(−3))and higher carrier mobility(>30 cm2/(V·s)).The superior properties of the ATT-pPoly film are attributed to the heavy doping and improved grain size.Heavy doping property is proved by the mean sheet resistance(Rsheet,m)and distribution profile.The R_(sheet,m)decreases by more than 30%,and it can be further decreased by 90%if the annealing temperature or duration is increased.The boron concentration of ATT-pPoly film annealed at 950℃ for 45 min is~3×10^(20)cm^(−3),and the distribution is nearly the same,except near the surface.Besides,the standard deviation coefficient(σ)of Rsheet,m is less than 5.0%,which verifies the excellent uniformity of ATT-pPoly film.
文摘随着移动互联网+的广泛发展,各行各业的线上线下电子商务模式(online to offline,O2O)也应运而生,然而,同质化竞争和数据价值挖掘不足的问题限制了市场的持续向好发展。聚焦于O2O模式下的顾客满意度研究,提出了一种新型的中文文本预测模型,命名为W2V-ATT-LSTM。该模型引入Attention机制以提高对重要文本的感知能力,进一步融合W2V和LSTM结构,深度挖掘头部企业真实交易数据进行分析处理、特征选择和模型训练。通过LDA模型进行主题挖掘,深入了解消费者对产品或服务的感受,为企业提供有针对性的改进建议。实验结果显示,W2V-ATT-LSTM模型在公开数据集任务中的准确率(91.4%)、精确率(82.2%)、召回率(81.7%)和F1(81.4%)等指标均优于KNN、贝叶斯、决策树、SVM等传统机器学习算法;在爬虫真实数据集任务中的准确率(94%)、精确率(90%)、召回率(89%)和F1(89%)也优于W2V、LSTM、Bi-LSTM和Bert;在多个公开中文情感分析数据集上的优越性能也表明W2V-ATT-LSTM对于理解和处理自然语言文本具有显著的实际应用价值。在当前竞争激烈的O2O市场,W2V-ATT-LSTM模型能为顾客与商家提供可靠的决策参考,有望帮助企业更好地理解顾客需求,提升服务水平,推动行业良性发展。
文摘Digital integration within healthcare systems exacerbates their vulnerability to sophisticated ransomware threats, leading to severe operational disruptions and data breaches. Current defenses are typically categorized into active and passive measures that struggle to achieve comprehensive threat mitigation and often lack real-time response effectiveness. This paper presents an innovative ransomware defense system, ERAD, designed for healthcare environments that apply the MITRE ATT&CK Matrix to coordinate dynamic, stage-specific countermeasures throughout the ransomware attack lifecycle. By systematically identifying and addressing threats based on indicators of compromise (IOCs), the proposed system proactively disrupts the attack chain before serious damage occurs. Validation is provided through a detailed analysis of a system deployment against LockBit 3.0 ransomware, illustrating significant enhancements in mitigating the impact of the attack, reducing the cost of recovery, and strengthening the cybersecurity framework of healthcare organizations, but also applicable to other non-health sectors of the business world.
