P2P2B模式下云服务投入是工业互联网(industrial internet of things,IIoT)平台的关键战略决策之一.构建由IIoT平台、龙头企业、潜在客户构成的演化博弈模型,研究IIoT平台在公有云研发投入和私有云研发投入中的策略选择,及其与龙头企业...P2P2B模式下云服务投入是工业互联网(industrial internet of things,IIoT)平台的关键战略决策之一.构建由IIoT平台、龙头企业、潜在客户构成的演化博弈模型,研究IIoT平台在公有云研发投入和私有云研发投入中的策略选择,及其与龙头企业的生态合作问题.结果表明:虽然公有云存在数据泄露隐患,但较高的规模收益仍会吸引IIoT平台投入公有云研发,而平台搭建期内龙头企业的高合作意愿会促使平台投入私有云,随着龙头企业合作研发的比例增加,平台又将改变其投入策略.驱动龙头企业合作的因素可以是成本收益、技术提升等直接因素,也可以是规模收益、数据泄露概率等间接因素.最后,基于平台生命周期探讨了初创期、平台搭建期与生态系统期IIoT平台的系统稳定策略,并得到相应的管理启示.展开更多
目的:舒芬太尼镇痛效果存在个体差异。本研究旨在评价CYP2B6(CPB6)*4和OPRM1基因多态性对膝关节镜下患者术后舒芬太尼静脉镇痛效应的影响。方法:选择择期腰麻硬膜外联合麻醉下行膝关节镜前交叉韧带重建术患者210例,患者术后使用静脉镇...目的:舒芬太尼镇痛效果存在个体差异。本研究旨在评价CYP2B6(CPB6)*4和OPRM1基因多态性对膝关节镜下患者术后舒芬太尼静脉镇痛效应的影响。方法:选择择期腰麻硬膜外联合麻醉下行膝关节镜前交叉韧带重建术患者210例,患者术后使用静脉镇痛泵。术前采集2 mL静脉血置入抗凝管,留作检测CPB6*4和OPRM1的基因多态性,根据CPB6*4和OPRM1基因的不同基因组将患者分为A/A组、G/A组、G/G组、T/T组和C/T组,观察不同基因组患者术后疼痛数字评分(Numerical Rating Scale,NRS)、病人自控镇痛(patient controlled analgesia,PCA)按压次数、舒芬太尼用量、术后开始出现疼痛的时间以及术后不良事件的发生率。结果:共纳入202例患者,CPB6*4rs2279343中A/A组76例,G/A组110例,G/G组16例;OPRM1 rs73568641中T/T组194例,C/T组8例。与G/A组和A/A组相比,G/G组术后7 d NRS均降低(均P<0.05);术后1和3 d NRS、PCA按压次数、舒芬太尼用量、术后开始疼痛的时间、术后不良反应、术后住院时间及术后3个月慢性疼痛的发生率差异均无统计学意义(均P>0.05)。与T/T组相比,C/T组术后1和3 d NRS、PCA按压次数、舒芬太尼用量增加,差异均有统计学意义(均P<0.05);术后7 d NRS、术后开始疼痛的时间、术后不良反应、术后住院时间及术后3个月慢性疼痛的发生率比较差异均无统计学意义(均P>0.05)。结论:CPB6*4和OPRM1基因多态性是引起膝关节镜患者术后舒芬太尼静脉镇痛个体化差异的遗传因素。展开更多
This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,suppo...This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,support vector machines,and neural networks,the prediction of the default rate was evaluated.The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%.Unlike other studies on the same topic,this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions.For the first-time,default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics.The findings highlighted that gross domestic product,exports,trade type,and trade bands are significant factors that explain defaults.Furthermore,the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment.The results showed that price reflects a significantly higher implicit probability of default than observed default,which in turn suggests that underlying factors exist besides the borrowers’probability of default.展开更多
文摘P2P2B模式下云服务投入是工业互联网(industrial internet of things,IIoT)平台的关键战略决策之一.构建由IIoT平台、龙头企业、潜在客户构成的演化博弈模型,研究IIoT平台在公有云研发投入和私有云研发投入中的策略选择,及其与龙头企业的生态合作问题.结果表明:虽然公有云存在数据泄露隐患,但较高的规模收益仍会吸引IIoT平台投入公有云研发,而平台搭建期内龙头企业的高合作意愿会促使平台投入私有云,随着龙头企业合作研发的比例增加,平台又将改变其投入策略.驱动龙头企业合作的因素可以是成本收益、技术提升等直接因素,也可以是规模收益、数据泄露概率等间接因素.最后,基于平台生命周期探讨了初创期、平台搭建期与生态系统期IIoT平台的系统稳定策略,并得到相应的管理启示.
文摘目的:舒芬太尼镇痛效果存在个体差异。本研究旨在评价CYP2B6(CPB6)*4和OPRM1基因多态性对膝关节镜下患者术后舒芬太尼静脉镇痛效应的影响。方法:选择择期腰麻硬膜外联合麻醉下行膝关节镜前交叉韧带重建术患者210例,患者术后使用静脉镇痛泵。术前采集2 mL静脉血置入抗凝管,留作检测CPB6*4和OPRM1的基因多态性,根据CPB6*4和OPRM1基因的不同基因组将患者分为A/A组、G/A组、G/G组、T/T组和C/T组,观察不同基因组患者术后疼痛数字评分(Numerical Rating Scale,NRS)、病人自控镇痛(patient controlled analgesia,PCA)按压次数、舒芬太尼用量、术后开始出现疼痛的时间以及术后不良事件的发生率。结果:共纳入202例患者,CPB6*4rs2279343中A/A组76例,G/A组110例,G/G组16例;OPRM1 rs73568641中T/T组194例,C/T组8例。与G/A组和A/A组相比,G/G组术后7 d NRS均降低(均P<0.05);术后1和3 d NRS、PCA按压次数、舒芬太尼用量、术后开始疼痛的时间、术后不良反应、术后住院时间及术后3个月慢性疼痛的发生率差异均无统计学意义(均P>0.05)。与T/T组相比,C/T组术后1和3 d NRS、PCA按压次数、舒芬太尼用量增加,差异均有统计学意义(均P<0.05);术后7 d NRS、术后开始疼痛的时间、术后不良反应、术后住院时间及术后3个月慢性疼痛的发生率比较差异均无统计学意义(均P>0.05)。结论:CPB6*4和OPRM1基因多态性是引起膝关节镜患者术后舒芬太尼静脉镇痛个体化差异的遗传因素。
基金the funding provided by the Galician Regional Government[ED431C 2020/18]co-funded by the European Regional Development Fund(ERDF/FEDER)within the period 2020-2023.
文摘This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,support vector machines,and neural networks,the prediction of the default rate was evaluated.The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%.Unlike other studies on the same topic,this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions.For the first-time,default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics.The findings highlighted that gross domestic product,exports,trade type,and trade bands are significant factors that explain defaults.Furthermore,the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment.The results showed that price reflects a significantly higher implicit probability of default than observed default,which in turn suggests that underlying factors exist besides the borrowers’probability of default.
