This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recogni...This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.展开更多
BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular ...BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.展开更多
随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向...随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向块内冲突的方案缺乏高效的冲突检测和避免方法,同时现有研究往往忽略区块间冲突对吞吐量的不利影响.提出了一种Fabric的优化方案Fabric-HT(fabric with high throughput),从区块内和区块间2方面入手,有效降低事务间并发冲突和提高系统吞吐量.针对区块内事务冲突,提出了一种事务调度机制,根据块内冲突事务集定义了一种高效数据结构——依赖关系链,识别具有“危险结构”的事务并提前中止,合理调度事务和消除冲突;针对区块间事务冲突,将冲突事务检测提前至排序节点完成,建立以“推送-匹配”为核心的冲突事务早期避免机制.在多场景下开展大量实验,结果表明Fabric-HT在吞吐量、事务中止率、事务平均执行时间、无效事务空间占用率等方面均优于对比方案.Fabric-HT吞吐量最高可达Fabric的9.51倍,是最新优化方案FabricSharp的1.18倍;空间利用率上相比FabricSharp提升了14%.此外,Fabric-HT也表现出较好的鲁棒性和抗攻击能力.展开更多
为定量识别城市非信控环形交叉口区域内的机动车冲突风险易发生点,降低环形交叉口的事故发生率,本文构建针对非信控环形交叉口机动车冲突风险识别模型。首先,利用无人机采集高精度、连续的多车辆轨迹视频,结合Kinovea视频运动分析软件...为定量识别城市非信控环形交叉口区域内的机动车冲突风险易发生点,降低环形交叉口的事故发生率,本文构建针对非信控环形交叉口机动车冲突风险识别模型。首先,利用无人机采集高精度、连续的多车辆轨迹视频,结合Kinovea视频运动分析软件实现运行车辆状态识别与跟踪,并记录车辆每一帧的运动数据;其次,基于交通冲突识别指标TTC(Time to Collision),提出适应环形交叉口道路线形特征的车辆TTC计算方法,并使用累计频率法确定严重、一般和轻微冲突的阈值分别为1.2,2.8,4.4 s;最后,通过绘制高峰和平峰交通冲突空间异步图,并结合交通冲突数和严重冲突率,对环形交叉口的36个子区段进行交通冲突风险等级评定。研究结果显示:在高峰时段,某一子区段的平均交通冲突发生次数约为15次,严重冲突率为17.45%;在平峰时段,某一子区段的平均交通冲突发生次数约为8次,严重冲突率为8.28%。重度风险区域在高峰时段占比达到50%,而在平峰时段为8.33%,这些重度风险区域主要集中在交织区段。因此,环形交叉口在高峰时段且位于交织区段的情况更易发生交通事故。本文研究成果有助于交通管理部门了解环形交叉口在不同时段和区段上的交通冲突情况和特征,以便采取相应的预警和管理措施。展开更多
文摘This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.
文摘BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.
文摘随着区块链技术应用的普及,联盟链Hyperledger Fabric(简称Fabric)已成为知名区块链开源平台,并得到广泛关注.然而Fabric仍受困于并发事务间冲突问题,冲突发生时会引发大量无效交易上链,导致吞吐量下降,阻碍其发展.对于该问题,现有面向块内冲突的方案缺乏高效的冲突检测和避免方法,同时现有研究往往忽略区块间冲突对吞吐量的不利影响.提出了一种Fabric的优化方案Fabric-HT(fabric with high throughput),从区块内和区块间2方面入手,有效降低事务间并发冲突和提高系统吞吐量.针对区块内事务冲突,提出了一种事务调度机制,根据块内冲突事务集定义了一种高效数据结构——依赖关系链,识别具有“危险结构”的事务并提前中止,合理调度事务和消除冲突;针对区块间事务冲突,将冲突事务检测提前至排序节点完成,建立以“推送-匹配”为核心的冲突事务早期避免机制.在多场景下开展大量实验,结果表明Fabric-HT在吞吐量、事务中止率、事务平均执行时间、无效事务空间占用率等方面均优于对比方案.Fabric-HT吞吐量最高可达Fabric的9.51倍,是最新优化方案FabricSharp的1.18倍;空间利用率上相比FabricSharp提升了14%.此外,Fabric-HT也表现出较好的鲁棒性和抗攻击能力.
文摘为定量识别城市非信控环形交叉口区域内的机动车冲突风险易发生点,降低环形交叉口的事故发生率,本文构建针对非信控环形交叉口机动车冲突风险识别模型。首先,利用无人机采集高精度、连续的多车辆轨迹视频,结合Kinovea视频运动分析软件实现运行车辆状态识别与跟踪,并记录车辆每一帧的运动数据;其次,基于交通冲突识别指标TTC(Time to Collision),提出适应环形交叉口道路线形特征的车辆TTC计算方法,并使用累计频率法确定严重、一般和轻微冲突的阈值分别为1.2,2.8,4.4 s;最后,通过绘制高峰和平峰交通冲突空间异步图,并结合交通冲突数和严重冲突率,对环形交叉口的36个子区段进行交通冲突风险等级评定。研究结果显示:在高峰时段,某一子区段的平均交通冲突发生次数约为15次,严重冲突率为17.45%;在平峰时段,某一子区段的平均交通冲突发生次数约为8次,严重冲突率为8.28%。重度风险区域在高峰时段占比达到50%,而在平峰时段为8.33%,这些重度风险区域主要集中在交织区段。因此,环形交叉口在高峰时段且位于交织区段的情况更易发生交通事故。本文研究成果有助于交通管理部门了解环形交叉口在不同时段和区段上的交通冲突情况和特征,以便采取相应的预警和管理措施。
