In the current paper, I present probably the simplest possible abstract formal proof that P ≠ NP, and NP = EXPTIME, in the context of the standard mathematical set theory of computational complexity and deterministic...In the current paper, I present probably the simplest possible abstract formal proof that P ≠ NP, and NP = EXPTIME, in the context of the standard mathematical set theory of computational complexity and deterministic Turing machines. My previous publications about the solution of the P vs. NP with the same result NP = EXPTIME, to be fully correct and understandable need the Lemma 4.1 and its proof of the current paper. The arguments of the current paper in order to prove NP = EXPTME are even simpler than in my previous publications. The strategy to solve the P vs. NP problem in the current paper (and in my previous publications) is by starting with an EXPTIME-complete language (problem) and proving that it has a re-formulation as an NP-class language, thus NP = EXPTIME. The main reason that the scientific community has missed so far such a simple proof, is because of two factors 1) It has been tried extensively but in vain to simplify the solutions of NP-complete problems from exponential time algorithms to polynomial time algorithms (which would be a good strategy only if P = NP) 2) It is believed that the complexity class NP is strictly a subclass to the complexity class EXPTIME (in spite the fact that any known solution to any of the NP-complete problems is not less than exponential). The simplicity of the current solution would have been missed if 2) was to be believed true. So far the majority of the relevant scientific community has considered this famous problem not yet solved. The present results definitely solve the 3rd Clay Millennium Problem about P versus NP in a simple, abstract and transparent way that the general scientific community, but also the experts of the area, can follow, understand and therefore become able to accept.展开更多
目的探讨β胶原特殊序列(β-crosslaps,β-CTX)、总1型胶原氨基端延长肽(t-P1NP)及N端中段骨钙素(N-MID-OT)在绝经后女性骨质疏松性骨折风险评估中的应用价值。方法回顾性分析2020年2月至2022年12月解放军总医院第六医学中心收治的102...目的探讨β胶原特殊序列(β-crosslaps,β-CTX)、总1型胶原氨基端延长肽(t-P1NP)及N端中段骨钙素(N-MID-OT)在绝经后女性骨质疏松性骨折风险评估中的应用价值。方法回顾性分析2020年2月至2022年12月解放军总医院第六医学中心收治的102例绝经后骨质疏松(postmenopausal osteoporosis,PMOP)患者的临床资料,将其作为研究组,并根据是否发生骨折将其分为PMOP骨折组(39例)与PMOP未骨折组(63例),另选100名健康体检者作为对照组。记录所有研究对象的β-CTX、t-P1NP及N-MID-OT水平并进行比较,用ROC曲线评价β-CTX、t-P1NP及N-MID-OT在绝经后女性骨质疏松性骨折风险评估中的应用价值。结果研究组的平均体重、体质量指数、股骨颈骨密度(bone mineral density,BMD)、左髋总和BMD及L 1~4总和BMD均明显低于对照组(P<0.05);而两组研究对象的平均年龄、平均身高及绝经年龄等比较差异均无统计学意义(P>0.05)。研究组的β-CTX、t-P1NP及N-MID-OT均明显高于对照组(t值依次为12.688、37.430、26.599,P<0.05)。β-CTX+t-P1NP+N-MID-OT联合检测用于预测PMOP的AUC值为0.978,敏感度为98.04%,特异度为97.00%,表明β-CTX+t-P1NP+N-MID-OT三指标联合检测用于预测PMOP的效能更高。PMOP骨折组的β-CTX、t-P1NP及N-MID-OT均明显高于PMOP未骨折组(t值依次为6.078、16.363、12.227,P<0.05)。β-CTX+t-P1NP+N-MID-OT联合检测用于评估PMOP骨折风险的AUC值为0.939,敏感度为94.87%,特异度为95.24%,表明β-CTX+t-P1NP+N-MID-OT联合检测用于评估PMOP骨折风险的效能更高。结论PMOP患者β-CTX、t-P1NP及N-MID-OT水平均明显升高,而PMOP骨折患者β-CTX、t-P1NP及N-MID-OT水平升高更为明显,且β-CTX+t-P1NP+N-MID-OT联合检测可显著提高对PMOP预测及骨折风险评估效能,值得借鉴。展开更多
5G,8K视频等新业务类型不断涌现,使得网络处理器(network processor,NP)的应用场景日趋复杂多样.为满足多样化网络应用在性能、灵活性以及服务质量保证等方面的差异化需求,传统NP试图在片上系统(system on chip,SoC)上集成大量处理器核...5G,8K视频等新业务类型不断涌现,使得网络处理器(network processor,NP)的应用场景日趋复杂多样.为满足多样化网络应用在性能、灵活性以及服务质量保证等方面的差异化需求,传统NP试图在片上系统(system on chip,SoC)上集成大量处理器核、高速缓存、加速器等异质处理资源,提供面向多样化应用场景的敏捷可定制能力.然而,随着摩尔定律和登纳德缩放定律失效问题的逐渐凸显,单片NP芯片研制在研发周期、成本、创新迭代等方面面临巨大挑战,越来越难以为继.针对上述问题,提出新型敏捷可定制NP架构ChipletNP,基于芯粒化(Chiplet)技术解耦异质资源,在充分利用成熟芯片产品及工艺的基础上,通过多个芯粒组合,满足不同应用场景下NP的快速定制和演化发展需求.基于ChipletNP设计实现了一款集成商用CPU、FPGA(field programmable gate array)和自研敏捷交换芯粒的银河衡芯敏捷NP芯片(YHHX-NP).基于该芯片的应用部署与实验结果表明,ChipletNP可支持NP的快速敏捷定制,能够有效承载SRv6(segment routing over IPv6)等新型网络协议与网络功能部署.其中,核心的敏捷交换芯粒相较于同级商用芯片能效比提升2倍以上,延迟控制在2.82μs以内,可以有效支持面向NP的Chiplet统一通信与集成.展开更多
目的:检测慢性心力衰竭(CHF)患者血清Ⅲ型前胶原氨基末端前肽(N-terminal peptide of typeⅢprocollagen,PⅢNP)、高迁移率族蛋白B1(high mobility group box-1,HMGB1)的表达水平,探讨二者与左室射血分数(LVEF)的相关性以及对CHF的诊断...目的:检测慢性心力衰竭(CHF)患者血清Ⅲ型前胶原氨基末端前肽(N-terminal peptide of typeⅢprocollagen,PⅢNP)、高迁移率族蛋白B1(high mobility group box-1,HMGB1)的表达水平,探讨二者与左室射血分数(LVEF)的相关性以及对CHF的诊断价值。方法:收集2021年12月至2022年11月期间于佳木斯大学附属第一医院心内科住院治疗的慢性心力衰竭患者90例为实验组,按照LVEF将其分为HFrEF组(n=33)、HFmrEF组(n=27)和HFpEF组(n=30)。此外选取排除心功能不全诊断的同期住院患者30例为对照组。对比各组患者血清中氨基末端脑钠肽前体(NT-proBNP)、PⅢNP、HMGB1水平的差异,分析PⅢNP、HMGB1水平与心功能指标的相关性,并借助ROC曲线评估NT-proBNP、HMGB1、PⅢNP单独以及联合应用对不同表型CHF患者的诊断价值。结果:HFrEF组PⅢNP、HMGB1及NT-proBNP水平均高于HFpEF组和对照组,差异有统计学意义(P<0.05)。PⅢNP与HMGB1呈正相关(P<0.05);PⅢNP、HMGB1均与NT-proBNP、LVEDD呈正相关,与LVEF呈明显负相关(P<0.05);HMGB1与LAD呈正相关(P<0.05),PⅢNP与LAD无明显相关性(P>0.05)。血清NT-proBNP、HMGB1、PⅢNP水平诊断HFrEF患者的AUC分别为0.867、0.871、0.779;诊断HFmrEF患者的AUC分别为0.840、0.804、0.760;诊断HFpEF患者的AUC分别为0.851、0.728、0.769。多生物标志物模型NT-proBNP+PⅢNP、NT-proBNP+HMGB1、NT-proBNP+PⅢNP+HMGB1诊断HFrEF患者的AUC分别为0.887、0.954、0.954;诊断HFmrEF患者的AUC分别为0.942、0.937、0.951;诊断HFpEF患者的AUC分别为0.904、0.910、0.914。结论:CHF患者血清PⅢNP、HMGB1明显升高,并且,CHF患者血清PⅢNP、HMGB1水平与心脏功能指标具有良好的相关性,说明PⅢNP、HMGB1可反映疾病的严重程度。PⅢNP、HMGB1对各表型CHF患者均具有诊断价值,并且,多生物标志物联合检测能提高对CHF患者诊断的敏感性。展开更多
文摘In the current paper, I present probably the simplest possible abstract formal proof that P ≠ NP, and NP = EXPTIME, in the context of the standard mathematical set theory of computational complexity and deterministic Turing machines. My previous publications about the solution of the P vs. NP with the same result NP = EXPTIME, to be fully correct and understandable need the Lemma 4.1 and its proof of the current paper. The arguments of the current paper in order to prove NP = EXPTME are even simpler than in my previous publications. The strategy to solve the P vs. NP problem in the current paper (and in my previous publications) is by starting with an EXPTIME-complete language (problem) and proving that it has a re-formulation as an NP-class language, thus NP = EXPTIME. The main reason that the scientific community has missed so far such a simple proof, is because of two factors 1) It has been tried extensively but in vain to simplify the solutions of NP-complete problems from exponential time algorithms to polynomial time algorithms (which would be a good strategy only if P = NP) 2) It is believed that the complexity class NP is strictly a subclass to the complexity class EXPTIME (in spite the fact that any known solution to any of the NP-complete problems is not less than exponential). The simplicity of the current solution would have been missed if 2) was to be believed true. So far the majority of the relevant scientific community has considered this famous problem not yet solved. The present results definitely solve the 3rd Clay Millennium Problem about P versus NP in a simple, abstract and transparent way that the general scientific community, but also the experts of the area, can follow, understand and therefore become able to accept.
文摘目的探讨β胶原特殊序列(β-crosslaps,β-CTX)、总1型胶原氨基端延长肽(t-P1NP)及N端中段骨钙素(N-MID-OT)在绝经后女性骨质疏松性骨折风险评估中的应用价值。方法回顾性分析2020年2月至2022年12月解放军总医院第六医学中心收治的102例绝经后骨质疏松(postmenopausal osteoporosis,PMOP)患者的临床资料,将其作为研究组,并根据是否发生骨折将其分为PMOP骨折组(39例)与PMOP未骨折组(63例),另选100名健康体检者作为对照组。记录所有研究对象的β-CTX、t-P1NP及N-MID-OT水平并进行比较,用ROC曲线评价β-CTX、t-P1NP及N-MID-OT在绝经后女性骨质疏松性骨折风险评估中的应用价值。结果研究组的平均体重、体质量指数、股骨颈骨密度(bone mineral density,BMD)、左髋总和BMD及L 1~4总和BMD均明显低于对照组(P<0.05);而两组研究对象的平均年龄、平均身高及绝经年龄等比较差异均无统计学意义(P>0.05)。研究组的β-CTX、t-P1NP及N-MID-OT均明显高于对照组(t值依次为12.688、37.430、26.599,P<0.05)。β-CTX+t-P1NP+N-MID-OT联合检测用于预测PMOP的AUC值为0.978,敏感度为98.04%,特异度为97.00%,表明β-CTX+t-P1NP+N-MID-OT三指标联合检测用于预测PMOP的效能更高。PMOP骨折组的β-CTX、t-P1NP及N-MID-OT均明显高于PMOP未骨折组(t值依次为6.078、16.363、12.227,P<0.05)。β-CTX+t-P1NP+N-MID-OT联合检测用于评估PMOP骨折风险的AUC值为0.939,敏感度为94.87%,特异度为95.24%,表明β-CTX+t-P1NP+N-MID-OT联合检测用于评估PMOP骨折风险的效能更高。结论PMOP患者β-CTX、t-P1NP及N-MID-OT水平均明显升高,而PMOP骨折患者β-CTX、t-P1NP及N-MID-OT水平升高更为明显,且β-CTX+t-P1NP+N-MID-OT联合检测可显著提高对PMOP预测及骨折风险评估效能,值得借鉴。
文摘5G,8K视频等新业务类型不断涌现,使得网络处理器(network processor,NP)的应用场景日趋复杂多样.为满足多样化网络应用在性能、灵活性以及服务质量保证等方面的差异化需求,传统NP试图在片上系统(system on chip,SoC)上集成大量处理器核、高速缓存、加速器等异质处理资源,提供面向多样化应用场景的敏捷可定制能力.然而,随着摩尔定律和登纳德缩放定律失效问题的逐渐凸显,单片NP芯片研制在研发周期、成本、创新迭代等方面面临巨大挑战,越来越难以为继.针对上述问题,提出新型敏捷可定制NP架构ChipletNP,基于芯粒化(Chiplet)技术解耦异质资源,在充分利用成熟芯片产品及工艺的基础上,通过多个芯粒组合,满足不同应用场景下NP的快速定制和演化发展需求.基于ChipletNP设计实现了一款集成商用CPU、FPGA(field programmable gate array)和自研敏捷交换芯粒的银河衡芯敏捷NP芯片(YHHX-NP).基于该芯片的应用部署与实验结果表明,ChipletNP可支持NP的快速敏捷定制,能够有效承载SRv6(segment routing over IPv6)等新型网络协议与网络功能部署.其中,核心的敏捷交换芯粒相较于同级商用芯片能效比提升2倍以上,延迟控制在2.82μs以内,可以有效支持面向NP的Chiplet统一通信与集成.
文摘目的:检测慢性心力衰竭(CHF)患者血清Ⅲ型前胶原氨基末端前肽(N-terminal peptide of typeⅢprocollagen,PⅢNP)、高迁移率族蛋白B1(high mobility group box-1,HMGB1)的表达水平,探讨二者与左室射血分数(LVEF)的相关性以及对CHF的诊断价值。方法:收集2021年12月至2022年11月期间于佳木斯大学附属第一医院心内科住院治疗的慢性心力衰竭患者90例为实验组,按照LVEF将其分为HFrEF组(n=33)、HFmrEF组(n=27)和HFpEF组(n=30)。此外选取排除心功能不全诊断的同期住院患者30例为对照组。对比各组患者血清中氨基末端脑钠肽前体(NT-proBNP)、PⅢNP、HMGB1水平的差异,分析PⅢNP、HMGB1水平与心功能指标的相关性,并借助ROC曲线评估NT-proBNP、HMGB1、PⅢNP单独以及联合应用对不同表型CHF患者的诊断价值。结果:HFrEF组PⅢNP、HMGB1及NT-proBNP水平均高于HFpEF组和对照组,差异有统计学意义(P<0.05)。PⅢNP与HMGB1呈正相关(P<0.05);PⅢNP、HMGB1均与NT-proBNP、LVEDD呈正相关,与LVEF呈明显负相关(P<0.05);HMGB1与LAD呈正相关(P<0.05),PⅢNP与LAD无明显相关性(P>0.05)。血清NT-proBNP、HMGB1、PⅢNP水平诊断HFrEF患者的AUC分别为0.867、0.871、0.779;诊断HFmrEF患者的AUC分别为0.840、0.804、0.760;诊断HFpEF患者的AUC分别为0.851、0.728、0.769。多生物标志物模型NT-proBNP+PⅢNP、NT-proBNP+HMGB1、NT-proBNP+PⅢNP+HMGB1诊断HFrEF患者的AUC分别为0.887、0.954、0.954;诊断HFmrEF患者的AUC分别为0.942、0.937、0.951;诊断HFpEF患者的AUC分别为0.904、0.910、0.914。结论:CHF患者血清PⅢNP、HMGB1明显升高,并且,CHF患者血清PⅢNP、HMGB1水平与心脏功能指标具有良好的相关性,说明PⅢNP、HMGB1可反映疾病的严重程度。PⅢNP、HMGB1对各表型CHF患者均具有诊断价值,并且,多生物标志物联合检测能提高对CHF患者诊断的敏感性。
