The aim of the present study was to identify multi-decadal variability （MDV） relative to the current centennial global warming trend in available observation data.The centennial global wanning trend was first identi...The aim of the present study was to identify multi-decadal variability （MDV） relative to the current centennial global warming trend in available observation data.The centennial global wanning trend was first identified in the global mean surface temperature （STgm） data.The MDV was identified based on three sets of climate variables,including sea surface temperature （SST）,ocean temperature from the surface to 700 m,and the NCEP and ERA40 reanalysis datasets,respectively.All variables were detrended and low-pass filtered.Through three independent EOF analyses of the filtered variables,all results consistently showed two dominant modes,with their respective temporal variability resembling the Pacific Decadal Oscillation/Inter-decadal Pacific Oscillation （PDO/IPO） and the Atlantic Multi-decadal Oscillation （AMO）.The spatial structure of the PDO-like oscillation is characterized by an ENSO-like structure and hemispheric symmetric features.The structure associated with the AMO-like oscillation exhibits hemispheric asymmetric features with anomalous warm air over Eurasia and warm SST in the Atlantic and Pacific basin north of 10°S,and cold SST over the southern oceans.The Pacific and Atlantic MDV in upper-ocean temperature suggest that they are mutually linked.We also found that the PDO-like and AMO-like oscillations are almost equally important in global-scale MDV by EOF analyses.In the period 1975-2005,the evolution of the two oscillations has given rise to strong temperature trends and has contributed almost half of the STgm warming.Hereon,in the next decade,the two oscillations are expected to slow down the global warming trends.展开更多
The International Centre on Global-scale Geochemistry (ICGG)is a research organization to provide systematic,longterm and authoritative global for sustaining natural resources geochemical observation data and environm...The International Centre on Global-scale Geochemistry (ICGG)is a research organization to provide systematic,longterm and authoritative global for sustaining natural resources geochemical observation data and environments in the world.展开更多
The introduction of DeepSeek R1,an AI language model developed by the Chinese AI lab DeepSeek,has made a significant impact in the tech world[1].Within a week of its release,the app surged to the top of download chart...The introduction of DeepSeek R1,an AI language model developed by the Chinese AI lab DeepSeek,has made a significant impact in the tech world[1].Within a week of its release,the app surged to the top of download charts,triggered a massive$1 trillion(£800 billion)sell-off in tech stocks,and prompted intense reactions from Silicon Valley.As artificial intelligence(AI)continues to evolve rapidly,it has become a cornerstone of global technological progress,with nations vying to push the boundaries of what AI can achieve.While companies like OpenAI and Nvidia in the United States have led AI research and deployment,the rise of DeepSeek represents a noteworthy shift in the landscape.DeepSeek’s innovative use of reinforcement learning(RL)and model distillation has significantly enhanced the reasoning capabilities of large language models(LLMs),while also advancing more efficient algorithms that reduce computing resource and energy consumption.This paper explores the factors behind DeepSeek’s success and its broader impact on making AI more accessible and efficient,especially for the developing world.By contributing to AI’s global accessibility,China’s advancements hold great potential to positively transform diverse sectors,from agriculture to energy and healthcare,supporting the goal of peaceful coexistence and improving life around the globe.展开更多
堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scal...堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scale temporal ConvTransformer,UnMS-TCT)网络用于输送机堆煤检测。首先融合RGB帧和光流帧提取的特征,使网络更全面地建模时空关系;然后在时序编码器中,将动态位置嵌入(dynamic position embedding,DPE),多头关系聚合器(multi-head relation aggregator,MHRA)以及多层感知机(multilayer perceptron,MLP)组成的全局模块,交叉注意力(cross-attention,CA)组成的局部模块,以交替方式形成全局-局部关系模块,增强多尺度下获取全局和局部时间关系的能力;其次利用残差全局-局部融合(residual global and local fusion,ResGLFus)模块融合多尺度特征,有效地提高融合过程的稳定性,最终实现高精度堆煤预测。实验结果表明:该方法能够实现对输送机堆煤的检测,mAP达到98.17%。展开更多
针对火焰检测过程中存在小目标难以检测的问题,提出了一种改进的YOLOv8n模型。首先,在双分支跨阶段局部特征融合(cross stage partial 2 with feature fusion,C2f)模块中加入动态蛇形卷积,有助于提取多尺度特征、增强特征表示。接着,将G...针对火焰检测过程中存在小目标难以检测的问题,提出了一种改进的YOLOv8n模型。首先,在双分支跨阶段局部特征融合(cross stage partial 2 with feature fusion,C2f)模块中加入动态蛇形卷积,有助于提取多尺度特征、增强特征表示。接着,将GhostnetV2引入到颈部网络中,不仅减少了模型的参数量,还提升了整体的检测精度和速度。然后,添加微小目标检测头以便更好地进行多尺度小目标的检测,基于局部和全局的挤压激励(squeeze and excitation,SE)注意力机制确保每一层的特征都得到充分优化,特别是小目标的细微特征。最后,基于最小点距离的交并比损失函数提高算法的收敛速度和定位精度。实验结果显示,改进YOLOv8n模型的P、R、FPS、mAP@0.5和mAP@0.5∶0.95指标平均值比YOLOv8n模型分别提高了3.34%、3.62%、14帧/s、3.01%和3.41%,表明模型拥有较好的小目标火焰检测能力。研究结果可为预防火灾等安全事故提供理论依据和决策支撑。展开更多
基金supported by the National Science Council (Grant No. NSC 98-2745-M-002-011-ASP)the National Basic Research Program "973" (Grant No. 2010CB950401, 2012CB955204)+1 种基金the research foundation of NUIST, the National Natural Science Foundation of China (Grant No. 41005047)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘The aim of the present study was to identify multi-decadal variability （MDV） relative to the current centennial global warming trend in available observation data.The centennial global wanning trend was first identified in the global mean surface temperature （STgm） data.The MDV was identified based on three sets of climate variables,including sea surface temperature （SST）,ocean temperature from the surface to 700 m,and the NCEP and ERA40 reanalysis datasets,respectively.All variables were detrended and low-pass filtered.Through three independent EOF analyses of the filtered variables,all results consistently showed two dominant modes,with their respective temporal variability resembling the Pacific Decadal Oscillation/Inter-decadal Pacific Oscillation （PDO/IPO） and the Atlantic Multi-decadal Oscillation （AMO）.The spatial structure of the PDO-like oscillation is characterized by an ENSO-like structure and hemispheric symmetric features.The structure associated with the AMO-like oscillation exhibits hemispheric asymmetric features with anomalous warm air over Eurasia and warm SST in the Atlantic and Pacific basin north of 10°S,and cold SST over the southern oceans.The Pacific and Atlantic MDV in upper-ocean temperature suggest that they are mutually linked.We also found that the PDO-like and AMO-like oscillations are almost equally important in global-scale MDV by EOF analyses.In the period 1975-2005,the evolution of the two oscillations has given rise to strong temperature trends and has contributed almost half of the STgm warming.Hereon,in the next decade,the two oscillations are expected to slow down the global warming trends.
文摘The International Centre on Global-scale Geochemistry (ICGG)is a research organization to provide systematic,longterm and authoritative global for sustaining natural resources geochemical observation data and environments in the world.
文摘The introduction of DeepSeek R1,an AI language model developed by the Chinese AI lab DeepSeek,has made a significant impact in the tech world[1].Within a week of its release,the app surged to the top of download charts,triggered a massive$1 trillion(£800 billion)sell-off in tech stocks,and prompted intense reactions from Silicon Valley.As artificial intelligence(AI)continues to evolve rapidly,it has become a cornerstone of global technological progress,with nations vying to push the boundaries of what AI can achieve.While companies like OpenAI and Nvidia in the United States have led AI research and deployment,the rise of DeepSeek represents a noteworthy shift in the landscape.DeepSeek’s innovative use of reinforcement learning(RL)and model distillation has significantly enhanced the reasoning capabilities of large language models(LLMs),while also advancing more efficient algorithms that reduce computing resource and energy consumption.This paper explores the factors behind DeepSeek’s success and its broader impact on making AI more accessible and efficient,especially for the developing world.By contributing to AI’s global accessibility,China’s advancements hold great potential to positively transform diverse sectors,from agriculture to energy and healthcare,supporting the goal of peaceful coexistence and improving life around the globe.
文摘堆煤是输送机常见故障之一,为了保障煤矿工业生产的安全,需要对煤矿井下输送机的堆煤情况进行检测。然而现有的检测方法存在容易误触、检测可靠性较差等缺点,针对这些问题提出一种基于Transformer统一多尺度时序卷积(unified multi-scale temporal ConvTransformer,UnMS-TCT)网络用于输送机堆煤检测。首先融合RGB帧和光流帧提取的特征,使网络更全面地建模时空关系;然后在时序编码器中,将动态位置嵌入(dynamic position embedding,DPE),多头关系聚合器(multi-head relation aggregator,MHRA)以及多层感知机(multilayer perceptron,MLP)组成的全局模块,交叉注意力(cross-attention,CA)组成的局部模块,以交替方式形成全局-局部关系模块,增强多尺度下获取全局和局部时间关系的能力;其次利用残差全局-局部融合(residual global and local fusion,ResGLFus)模块融合多尺度特征,有效地提高融合过程的稳定性,最终实现高精度堆煤预测。实验结果表明:该方法能够实现对输送机堆煤的检测,mAP达到98.17%。
文摘针对火焰检测过程中存在小目标难以检测的问题,提出了一种改进的YOLOv8n模型。首先,在双分支跨阶段局部特征融合(cross stage partial 2 with feature fusion,C2f)模块中加入动态蛇形卷积,有助于提取多尺度特征、增强特征表示。接着,将GhostnetV2引入到颈部网络中,不仅减少了模型的参数量,还提升了整体的检测精度和速度。然后,添加微小目标检测头以便更好地进行多尺度小目标的检测,基于局部和全局的挤压激励(squeeze and excitation,SE)注意力机制确保每一层的特征都得到充分优化,特别是小目标的细微特征。最后,基于最小点距离的交并比损失函数提高算法的收敛速度和定位精度。实验结果显示,改进YOLOv8n模型的P、R、FPS、mAP@0.5和mAP@0.5∶0.95指标平均值比YOLOv8n模型分别提高了3.34%、3.62%、14帧/s、3.01%和3.41%,表明模型拥有较好的小目标火焰检测能力。研究结果可为预防火灾等安全事故提供理论依据和决策支撑。
