引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

企业高质量发展视角下上市公司内部控制效率评价

内部控制评价对于企业自我完善和发展至关重要,但我国企业内部控制评价难以满足企业高质量发展需要,这主要是由于缺少科学的评价思路和方法。本文基于系统观构建内部控制效率评价指标体系,并运用网络DEA模型与Global Malmquist指数对2019~2021年我国1653家制造业A股非∗ST上市公司的内部控制效率进行实证分析。结果表明:应用网络DEA模型构建的内部控制效率指数更符合企业高质量发展的要求。分析发现,企业的内部环境、风险评估和控制活动效率易受外部环境影响,内部控制效率提升依赖于企业对相关规范的应用能力。此外,内部控制效率与企业资产规模、成立年限和股权制衡度负相关,与独立董事比例、机构投资者持股比例和管理层持股比例正相关,且非国有企业比国有企业具有更高的内部控制效率。这些发现验证了网络DEA模型在评价上市公司内部控制效率方面的科学性和合理性,为企业内部控制的改进提供了明确的方向和依据。

Oruxmaps软件结合Global Mapper软件在矿产资源行业并购中的应用

纸质地质图结合手持GPS,采用手工方式野外记录的传统地质调查工作方式已不能适应数字化和信息化的要求,在矿产资源并购项目中,运用Oruxmaps、Global Mapper等软件,可以制作适应地质调查工作所特需的离线地质图,实现地质图、GPS定位、路线轨迹记录、文字记录、影像记录等的有机结合,形成了一套行之有效的工作流程,极大地提高了路线地质调查工作的质量和效率。

Global Mapper在水库库容计算及淤积分析中的应用研究

基于快捷精准计算水库库容,并与原始库容对比分析库容淤积的目的,使用Global Mapper软件的库容计算和DEM叠加分析功能,通过水库库容复核工程实例进行应用试验,结果表明使用Global Mapper计算库容的结果精度与等高线法的库容结果相当,计算高效、结果可靠,DEM叠加分析成果可以直观展示库容淤积分布,有助于为管理单位冲淤冲沙提供决策。

融合RoBERTa-WWM和全局指针网络的农业病害实体关系联合抽取研究

针对实体和关系抽取过程中存在的一词多义、实体嵌套、三元组重叠的问题,本文提出了1种融合RoBERTa-WWM和全局指针网络的联合抽取模型RBGPL。该模型引入RoBERTa-WWM预训练模型,利用上下文的语境信息融合克服了不同语境下一词多义问题;采用全局指针网络Global pointer标注方式解决了实体嵌套问题;通过全局指针联合解码模型将三重抽取转变为五重提取,解决了三元组重叠问题。在自建农业病害数据集上,模型RBGPL的精确率、召回率、F1值达到76.23%,91.18%,83.04%,与其他联合抽取模型相对比F1值均取最优,有效地克服了一词多义问题和三元组重叠问题。此外,在病原(Pathogeny)和作物名称(Crop)2种易嵌套实体的F1值上提升了3%和18%,实体嵌套得到了显著缓解。本文方法提高了中文农业病害领域实体关系抽取性能,可为农业病害领域知识图谱的构建提供技术支持。

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

Global hybrid simulations of soft X-ray emissions in the Earth’s magnetosheath

Earth’s magnetopause is a thin boundary separating the shocked solar wind plasma from the magnetospheric plasmas,and it is also the boundary of the solar wind energy transport to the magnetosphere.Soft X-ray imaging allows investigation of the large-scale magnetopause by providing a two-dimensional(2-D)global view from a satellite.By performing 3-D global hybrid-particle-in-cell(hybrid-PIC)simulations,we obtain soft X-ray images of Earth’s magnetopause under different solar wind conditions,such as different plasma densities and directions of the southward interplanetary magnetic field.In all cases,magnetic reconnection occurs at low latitude magnetopause.The soft X-ray images observed by a hypothetical satellite are shown,with all of the following identified:the boundary of the magnetopause,the cusps,and the magnetosheath.Local X-ray emissivity in the magnetosheath is characterized by large amplitude fluctuations(up to 160%);however,the maximum line-of-sight-integrated X-ray intensity matches the tangent directions of the magnetopause well,indicating that these fluctuations have limited impact on identifying the magnetopause boundary in the X-ray images.Moreover,the magnetopause boundary can be identified using multiple viewing geometries.We also find that solar wind conditions have little effect on the magnetopause identification.The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will provide X-ray images of the magnetopause for the first time,and our global hybrid-PIC simulation results can help better understand the 2-D X-ray images of the magnetopause from a 3-D perspective,with particle kinetic effects considered.

基于超效率SBM-GML指数模型的冷链物流绿色发展效率

【目的】将环境规制纳入冷链物流效率分析框架,对我国冷链物流绿色低碳发展效率展开综合测度,为促进冷链物流绿色、高效、可持续发展提供理论参考。【方法】本研究运用非期望产出超效率SBM模型方法和Global Malmquist-lunberger指数法对2012—2020年全国30个省区(市)的冷链物流绿色发展效率进行静态测度与动态分析,并构建“绿色发展效率-全要素生产率GML指数”矩阵,综合比较各省份冷链物流绿色发展效率的优势与不足。【结果】我国的冷链物流绿色发展效率水平整体不高,2020年达到有效的仅有河北、山西、辽宁和上海,并且差距逐渐拉大。分区域看,我国东中西3大区域冷链物流绿色发展效率整体上呈东部地区>中部地区>西部地区的态势。东中部地区冷链物流绿色发展效率呈小幅波动上升趋势,西部地区呈小幅波动下降趋势;现阶段各省份冷链物流绿色发展效率的提升主要依赖技术进步,不同的效率水平对纯技术效率和规模效率的依赖程度不同;通过矩阵分析,全国30个省份被分为稳定型、活跃型、低谷型和潜力型4类。【结论】降低我国冷链物流行业碳排放,提升冷链物流绿色发展效率,应采取以下措施:1)加快绿色技术创新,推动冷链物流业绿色转型;2)统筹发展规划,实现区域协调发展;3)贯彻绿色发展理念,引导各环节落实;4)完善基础设施建设,优化冷链物流网络。

‘Partly'globalized networks and driving mechanism in resource-based state-owned enterprises:A case study of J Group

In the context of economic globalization,while multinational enterprises from developed countries occupy a high-end position in the global value chain,enterprises from developing countries are often marginalized in the world market.In China,resource-based state-owned enterprises(SOEs)are tasked with the mission of safeguarding resource security,and their internationalization development ideas and strategic deployment are significantly and fundamentally different from those of other non-state-owned enterprises and large multinational corporations.This study provides ideas for the globalization policies of enterprises in developing countries.We consider J Group in western China as a case and discuss its productive investment and global production network development from 2010 to 2019.We found that J Group was‘Partly'globalized,and there are multiple core nodes with the characteristics of centralized and decentralized coexistence in the production network;in addition,the overall layout centre shifted to Southeast Asia and China;however,its global production was restricted by the enterprise's investment security considerations,support and restrictions of the home country,political security risk of the host country,and sanctions from the West.These findings provide insights for future research:under the wave of anti-globalization and'internal circulation as the main body',resource SOEs should consider the potential risk of investment,especially keeping the middle and downstream industrial chain in China as much as possible.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

Human brain organoid:trends,evolution,and remaining challenges

Advanced brain organoids provide promising platforms for deciphering the cellular and molecular processes of human neural development and diseases.Although various studies and reviews have described developments and advancements in brain organoids,few studies have comprehensively summarized and analyzed the global trends in this area of neuroscience.To identify and further facilitate the development of cerebral organoids,we utilized bibliometrics and visualization methods to analyze the global trends and evolution of brain organoids in the last 10 years.First,annual publications,countries/regions,organizations,journals,authors,co-citations,and keywords relating to brain organoids were identified.The hotspots in this field were also systematically identified.Subsequently,current applications for brain organoids in neuroscience,including human neural development,neural disorders,infectious diseases,regenerative medicine,drug discovery,and toxicity assessment studies,are comprehensively discussed.Towards that end,several considerations regarding the current challenges in brain organoid research and future strategies to advance neuroscience will be presented to further promote their application in neurological research.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Advances in ice avalanches on the Tibetan Plateau

As some of the greatest natural disasters in the cryosphere,ice avalanches(IAs)seriously threaten lives and cause catastrophic damage to the resource environment,but a comprehensive overview of the state of knowledge on IAs remains lacking.We summarized 63 IAs on the Tibetan Plateau(TP)since the 20th century,of which,over 20 IAs occurred after the 21st century.The distributions of IAs are mainly concentrated in the southeastern and northwestern TP,and the occurrence time of IAs is mostly concentrated from July to September.We highlight recent advances in mechanical properties and genetic mechanisms of IAs and emphasize that temperature,rainfall,and seismicity are the inducing factors.The failure modes of IAs are summarized into 6 categories by examples:slip pulling type,slip toppling type,slip breaking type,water level collapse type,cave roof collapse type,and wedge failure type.Finally,we deliver recommendations concerning the risk assessment and prediction of IAs.The results provide important scientific value for addressing climate change and resisting glacier-related hazards.

A Study on the Transmission Dynamics of the Omicron Variant of COVID-19 Using Nonlinear Mathematical Models

This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQIcRVW and SQIRV models,considering the delay in converting susceptible individuals into infected ones.The significant delays eventually resulted in the pandemic’s containment.To ensure the safety of the host population,this concept integrates quarantine and the COVID-19 vaccine.We investigate the stability of the proposed models.The fundamental reproduction number influences stability conditions.According to our findings,asymptomatic cases considerably impact the prevalence of Omicron infection in the community.The real data of the Omicron variant from Chennai,Tamil Nadu,India,is used to validate the outputs.

GLOBAL SOLUTIONS TO 1D COMPRESSIBLE NAVIER-STOKES/ALLEN-CAHN SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND FREE-BOUNDARY

This paper is concerned with the Navier-Stokes/Allen-Cahn system,which is used to model the dynamics of immiscible two-phase flows.We consider a 1D free boundary problem and assume that the viscosity coefficient depends on the density in the form ofη(ρ)=ρ^(α).The existence of unique global H^(2m)-solutions(m∈N)to the free boundary problem is proven for when 0<α<1/4.Furthermore,we obtain the global C^(∞)-solutions if the initial data is smooth.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Emerging global interest:Unraveling the link between diabetes mellitus and depression

BACKGROUND Studies have shown a strong bidirectional association between diabetes and depression,with diabetes increasing the risk of developing depression and vice versa.Depression among patients with diabetes is associated with poor glycemic control,complications,and poor self-care.AIM To explore the present state of research globally concerning diabetes and depression,to aid understanding the current research landscape and identify potential future areas of research.METHODS A bibliometric approach was used,utilizing the Scopus database to gather pertinent research articles released from 2004 to 2023.Analyses encompassed publication patterns,significant contributors,research focal points,prevalent themes,and the most influential articles,aimed at discerning emerging research subjects.RESULTS A total of 3229 publications that met the search criteria were identified.A significant increase in the number of publications related to diabetes and depression has been observed in the past two decades.The most productive nation was the USA(n=1015;31.43%),followed by China(n=325;10.07%),the UK(n=236;7.31%),and Germany(n=218;6.75%).Three principal themes in research on depression and diabetes were delineated by the analysis.First,the exploration of the elevated prevalence and etiology of this comorbidity;second,the focus on interventions,particularly randomized controlled trials,aimed at enhancing diabetes management among individuals with depression;and finally,the investigation of the involved risk factors and biological mechanisms under-lying this bidirectional relationship.CONCLUSION There has been a recent surge of interest in the relationship between diabetes and depression.This could aid researchers to identify areas lacking in the literature and shape future research.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.