Natural forests exhibit higher organic carbon concentrations and recalcitrant carbon proportions in soil than plantations:a global data synthesis

Different chemical compositions of soil organic carbon(SOC)affect its persistence and whether it signifi-cantly differs between natural forests and plantations remains unclear.By synthesizing 234 observations of SOC chemical compositions,we evaluated global patterns of concentra-tion,individual chemical composition(alkyl C,O-alkyl C,aromatic C,and carbonyl C),and their distribution even-ness.Our results indicate a notably higher SOC,a markedly larger proportion of recalcitrant alkyl C,and lower easily decomposed carbonyl C proportion in natural forests.How-ever,SOC chemical compositions were appreciably more evenly distributed in plantations.Based on the assumed con-ceptual index of SOC chemical composition evenness,we deduced that,compared to natural forests,plantations may have higher possible resistance to SOC decomposition under disturbances.In tropical regions,SOC levels,recalcitrant SOC chemical composition,and their distributed evenness were significantly higher in natural forests,indicating that SOC has higher chemical stability and possible resistance to decomposition.Climate factors had minor effects on alkyl C in forests globally,while they notably affected SOC chemi-cal composition in tropical forests.This could contribute to the differences in chemical compositions and their distrib-uted evenness between plantations and natural stands.

Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

Bad News—The Dominant Causes of the Earth’s Global Warming Are Processes on the Sun, and Humanity Can Do Nothing or Little to Stop It?

Arguments that global warming in the Earth’s atmosphere of the last 70 years is partially or entirely caused by changes in the solar magnetic field are presented in the work. Global warming is probably a consequence of ionizing radiation emitted from the Sun mainly in the “rise” phase of solar activity. The ionizing radiation is positively charged particles with high energy. They penetrate deep into the Earth’s atmosphere, creating increased content of ions serving as condensation nuclei. The condensation nuclei increase cloudiness in the lower atmosphere and lower the surface air temperature. When solar activity decreases as observed in the last 70 years, the reverse process occurs— cloud cover decreases, more solar electromagnetic radiation reaches the earth’s surface and increases the temperature. An additional argument for the presence of high-energy radiation that penetrates deeply into the Earth’s atmosphere and even reaches the Earth’s surface is the high statistically significant correlation between the fluxes of such radiation recorded by GOES series satellites in a geostationary orbit (36,000 km above the Earth’s surface) and the human mortality from deadliest diseases.

Characteristics of plankton Hg bioaccumulations based on a global data set and the implications for aquatic systems with aggravating nutrient imbalance

The bioaccumulation of mercury(Hg)in aquatic ecosystem poses a potential health risk to human being and aquatic organism.Bioaccumulations by plankton represent a crucial process of Hg transfer from water to aquatic food chain.However,the current understanding of major factors affecting Hg accumulation by plankton is inadequate.In this study,a data set of 89 aquatic ecosystems worldwide,including inland water,nearshore water and open sea,was established.Key factors influencing plankton Hg bioaccumulation(i.e.,plankton species,cell sizes and biomasses)were discussed.The results indicated that total Hg(THg)and methylmercury(MeHg)concentrations in plankton in inland waters were significantly higher than those in nearshore waters and open seas.Bioaccumulation factors for the logarithm of THg and MeHg of phytoplankton were 2.4–6.0 and 2.6–6.7 L/kg,respectively,in all aquatic ecosystems.They could be further biomagnified by a factor of 2.1–15.1 and 5.3–28.2 from phytoplankton to zooplankton.Higher MeHg concentrations were observed with the increases of cell size for both phyto-and zooplankton.A contrasting trend was observed between the plankton biomasses and BAF_(MeHg),with a positive relationship for zooplankton and a negative relationship for phytoplankton.Plankton physiologic traits impose constraints on the rates of nutrients and contaminants obtaining process from water.Nowadays,many aquatic ecosystems are facing rapid shifts in nutrient compositions.We suggested that these potential influences on the growth and composition of plankton should be incorporated in future aquatic Hg modeling and ecological risk assessments.

Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

Quantitative credibility evaluation of Global Energy Interconnection data

The development of Global Energy Interconnection(GEI)is essential for supporting a wide range of basic data resources.The Global Energy Interconnection Development and Cooperation Organization has established a comprehensive data center covering six major systems.However,methods for accurately describing and scientifically evaluating the credibility of the massive amount of GEI data remain underdeveloped.To address this lack of such methods,a GEI data credibility quantitative evaluation model is proposed here.An evaluation indicator system is established to evaluate data credibility from multiple perspectives and ensure the comprehensiveness and impartiality of evaluation results.The Cloud Model abandons the hard division of comments to ensure objectivity and accuracy in evaluation results.To evaluate the suitability of the proposed method,a case analysis is conducted,wherein the proposed method demonstrates sufficient validity and feasibility.

Quality Control and Analysis of Global Gauge-Based Daily Precipitation Dataset from 1980 to 2009

A series of quality control(QC) procedures were performed on a gauge-based global daily precipitation dataset from the Global Telecommunication System(GTS) for the period 1980-2009.A new global daily precipitation(NGDP) dataset was constructed by applying those QC procedures to eliminate erroneous records.The NGDP dataset was evaluated using the NOAA Climate Prediction Center Merged Analysis of Precipitation(CMAP) and the Global Precipitation Climatology Project(GPCP) precipitation datasets.The results showed that the frequency distribution and spatial distribution pattern of NGDP had a nice match with those from the CMAP and GPCP datasets.The global mean correlation coefficients with the CMAP and GPCP data increased from 0.24 for original GTS precipitation data to about 0.70 for NGDP data.Correspondingly,the root mean square errors(RMSE) decreased from 12 mm per day to 1 mm per day.The interannual variabilities of NGDP monthly precipitation are consistent with the CMAP and GPCP datasets in Asia.Meanwhile,the seasonal variabilities for most land areas on the Earth of NGDP dataset are also consistent with the CMAP and GPCP precipitation products.

Research on big data applications in Global Energy Interconnection

Construction of Global Energy Interconnection(GEI) is regarded as an effective way to utilize clean energy and it has been a hot research topic in recent years. As one of the enabling technologies for GEI, big data is accompanied with the sharing, fusion and comprehensive application of energy related data all over the world. The paper analyzes the technology innovation direction of GEI and the advantages of big data technologies in supporting GEI development, and then gives some typical application scenarios to illustrate the application value of big data. Finally, the architecture for applying random matrix theory in GEI is presented.

Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

Analysis of the Global Swell Distributions Using ECMWF Re-Analyses Wind Wave Data

The existence of three well-defined tongue-shaped zones of swell dominance,termed as 'swell pools',in the Pacific,the Atlantic and the Indian Oceans,was reported by Chen et al.(2002)using satellite data.In this paper,the ECMWF Re-analyses wind wave data,including wind speed,significant wave height,averaged wave period and direction,are applied to verify the existence of these swell pools.The swell indices calculated from wave height,wave age and correlation coefficient are used to identify swell events.The wave age swell index can be more appropriately related to physical processes compared to the other two swell indices.Based on the ECMWF data the swell pools in the Pacific and the Atlantic Oceans are confirmed,but the expected swell pool in the Indian Ocean is not pronounced.The seasonal variations of global and hemispherical swell indices are investigated,and the argument that swells in the pools seemed to originate mostly from the winter hemisphere is supported by the seasonal variation of the averaged wave direction.The northward bending of the swell pools in the Pacific and the Atlantic Oceans in summer is not revealed by the ECMWF data.The swell pool in the Indian Ocean and the summer northward bending of the swell pools in the Pacific and the Atlan-tic Oceans need to be further verified by other datasets.

Overview of global monthly surface temperature data in the past century and preliminary integration

This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.

环境病原微生物研究数据库及数据挖掘方法

环境生物安全与社会稳定、人类健康及国防安全等多个关键领域密切相关。鉴于气候变化以及人类活动对生态系统不断加大的压力,未知病原体从环境中溢出的风险显著增加。当前的研究方法多集中于已知病原体的检测与分析,然而,组学技术及其相关生物信息学技术的快速发展与应用,为识别未知环境病原体及预警潜在的环境健康风险提供了新的机遇与解决方案。这些技术的发展不仅促进了对微生物与动物、人类以及环境之间相互作用关系的理解,更是实现“同一健康(One Health)”理念的重要组成部分。本文概述了全球变化对环境病原体逸出的影响及其引发的健康风险,从数据存储、数据分析到数据挖掘三个维度对生物信息学在环境病原体研究中的应用与进展进行了深入探讨,对当前领域内存在的问题进行了剖析,并对未来利用大数据进行环境病原体检测与风险评估的应用前景进行了展望。

进化计算在大规模高维特征选择中的应用综述

随着大数据时代的到来,数据的规模和特征维度呈现爆炸式增长,这给数据处理带来了前所未有的挑战。特征选择作为数据预处理的关键环节,在处理大规模高维数据时显得尤为重要。而进化计算方法因其出色的全局搜索能力和高效的优化性能,越来越多的研究者开始对其进行研究,其在大规模高维特征选择中得到了广泛的应用。本文首先介绍了大规模高维数据处理的重要性;然后简单介绍了部分经典和较新的进化计算方法,并详细介绍了其在大规模高维特征选择中的应用情况;最后对目前进化计算在大规模高维特征选择中存在的问题进行总结,并展望了其未来的发展方向。

弱全局信息下巨星座多路并发熵均衡路由策略

为保障地面系统对巨星座信息的高效获取,考虑实际卫星感知能力的约束,提出一种弱全局信息下巨星座多路并发熵均衡路由策略。首先,根据星座运行模式构建簇群,同时考虑簇的感知能力,建立弱全局信息下的“星-星/星-地”数据传输模型。其次,综合考虑网络结构、数据流量分布和信息多点频发等因素,对现有算法进行改进,引入熵作为评估节点负载分布均匀度的量化指标,引入惩罚因子对路径成本进行动态调整,并通过滚动窗口法规划巨星座多路并发情况下的对地传输路径。最后,以Starlink一期部署的卫星为例,进行数值仿真,验证所提方法的有效性。

Global and Hemispherical Interannual Variation of Total Column Ozone from TOMS and OMI Data

Daily Total Column Ozone (TCO) measurements compiled from Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instruments (OMI) were used to analyze the global and hemispherical TCO interannual variations. Two periods of TCO measurements were analyzed separately covering full years. For the 1978-1994 period, the TCO showed a global decade decrease rate of 13.45 DU (about -4.3%). For the Northern Hemisphere(NH) the decade decrease rate was of 12.96 DU (-4.0%), while in the Southern Hemisphere (SH) was of 13.57 DU (-4.5%). These decreases in ozone trends, using the totality of TOMS and OMI satellite measurements, are greater than those reported in literature. The 1998-2014 period global TCO decade decrease rate was of 1.56 DU, corresponding 0.94 DU and 0.138 DU for the NH and SH, respectively. The global TCO variations must show a double annual periodicity, the first one with maxima in March due to the Northern Hemisphere (NH) and the second one during September due to the Southern Hemisphere (SH). However, the maxima due to SH TCO interannual variations have gradually vanished. A disturbance in the SH TCO interannual variations has appeared since 1980;graphically the periodicity brakes down and transforms to a double peak from 1985 and on. This effect can be attributed to the hemispheric impact of the ozone hole at the South Pole. Between October 1, 2004 and December 14, 2005 TOMS and OMI have recorded this disturbance unequivocally. We conclude that the disturbance in SH TCO has an irreversible character.

区间函数型数据构权方法研究

针对由数据量过大而引起的函数型综合评价信息损失或是计算复杂度过大等问题,文中提出了一种基于区间函数型数据的综合评价方法,并针对区间函数型数据表支持的综合评价问题的特殊性,提出了一种新的确定权重系数的"全局"拉开档次法.相较于函数型综合评价,区间函数型综合评价在呈现连续的函数特征同时,增加了区间化的步骤,能够更好地挖掘数据信息,提升综合评价的效率.最后通过以义乌小商品景气指数为例,分别使用函数型数据形式和区间函数型数据形式下的"全局"拉开档次法对其进行赋权,进而进行评价研究.结果表明该文提出的区间函数型综合评价方法更具优势.

数字主权与全球数字治理

数字主权是国家主权在数字领域的自然延伸,同传统主权一样表现为国家在数字领域的对内最高管辖权和对外独立自主权。数字主权大体包括网络主权和数据主权两个方面。就网络主权而言,应当坚持网络空间的“和平”和“日常”属性,强调一国基于国家主权所享有的管辖权、管理权和规制权;就数据主权而言,应当探索平衡、适度的跨境数据流动监管路径和方法,在维护数据主权与发展数字经济之间求得最优解,有望推动全球数字治理正向发展,对其的异化和滥用则可能诱发监管过度扩张、加速西方国家对华“数字脱钩”。作为因应,中国在国内层面应当坚持数字主权的国家属性和“防御”属性,探索更加平衡、适度的监管方法和路径;在双边层面应当对“等效监管”要求作出更加灵活有效的应对,包括考虑主动进行“逆向”充分性认定;在多边层面应当探索确立以主权平等为基础的全球数字治理模式,并充分利用有关区域性机制。

数字化时代下法学实证研究的挑战与机遇

数字化时代法学实证研究的挑战方面,强调由大数据多样性和复杂性引发的信息失真问题,建议通过高级数据清洗技术和智能化处理策略进行应对;伦理与隐私问题备受关注,研究者需遵循明确的伦理准则,采用匿名化和脱敏技术以确保隐私权的保护;调整与创新方面,主张引入计算机科学和信息技术,与其他学科进行协作;能力建设方面,强调跨学科知识的培养和对新技术的灵活应用。在机遇方面,关注数据分析与预测性法学的发展、全球化视野下的合作机会以及新型法律服务模式的涌现。对实证研究实践提出了提升研究者素质与能力、规范研究与标准操作、研究伦理准则、风险评估、开放透明与评估机制等建议。

全球数据与隐私安全面临的突出挑战及对策探析

随着云计算、移动互联网、区块链、人工智能、量子计算等新一代信息技术的发展,人类进入数字文明时代,数据被认为是“第五大生产要素”。然而,数据与隐私问题已成为全球网络空间安全的“重灾区”,给政治、经济、社会等领域带来巨大风险,关系国家安全、社会稳定以及人权保障。为此,各国持续加强对本国数据与隐私合规性的监督管理力度,意在强化数据与隐私安全治理,同时维护本国数据战略地位。本文旨在深入分析全球数据与隐私安全面临的突出挑战,剖析其内在原因以及各国政府和企业应对举措,并提出适合我国国情的对策建议。