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.

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.

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。

全球开放科学云的服务生态解构与启示

[目的/意义]全球开放科学云是近年来为扩大资源获取范围,应对全球挑战而提出的新概念。对这一概念进行深入分析,有助于为我国开放科学基础设施发展提供参考。[方法/过程]在网络调研和文献调研的基础上,对全球开放科学云的发展历程进行分析,并以全球开放科学为目标,结合GOSC测试平台的原型,探索科研用户参与开放科学的模式,构建全球开放科学云服务生态系统。[结果/结论]数据服务生态系统是以服务主体的三元互动模式为基础结构,以GOSC平台为服务媒介,引导数据交互活动中各利益相关者在微观、中观和宏观层面相互协作,推动数据价值共创并提升服务质量。基于服务生态系统中保障数据价值共创实现三个层次,并结合我国的实际,提出价值创造模式、数据交互和基础环境三个方面的参考意见,以优化国内科研生态系统和技术基础设施,提高我国科研能力。

基于数据主权视角的全球治理机制建构——挑战、导向与路径

随着人类社会逐渐步入深度信息化的大数据时代,海量数据在全球范围的流通和使用催生出了全球数据治理问题。各国在处理数据问题的实践中逐步形成了数据主权观念,但目前学界尚未形成统一的数据主权定义。近年来,在信息技术领域占据领先地位的美国、欧盟、中国等纷纷在数据主权保护领域开展了探索。而全球范围内数据治理机制的缺失给全球数据治理带来了风险和挑战。作为公共管理理论在全球化背景下的新领域,如何构建数据主权视角下的全球治理机制这一系统性工程成为全人类共同面临的问题。从合法性、有效性和适应性角度探讨解决全球范围内的数据治理机制的构建问题符合数据主权视角下的全球治理机制构建要求。在这些原则基础上,本文认为世界各国和其他治理主体应该从以下方面着手:推动大国协调治理机制建设,引入多元合作机制,探索数据治理领域的“南北合作”与“南南合作”,探索“以数据治理数据”的路径等。

一种解决数据异构问题的联邦学习方法

联邦学习是一种不通过中心化的数据训练就能获得机器学习模型的系统,源数据不出本地,降低了隐私泄露的风险,同时本地也获得优化训练模型。但是由于各节点之间的身份、行为、环境等不同,导致不平衡的数据分布可能引起模型在不同设备上的表现出现较大偏差,从而形成数据异构问题。针对上述问题,提出了基于节点优化的数据共享模型参数聚类算法,将聚类和数据共享同时应用到联邦学习系统中,该方法既能够有效地减少数据异构对联邦学习的影响,也加快了本地模型收敛的速度。同时,设计了一种评估全局共享模型收敛程度的方法,用于判断节点聚类的时机。最后,采用数据集EMNIST、CIFAR-10进行了实验和性能分析,验证了共享比例大小对各个节点收敛速度、准确率的影响,并进一步分析了当聚类与数据共享同时应用到联邦学习前后各个节点的准确率。实验结果表明,当引入数据共享后各节点的收敛速度以及准确率都有所提升,而当聚类与数据共享同时引入到联邦学习训练后,与FedAvg算法对比,其准确度提高10%~15%,表明了该方法针对联邦学习数据异构问题上有着良好的效果。

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。

基于Global Mapper的测绘图形数据转换

Global Mapper支持多种格式的光栅、高程、矢量数据的读取与输出,并内置多种坐标投影方式。尝试挖掘其支持多种数据格式、多种投影模式的功能,快速实现测绘图形数据的转换。通过设置当前工作空间的坐标投影模式,然后打开任何其支持的、含有坐标投影信息的数据,所有数据均自动转换成当前的投影模式,最后将文件输出(另存为)为所需的格式,即可快速完成数据转换。转换的内容主要包括不同格式的DTM(Digital Terrain Model,数字地面模型)数据转换、相同格式但不同坐标投影之间的相互转换,以及卫星影像配准等。