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.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

Human Factors Analysis in Software Engineering

The general human factors analysis analyzes human functions, effects and influence in a system. But in a narrow sense, it analyzes human influence upon the reliability of a system, it includes traditional human reliability analysis, human error analysis, man-machine interface analysis, human character analysis, and others. A software development project in software engineering is successful or not to be completely determined by human factors. In this paper, we discuss the human factors intensions, declare the importance of human factors analysis for software engineering by listed some instances. At last, we probe preliminarily into the mentality that a practitioner in software engineering should possess. Key words human factors analysis - software engineering - software reliability CLC number TP 311.5 Foundation item: Supported by the National Natural Science Foundation of China (60173013)Biography: Xu Ren-zuo (1946-), male, Professor, research direction: software engineering, software reliability engineering, software safety and software testing.

ON THE RELATION BETWEEN THE EVOLUTION OF NATURAL ENVIRONMENT AND HUMAN FACTORS AND THE DEVELOPMENT OF URBAN SETTLEMENT——Take the Lanzhou Valley Basin as An Examples

Lanzhou Valley Basin is composed of two smaller diamond-shaped basins striking from NW to SE and including 6 river terraces. The lower terraces remaining even and smooth are main sites for city buildings. The main function of Lanzhou urban settlement is a ferry and transport centre. Lanzhou used to be an im-portant crossing site and post on the 'silk road',and then became a transport and trade centre in Northwest China. In the middle part of the 17th century, Lanzhou became the capital of Gansu,a new province. The development process of Lanzhou urban settlement indicates that urban settlement is a system consisting of natural environment and human society based on the former.

The cause of human fatigue and scenario analysisin the process of marine transportation

The framework of fatigue factors is constructed to reduce the number of maritime accidents caused by human fatigue.Based on the study of maritime human fatigue,by screening and classifying it,there are 15 fatigue factors,which can be classified into 4 types:sleep,rest and rhythm;work factors;personal conditions;environmental factors.These factors are regarded as the variables for constructing scenarios.The importance,uncertainty,influence and dependence of variables and variable pairs were assessed by using morphological analysis,quantitative scales and correlation matrices.Ship movement and workload are selected as the two key variables,which are regarded as the axes of generating scenarios.One of the scenarios is selected as the typical scenario to illustrate the relationship between the causes of fatigue.Then,the analysis framework is constructed according to the fatigue factors relationship,and several potential solutions are proposed,which include the development of foresighted and flexible work plans,and the application of wearable facilities to improve monitoring and assessment systems.The proposed framework lays a theoretical foundation for studying maritime human fatigue,and scenario analysis can provide an effective strategy for reducing crews'fatigue.

Patients with brain metastases from gastrointestinal tract cancer treated with whole brain radiation therapy:Prognostic factors and survival

AIM: To identify the prognostic factors with regard to survival for patients with brain metastasis from primary tumors of the gastrointestinal tract. METHODS: Nine hundred and sixteen patients with brain metastases, treated with whole brain radiation therapy (WBRT) between January 1985 and December 2000 at the Department of Radiation Oncology, University Hospital Freiburg, were analyzed retrospectively. RESULTS: Fifty-seven patients presented with a primary tumor of the gastrointestinal tract (esophagus: n=0, stomach: n=10, colorectal: n=47). Twenty-six patients had a solitary brain metastasis, 31 patients presented with multiple brain metastases. Surgical resection was performed in 25 patients. WBRT was applied with daily fractions of 2 Gray (Gy) or 3Gy to a total dose of 50Gy or 30Gy, respectively. The interval between diagnoses of the primary tumors and brain metastases was 22.6mo vs 8.0mo for patients with primary tumors of the colon/rectum vs other primary tumors, respectively (P<0.01, log-rank). Median overall survival for all patients with brain metastases (n=916) was 3.4mo and 3.2mo for patients with gastrointestinal neoplasms. Patients with gastrointestinal primary tumors presented significantly more often with a solitary brain metastasis than patients with other primary tumors (P<0.05, log-rank). In patients with gastrointestinal neoplasms (n=57), the median overall survival was 5.8 mo for patients with solitary brain metastasis vs 2.7mo for patients with multiple brain metastases (P<0.01, log-rank). The median overall survival for patients with a Karnofsky performance status (KPS) ≥70 was 5.5mo vs 2.1mo for patients with KPS <70 (P<0.01, log-rank). At multivariate analysis (Cox Model) the performance status and the number of brain metastases were identified as independent prognostic factors for overall survival. CONCLUSION: Brain metastases occur late in the course of gastrointestinal tumors. Pretherapeutic variables like KPS and the number of brain metastases have a profound influence on treatment outcome.

Correlation of human epidermal growth factor receptor protein expression and colorectal cancer

AIM:To investigate the correlation between human epidermal growth factor receptor(HER-2) protein expression and colorectal cancer(CRC) using a casecontrol study and meta-analysis.METHODS:Tumor tissue specimens from 162 CRC patients were selected for the case group.Fifty cases were randomly selected,and normal CRC tissue at least 10 cm away from the tumor margins of these cases was used to generate the control group.The expression of the HER-2 protein in the 162 CRC tissue samples and the 50 adjacent normal mucosa tissue samples was detected via immunohistochemistry.The experimental data were analyzed using SPSS 18.0software,and R software version 3.1.0 was utilized for further verification.RESULTS:The expression of HER-2 protein in the 162 CRC tissue samples was significantly higher than in the normal tissue specimens.The data showed that the expression of HER-2 in CRC was related to the Dukes' stage,the depth of invasion and lymph node metastasis.The HER-2-positive patients had lower 3-and 5-year OS rates than the HER-2-negative patients,but there was no significant difference.However,there was a statistically significant difference in the 3- and5-year disease-free survival(DFS) rates of HER-2-positive and HER-2-negative patients.The results of the meta-analysis showed that the expression of HER-2in CRC patients was statistically significantly increased over that of healthy people.The 3-year DFS rate in HER-2-positive patients was markedly lower than that in HER-2-negative patients.CONCLUSION:Down-regulation of HER-2 expression might be a dependable strategy for CRC therapy.

Alterative Expression and Sequence of Human Elongation Factor-1δ during Malignant Transformation of Human Bronchial Epithelial Cells Induced by Cadmium Chloride

Objective To study the alternative expression and sequence of human elongation factor-1δ （human EF-1δ p31） during malignant transformation of human bronchial epithelial cells induced by cadmium chloride （CdCl2） and its possible mechanism. Methods Total RNA was isolated at different stages of transformed human bronchial epithelial cells （16HBE） induced by CdCl2 at a concentration of 5.0 μM. Special primers and probe for human EF-1δ p31 were designed and expression of human EF-18 mRNA from different cell lines was detected with fluorescent quantitative PCR technique. EF-18 cDNA from different cell lines was purified and cloned into pMD 18-T vector followed by confirming and sequencing analysis. Results The expressions of human EF-1δ p31 at different stages of 16HBE cells transformed by CdCl2 was elevated （P〈0.01 or P〈0.05）. Compared with their corresponding non-transformed ceils, the overexpression level of EF-15 p31 was averagely increased 2.9 folds in Cd-pretransformed cells, 4.3 folds in Cd-transformed ceils and 7.2 folds in Cd-tumorigenic cells. No change was found in the sequence of overexpressed EF-1δ p31 at different stages of 16HBE cells transformed by CdCl2. Conclusion Overexpression of human EF-1δ p31 is positively correlated with malignant transformation of 16HBE cells induced by CdCl2, but is not correlated with DNA mutations.

Comparative Study on the Human Driving Force of Cultivated Land and Construction Land Use Change in Hubei Province,China

Change trend of cultivated land and construction land in Hubei Province is analyzed.The years 1998-2003 are the reduction period of cultivated land;the years 2004-2007 are the increase period of cultivated land;the years 1988-2002 are the slow growth period of construction land;and the years of 2003 and 2007 are the increase period of construction land.Based on related social economic statistical data in 1988-2007,human driving forces on cultivated land and construction land in Hubei Province are analyzed quantitatively and comparatively by using SPSS software and mathematical statistics method.Result shows that driving force factors have negative driving effect on cultivated land,and positive driving effect on construction land.Driving force factors have significant differences of driving effects on cultivated land and construction land.Three principal components affecting the changes of cultivated land and construction land are policy,population and economic development level,and gap between urban and rural living standards.The three principal components have different degrees of sensitivity on the two land use types and the effects direction are reverse.The other factor driving abilities are relatively weak,and these factors have relatively stronger driving force on construction land than that on cultivated land.It is put forward that Hubei Province should implement rational allocation of land resources and scientific and rational use in order to ensure land security and to realize the sustainable development of regional economy and society based on the intensive land use,the planning policy constraints,and the agricultural and industrial production.

Standardized Evaluation of Camera-based Driver State Monitoring Systems

Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems rely on the in-cabin camera(s) and other optical sensors. With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems. However, to the best of our knowledge,currently, there is no common standard available to objectively compare the performance of these technologies. Thus, it is imperative to develop one standardized process for evaluation purposes.In this paper, we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1) defining and selecting the important influential individual and environmental factors, 2) countering the effects of individual differences and randomness in driver behaviors, and 3) building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data. We have collected data on a large scale on a commercial driver state-sensing platform. For each subject, 30 to 40 minutes of head motion data was collected and included variables, such as lighting conditions, head/face features,and camera locations. The collected data was analyzed based on a proposed performance measure. The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product, which builds a common base for comparing the performance of different systems.

HER2 discordance between paired primary gastric cancer and metastasis:a meta-analysis

Background： A number of studies have examined human epidermal growth factor receptor 2 （HER2） status in primary gastric cancer （GC） and associated metastasis, some showed their great concordance in FIER2 expression, but others demonstrated notable discordance. There is still little consensus on HER2 discordance, therefore, a systematic review and meta-analysis was conducted to assess the status on HER2 discordance between primary GC and its paired metastasis. Methods： PubMed, EMBASE, ASCO and The Cochrane Library were searched for studies that explored the concordance between primary tumor and metastasis in patients with GC up to 10 March, 2014. Data of discordance of HER2 between primary GC and corresponding metastasis were extracted from the publications and random-effects models were used to estimate pooled discordance proportions. Results： Eighteen articles including 1,867 patients were included for the meta-analysis in accordance with the selection criteria. Pooled discordance proportions were 7% [95% confidence interval （CI）： 5-10%] for HER2 status. Pooled proportions of tumors shifting from positive to negative and from negative to positive were 17% （95% CI： 7-29%） and 4% （95% CI： 2-6%） respectively. No publication bias was found in the meta-analysis. Conclusions： The discordance of HER2 status is not rare in primary and metastatic GC through our metaanalysis. Prospective studies are needed to testify the clinical significance of the discordance of HER2 status.

Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

Study on strength reduction factors consid-ering the effect of classification of design earthquake

The strength reduction factors are not only the key factors in determining seismic action for force-based seismic design, but also the key parameters to derive the inelastic response spectra for performance-based seismic design. In this paper, with a high quality ground motion database that includes a reasonable-sized set of records from China, a statistical study on the strength reduction factors is conducted and a new expression of strength reduction factors involving classification of design earthquake, which is an important concept to determine design spectra in Chinese seismic design code, is proposed. The expression of strength reduction factors can reflect the ground motion characteristics of China to a certain extent and is particularly suitable for Chinese seismic design. Then, the influence effects of site condition, classification of design earthquake, period of vibration, ductility level, earthquake magnitude and distance to fault on strength reduction factors are investigated. It is concluded that the effect of site condition on the strength reduction factors cannot be neglected, especially for the short-period structures of higher ductility. The classification of design earthquake also has an important effect on strength reduction factors and it may be unsuitable to use the existing expressions of strength reduction factors to the design spectra of current Chinese seismic code. The earthquake magnitude has no practical effect on strength reduction factors and if the near-fault records with forward directivity effect are not taken into consideration, the effect of distance to fault on strength reduction factors can also be neglected.

Epidemiological characterization and geographic distribution of human immunodeficiency virus/acquired immunodeficiency syndrome infection in North African countries

BACKGROUND Human immunodeficiency virus(HIV)infection is a major global public health concern.North African countries carry a disproportionate burden of HIV representing one of the highest rates in Africa.AIM To characterize the epidemiological and spatial trends of HIV infection in this region.METHODS A systematic review was carried out on all the published data regarding HIV/acquired immunodeficiency syndrome in North African countries over ten years(2008-2017)following the PRISMA guidelines.We performed a comprehensive literature search using Medline PubMed,Embase,regional and international databases,and country-level reports with no language restriction.The quality,quantity,and geographic coverage of the data were assessed at both the national and regional levels.We used random-effects methods,spatial variables,and stratified results by demographic factors.Only original data on the prevalence of HIV infection were included and independently evaluated by professional epidemiologists.RESULTS A total of 721 records were identified but only 41 that met the criteria were included in the meta-analysis.There was considerable variability in the prevalence estimates of HIV within the countries of the region.The overall prevalence of HIV ranged from 0.9%[95%confidence interval(CI)0.8-1.27]to 3.8%(95%CI 1.17-6.53).The highest prevalence was associated with vulnerable groups and particularly drug abusers and sexually promiscuous individuals.The dense HIV clustering noted varied from one country to another.At least 13 HIV subtypes and recombinant forms were prevalent in the region.Subtype B was the most common variant,followed by CRF02_AG.CONCLUSION This comprehensive review indicates that HIV infection in North African countries is an increasing threat.Effective national and regional strategies are needed to improve monitoring and control of HIV transmission,with particular emphasis on geographic variability and HIV clustering.

HFACS-MI改进模型在危险化学品仓储事故人因分析中的应用

为了研究危险化学品仓储事故的关键人因,通过人因分析与分类系统-煤矿(Human Factors Analysis and Classification System-Mine,HFACS-MI)改进模型从员工的不安全行为、影响不安全行为的前提条件、监督行为、组织影响和企业外部因素5个层次系统地分析65起危险化学品仓储事故,并运用卡方检验和让步比分析了产生不安全行为的原因和各层次间的内在关系。结果表明,外部管理、组织过程、监督不充分、个人因素、技能差错和违规是导致危险化学品仓储事故发生的重要因素。HFACS-MI改进模型上下层级的人为因素间存在显著的因果关系,且主要的危险化学品仓储事故致因路径共有5条,其中“外部管理因素—组织过程漏洞—监督不充分—个人因素—违规”是最关键的致因路径。改进的HFACS-MI模型可以有效分析危险化学品仓储事故人因因素。

中国人文社科领域研究方法应用的实证研究——基于大规模博士学位论文的调查

[目的/意义]研究方法的发展能在一定程度上反映学科的发展。目前鲜有针对中国整体人文社科领域研究方法使用的调研。对该领域不同学科研究方法的使用情况进行比较,可以揭示不同学科研究方法应用的特点,并为相关学者进行课题研究提供研究方法的使用建议。[方法/过程]与一般期刊论文相比,中国人文社科领域的博士学位论文中的研究方法通常会在特定的小节被明确阐述,便于提取分析。文章基于人文社科领域21个一级学科共63741篇博士学位论文,构建研究方法分类框架,抽取论文使用的研究方法,并从研究方法的使用频次、时间演化以及学科相关度角度进行深入分析。[结果/结论]中国人文社科领域研究方法命名存在不规范的问题,但方法意识正逐渐增强;比较分析法、历史分析法、文献研究法等经典的数据分析方法仍占主导地位;多数热门研究方法在人文社科领域不同学科之中存在一定的普适性。

煤矿安全事故的综合致因模型

为了系统研究导致煤矿安全事故的人机环因素,结合煤矿实际,将设备因素、环境因素与人因分析和分类系统(Human Factors Analysis and Classification System,HFACS)相结合,构建了煤矿安全事故的“弓弦箭”致因模型。其中,弓弦由设备因素11项、人因因素17项和环境因素11项的致因因素构成,箭表示发生不同人员伤亡数量、不同经济损失的安全事故,利用该模型可以综合描述煤矿安全事故发生的原因以及事故的后果。以“弓弦箭”致因模型为基础,借助蚁群聚类及Ucinet-netdraw方法,分别对100起典型煤矿安全事故进行分析研究。结果显示:(1)在统计的100起事故中,致因因素总频数为803,其中人因因素最高为644,占比80.2%,设备因素次之为92,占比11.5%,环境因素最少为67,占比8.3%;(2)以聚类数为4,最大迭代次数为1000,蚂蚁个数为100,阈值为0.9,蒸发率为0.1进行蚁群聚类分析,得到4类煤矿安全事故致因因素,表明煤矿安全事故是由设备因素、环境因素和人因因素交织作用产生,其中人因因素是最重要的致因因素,且操作者的不安全行为技能差错、决策差错、知觉差错和违反规章制度表现出一定的独立性;(3)Ucinet-netdraw分析显示煤矿安全事故的关键致因路径为:“管理过程漏洞”→“监督不充分”→“个人准备状态”→“违反规章制度”。

基于HFACS模型的军事演训活动风险评估指标体系构建

基于人因分析与分类系统(HFACS)分析军事演训活动的全面风险,考虑联合军事演训和新域新质作战力量的特点,并突出演训活动的战斗力标准。在改进HFACS模型分析框架的基础上,逐层次细化拆解,建立初步的风险评估指标库,并结合专家知识运用风险矩阵对指标库进行优化。构建人为因素视角下涵盖战斗力的风险、组织管理的风险、不安全行为的前提条件的风险和不安全行为的风险等4个方面以及包含战斗精神、新型主战武器、智能装备等在内的15项极高风险指标、20项高风险指标和8项中风险指标的风险评估指标体系。