Daredevil·Gladiator·Cherry Red·Whirlwind·Flash·Grit·Spider Grass·MercuryWith less to lose,we take a risk,creating challenges and pushingourselves in ways we only fantasized about whi...Daredevil·Gladiator·Cherry Red·Whirlwind·Flash·Grit·Spider Grass·MercuryWith less to lose,we take a risk,creating challenges and pushingourselves in ways we only fantasized about while living in the safezone.Self-tested,defying limits,we feel invigorated.Each stepwe take towards our wildest dreams brings us one step closer tothe edge.This dynamic palette merges bold and muted tones.Itschameleon nature applies from active to formal wear.展开更多
Inquisitor Making sense of the static around us,digging deeper permeates every aspect of our lives.Scrutinizing instead of assuming,we are inspired to self-educate which infiltrates everything from our consumer decisi...Inquisitor Making sense of the static around us,digging deeper permeates every aspect of our lives.Scrutinizing instead of assuming,we are inspired to self-educate which infiltrates everything from our consumer decisions to global perspectives.Self-identification through brands or political movements loses steam as perspective is gained through more unbiased or raw outlets.展开更多
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 sys...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.展开更多
A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one...A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.展开更多
The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has ...The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.展开更多
In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col<...In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col</span><span style="font-family:Verdana;">lected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis </span><span style="font-family:Verdana;">software Office Excel 2013. The model was used to predict the rainfall in</span><span style="font-family:Verdana;"> spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution cha</span><span style="font-family:Verdana;">racteristics, annual distribution characteristics and the trend of historica</span><span style="font-family:Verdana;">l </span><span style="font-family:Verdana;">rainfall time series fluctuation, through the qualitative analysis of figures.</span><span style="font-family:Verdana;"> Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are </span></span><span style="font-family:Verdana;">basically </span><span style="font-family:Verdana;">consistent with the measured values.展开更多
Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convi...Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.展开更多
With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in...With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.展开更多
In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis ...In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis of the data during 1992～1998, and the approximate load in 2010 is forecast on the basis of the data during 1990～1998.展开更多
文摘Daredevil·Gladiator·Cherry Red·Whirlwind·Flash·Grit·Spider Grass·MercuryWith less to lose,we take a risk,creating challenges and pushingourselves in ways we only fantasized about while living in the safezone.Self-tested,defying limits,we feel invigorated.Each stepwe take towards our wildest dreams brings us one step closer tothe edge.This dynamic palette merges bold and muted tones.Itschameleon nature applies from active to formal wear.
文摘Inquisitor Making sense of the static around us,digging deeper permeates every aspect of our lives.Scrutinizing instead of assuming,we are inspired to self-educate which infiltrates everything from our consumer decisions to global perspectives.Self-identification through brands or political movements loses steam as perspective is gained through more unbiased or raw outlets.
文摘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.
基金partially supported by the University of Salerno (Italy) through the Civil and Environmental Engineering Ph.D. programme and FARB research funding
文摘A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.
文摘The latest release of "2009 China Luxury Forecast" shows that while the financial crisis is leading a general decline in demand for luxury brands in Europe,America and Japan,the global economic downturn has had limited impact on Chinese luxury consumption and that there is widespread confidence in the future among Chinese luxury consumers.
文摘In this paper, the monthly rainfall statistical data of Nanning City, Capital of </span><span style="font-family:Verdana;">Guangxi Zhuang Autonomous Region, China, from 2006 to 2018, were col</span><span style="font-family:Verdana;">lected. On the basis of qualitative analysis of the rainfall seasonal changing law, the non-linear seasonal rainfall forecast model on Nanning City with the method of Trend Comparison Ratio (TCR) was established by the statistical analysis </span><span style="font-family:Verdana;">software Office Excel 2013. The model was used to predict the rainfall in</span><span style="font-family:Verdana;"> spring, summer, autumn and winter in Nanning in 2019. The results were: 286.41 mm, 695.79 mm, 292.20 mm and 118.11</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">mm, respectively. It was also found that the predicted results were consistent with the seasonal distribution cha</span><span style="font-family:Verdana;">racteristics, annual distribution characteristics and the trend of historica</span><span style="font-family:Verdana;">l </span><span style="font-family:Verdana;">rainfall time series fluctuation, through the qualitative analysis of figures.</span><span style="font-family:Verdana;"> Compared with the actual measured rainfall data of Nanning City in 2019 in the China Statistical Yearbook (2020), the predicted values are </span></span><span style="font-family:Verdana;">basically </span><span style="font-family:Verdana;">consistent with the measured values.
文摘Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.
基金Fundamental Research Foundation for Universities of Heilongjiang Province,Grant/Award Number:LGYC2018JQ003。
文摘With the continuous development of machine learning and the increasing complexity of financial data analysis,it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry.To improve the effectiveness of stock trend prediction and solve the problems in time series data processing,this paper combines the fuzzy affiliation function with stock-related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index.Meanwhile,in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge,this paper combines the deep forest model to train the stock data separately.The experimental results show that(1)the accuracy of the extreme random forest and the accuracy of the multi-grain cascade forest are both higher than that of the gated recurrent unit(GRU)model when the un-fuzzy index-adjusted dataset is used as features for input,(2)the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index-adjusted dataset as features for input,(3)the accuracy of the fuzzy index-adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un-fuzzy index-adjusted dataset as features for inputting the extreme random forest and(4)the average accuracy of the fuzzy index-adjusted dataset as features for inputting multi-grain cascade forest increased by 5.67%.
文摘In this paper electric load is forecast for the classified power consumers of Shanghai urban area for the scheduled years in short term and in long term respectively. The monthly load in 1999 is forecast on the basis of the data during 1992～1998, and the approximate load in 2010 is forecast on the basis of the data during 1990～1998.
