Women’s empowerment and food consumption:Evidence from female-headed households in Tanzania

Despite the growing recognition of women’s increasing role in the household and corresponding empowerment programs in sub-Saharan Africa,intensive research on the relationship between women’s influence and household food consumption is minimal.Using the most recent(2017-2018)national household survey data from Tanzania,this study examined the influence of women’s empowerment on household food consumption.First,we compared the monthly consumption of eight food categories between female-headed households(FHHs)and male-headed households(MHHs)using both descriptive statistics and the propensity score matching(PSM)method.Furthermore,we adopted the two-stage Linear Expenditure System and Almost Ideal Demand System model(LES-AIDS)to estimate income and price elasticities for the two household types.The results show that FHHs consume bread and cereals,fish,oils and fats,vegetables,and confectionery(sugar,jam,honey,chocolate,etc.)more than MHHs.Moreover,FHHs have a significantly higher income elasticity of demand for all food groups than MHHs.They are also more price elastic than MHHs in meat,fish,oils,fats,sugar,jam,honey,chocolate,etc.

Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market.As the history of the Bitcoin market is short and price volatility is high,studies have been conducted on the factors affecting changes in Bitcoin prices.Experiments have been conducted to predict Bitcoin prices using Twitter content.However,the amount of data was limited,and prices were predicted for only a short period(less than two years).In this study,data from Reddit and LexisNexis,covering a period of more than four years,were collected.These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts.An accuracy of 90.57%and an area under the receiver operating characteristic curve value(AUC)of 97.48%were obtained using the extreme gradient boosting(XGBoost).It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner(VADER)and 11 technical indicators utilizing moving average,relative strength index(RSI),stochastic oscillators in predicting Bitcoin price trends can produce significant results.Thus,the input features used in the paper can be applied on Bitcoin price prediction.Furthermore,this approach allows investors to make better decisions regarding Bitcoin-related investments.

数据定价研究的主题演变及趋势——基于CiteSpace的文献计量分析

由于数据定价对促进企业数字化转型、构建数据要素市场和促进经济可持续发展具有重要意义,因此借助CiteSpace文献计量分析软件,以中国知网、Web of Science数据库中相关研究文献为数据源,聚焦数据定价研究的主题演变、趋势、时代背景,对1993—2022年国内外数据定价权威文献进行计量分析。研究发现,1993—2022年国内外数据定价的研究整体呈波动上升趋势,近3年热度明显;研究模式国外多为合作共享研究而国内多为独立研究,均缺乏研究机构与数据供应商的合作研究;研究内容国内侧重于数据定价的成本效益、公平交易、隐私权属等,国外则侧重于数据定价的隐私安全与保护、人工智能算法、客户服务质量等,均与信息技术的发展密切相关;未来,数据定价研究将进一步立足“双碳”背景,结合环境、社会和公司治理(Environmental,Social and Governance,ESG)投资理念,布局和拓展新视角。

2024年1月23日乌什M_(S)7.1地震序列类型判定

利用新疆地震台网观测数据,对2024年1月23日乌什M_(S)7.1地震序列进行分析,结果显示乌什地震序列的余震活动丰富,频度衰减较快。通过历史地震序列类比、序列参数(b值、p值、h值)、等待时间、震级差和能量比等参数的跟踪分析,判断此次乌什M_(S)7.1地震序列为主震―余震型。从历史地震序列类型的早期定性判定到地震序列参数定量计算的跟踪判定乌什7.1级地震序列类型,对地震序列类型的跟踪判定有一定参考作用。

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

基于RST算法提取乌什M_(S)7.1地震地表温度异常

为探讨RST算法在新疆地区地震热红外异常提取中的适用性,以乌什M_(S)7.1地震为例,构建研究区MODIS地表温度数据集,并对数据集进行逐像元时间序列趋势去除后,利用RST算法提取乌什M_(S)7.1地震地表温度异常信息。结果表明:乌什M_(S)7.1地震地表温度异常的变化过程分为“出现—消失—再出现—扩大—达到最大—消失”6个阶段;乌什M_(S)7.1地震的地表温度异常区域大致沿构造带的走向分布;乌什M_(S)7.1地震发生在地表温度异常面积出现最大值后的第99天。利用MODIS数据结合RST算法提取地震热红外异常在新疆地区相关研究中具有一定的可行性。

农业节水灌溉REITs项目IRR测算研究

通过农业节水灌溉特许经营项目REITs案例研究发现,基础设施特许经营项目存在价值漏损时,可采用修正的B-S期权模型进行估值,可分配现金流用于分红的比例越高,投资收益越高,但项目的看涨期权价值越低。在项目高溢价转让的情况下,银行贷款比例越高、偿还时间越短,投资内部收益率(IRR)及投资收益可能越低;收益波动越大,看涨期权价值越大,但IRR可能会下降。采用B-S期权定价模型丰富了现有经营权类公募REITs资产估值方法,具有推广应用价值。对公募REITs产品在不同贷款比例、还款期限以及分红条件下确定合理投资收益,具有实践指导作用。

国内外融合教育研究进展的对比分析——基于WOS与CSSCI核心数据库文献共被引知识图谱解读

借助CiteSpace软件,采用文献共被引技术,对比分析了国内外“融合教育”研究的异同点。结果显示:发文量上,国外处于迅猛增长期,国内呈现稳步增长状态;研究领域方面,国外注重跨学科合作,国内聚焦于教育领域;研究热点方面,两者虽然都关注了“融合教育理论”“融合教育教师”,但国外更微观,注重模型建构与行动研究,国内更宏观,侧重国家政策分析;研究前沿方面,国外聚焦循证教育,国内侧重教师融合素养培养。基于以上分析,后续研究除需要构建面向全社会参与的融合教育运行机制外,还应开展基于循证实践的教育行动研究以及跨学科的应用与开发研究。

Colorectal cancer burden,trends and risk factors in China:A review and comparison with the United States

Objective:China and the United States(the U.S.)have the heaviest colorectal cancer(CRC)burden with considerable variations in temporal trends.This study aims to analyze the temporal patterns of CRC burden and its risk factors in China and the U.S.across the past three decades.Methods:Data were extracted from the Global Burden of Disease(GBD)Study in 2019,including cases,deaths,disability-adjusted life-years(DALYs),age-standardized rate(ASR),and summary exposure value(SEV)of CRC in China and the U.S.between 1990 and 2019.Annual average percentage changes(AAPCs)of CRC burden were calculated using the Joinpoint regression model.The mortality in CRC attributable to potential risk factors was characterized by countries,gender,and age groups.Results:In 2019,there were 607,900 and 227,241 CRC cases,and 261,777 and 84,026 CRC deaths in China and the U.S.,respectively.The age-standardized incidence rate(ASIR)was 30.55 per 100,000 in China and 41.86 per100,000 in the U.S.,and the age-standardized mortality rate(ASMR)was 13.86 per 100,000 in China and 14.77 per100,000 in the U.S.CRC incidence,mortality,and DALY rate in the U.S.showed downward trends in the past three decades(AAPC=-0.47,-1.06,and-0.88,respectively),while upward trends were observed in China(AAPC=3.11,1.05,and 0.91,respectively).Among the cause of CRC,the leading risk factor contributing to CRC death was low milk in China and smoking in the U.S.,respectively.Conclusions:From 1990 to 2019,the burden of CRC in China increased dramatically,particularly for males and middle-aged and elderly people.The management of the major risk factors associated with the high burden of CRC should be enhanced.

The relationship between international crude oil prices and China＇s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.

Climate Change: Trends and People’s Perception in Nepal

In the context of climate change, the frequency and intensity of extreme events such as floods and droughts will increase which could put tremendous challenges in water resources management in the coming days. While scientific knowledge on climate threats and changing climate patterns are essential, it is also important to consider the impacts in relation to how the threats are perceived and handled by local people. This paper intends to assess the trend and people’s perception on temperature and precipitation. Three focus groups’ discussion and a total number of 240 households were interviewed during field visit. The collected information was scaled from the least preferred-1 to the most preferred-5 based on their preferences. The trend of mean of annual average, maximum and minimum temperature indicates that the temperature has increased significantly and precipitation intensity and magnitude are also in increasing trend in the monsoon and postmonsoon seasons which may raise the extreme flood events. These facts were verified with the people’s perception. This finding could be useful for formulation of effective flood management policy and plan in this river basin as well as very applicable for other similar areas.

Spatial Quantitative Analysis of Garlic Price Data Based on ArcGIS Technology

In order to solve the hidden regional relationship among garlic prices,this paper carries out spatial quantitative analysis of garlic price data based on ArcGIS technology.The specific analysis process is to collect prices of garlic market from 2015 to 2017 in different regions of Shandong Province,using the Moran's Index to obtain monthly Moran indicators are positive,so as to analyze the overall positive relationship between garlic prices;then using the geostatistical analysis tool in ArcGIS to draw a spatial distribution Grid diagram,it was found that the price of garlic has a significant geographical agglomeration phenomenon and showed a multi-center distribution trend.The results showed that the agglomeration centers are Jining,Dongying,Qingdao,and Yantai.At the end of the article,according to the research results,constructive suggestions were made for the regulation of garlic price.Using Moran’s Index and geostatistical analysis tools to analyze the data of garlic price,which made up for the lack of position correlation in the traditional analysis methods and more intuitively and effectively reflected the trend of garlic price from low to high from west to east in Shandong Province and showed a pattern of circular distribution.

A Trend Analysis of Temperature and Rainfall to Predict Climate Change for Northwestern Region of Bangladesh

Bangladesh is one in all the foremost climate vulnerable countries of the world. In recent years, climate change studies over the country get plenty of attention by the researchers and policy makers. A substantial quantity of global climate change studies over the country use climate models to estimate future projections and uncertainties. Maximum temperature, precipitation and their potential future changes are evaluated in an ensemble of the 5th Phase Coupled Model Inter-comparison Project (CMIP5) within the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fifth Assessment Report (AR5) and the available historical data collected by the Bangladesh Meteorological Department (BMD) during the period 1981-2008 in the north-western region of Bangladesh and also the comparison between these two values. It has been found that average maximum temperature shows a positive trend of increase at a rate of 0.29°C and 5.3°C per century respectively, for BMD data and MPI-ESM-LR (CMIP5) model data. But the rainfall is decreasing at a rate of 8.8 mm and 40.1 mm per century respectively for BMD data and MPI-ESM-LR (CMIP5) model data. It is seen that July was the maximum monsoon rainfall month and January was the lowest rainfall month. The peak frequency is slightly smaller than 12 months, which indicates that the major events are occurring before ending a year compared to the previous year. According to MPI-ESM-LR (CMIP5) model data, future normal temperature on north-western region will be increased at a rate of 1.62°C during the period 2040-2100.

Assessment of China's International Environment and U.S. Strategic Trend

It is of great significance to have a clear understanding of the international environment that China faces. But this is an uneasy job. Since September 11, the world situation has become so complicated that it is hard to grasp its essence even with painstaking efforts. To judge such an already complicated international environment from China’s perspective adds more difficulties, for different people usually have different views due to the different angles from which they see the situation and different methods they adopt. This, understandably, leads to hot debates within the Chinese academic circles. To provide a stage for exchange of views, at the invitation of the CIR Expert Forum, some well-known scholars and experts on international relations from Beijing gathered to hold a symposium on November 5, 2002, with the theme of "How to assess the international environment that China faces". The speeches at the symposium will be published in two installments. Following are the first half.

Research on trend prediction of component stock in fuzzy time series based on deep forest

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%.

Trend of Network Services and NEC’s Approach

1 A Decade of Experience in Offering Network Services in Japan2 Why Internet Now?2.1 The Internet as the Standard.bearer of Multimedia2.2 The Internet up to Now2.3 PC Online Services vs.Internet2.4 Intranct and Electronic Commerce Services to Come2.5 Commercial Internet Services as

Mediterranean Sea level trends from long-period tide gauge time series

The main purpose of this study is to highlight, on the basis of statistical tests, the significant long-term changes of the Mediterranean Sea level, through the analysis of historical tide gauge records. In this framework, 14 tide gauge monthly series selected from the Permanent Service of the Mean Sea Level(PSMSL) database were used. The search for the presence or not of trends within these series, that have a temporal coverage from 59 to 142 years, was carried out using the Mann-Kendall test and the Sen’s slope estimator. The obtained results show that the Split Rt Marjana series are the only ones which does not exhibit a significant trend. The other 13 series show significant increasing trends. This result seems sufficient to suppose the presence, in the past century, of a new climatic phase on the scale of the Mediterranean basin, where the rising sea level is one of the consequences.

Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.