Peanut consumption and reduced risk of colorectal cancer in women:A prospective study in Taiwan

瞄准：检验花生消费是否与肤色的减少的风险被联系在有 10 年的后续的一个未来的队的表面的癌症。方法：在 1990-1992，在 7 个镇区的居民(30 ～ 65 年的 12026 个男人和 11917 个女人) ，台湾，被会见并且招募并且每年列在后面起来进一个屏蔽癌症的队。在这个队的 Colorectal 癌症诉讼从癌症登记和死亡证被识别。在 2001 底的这疾病的发生率被性为主要学习变量和 covariates 计算。饮食的吸入借助于每周的食物频率措施被估计，包括包括甘薯，豆子产品，花生产品， pickled 食品， nitrated 或吸的食品的经常消费的食物组和民间盘子。结果：在学习经期期间， 107 新肤色表面的癌症盒子(68 个男人和 39 个女人) 被证实。多，变量考克斯的比例的危险模型证明花生消费的相对风险(RR ) 是 0.73 [95% 信心间隔(CI )= 0.44-1.21 ] 为男人并且 0.42 (95% CI = 0.21-0.84 ) 为女人。然而， pickled 食品的经常的吸入为女人是有害的(RR = 2.15， 95% CI = 0.99-4.65 ) 。表面的癌症也是的颜色的风险在香烟吸烟者之中提高了然而并非重要(P【0.05 ) 。结论：这研究建议花生和它的产品的经常的吸入可以减少表面的癌症在女人冒的风险的颜色，表明花生吸入的增殖反的效果。

Telematics as a Transformative Agent for the Zimbabwean Auto Insurance Ecosystem

Zimbabwe has witnessed the evolution of Information Communication Technology (ICT). The vehicle population soared to above 1.2 million hence rendering the Transport and Insurance domains complex. Therefore, there is a need to look at ways that can augment conventional Vehicular Management Information Systems (VMIS) in transforming business processes through Telematics. This paper aims to contextualise the role that telematics can play in transforming the Insurance Ecosystem in Zimbabwe. The main objective was to investigate the integration of Usage-Based Insurance (UBI) with vehicle tracking solutions provided by technology companies like Econet Wireless in Zimbabwe, aiming to align customer billing with individual risk profiles and enhance the synergy between technology and insurance service providers in the motor insurance ecosystem. A triangulation through structured interviews, questionnaires, and literature review, supported by Information Systems Analysis and Design techniques was conducted. The study adopted a case study approach, qualitatively analyzing the complexities of the Telematics insurance ecosystem in Zimbabwe, informed by the TOGAF framework. A case-study approach was applied to derive themes whilst applying within and cross-case analysis. Data was collected using questionnaires, and interviews. The findings of the research clearly show the importance of Telematics in modern-day insurance and the positive relationship between technology and insurance business performance. The study, therefore revealed how UBI can incentivize positive driver behavior, potentially reducing insurance premiums for safe drivers and lowering the incidence of claims against insurance companies. Future work can be done on studying the role of Telematics in combating highway crime and corruption.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Profile Evolution and Energy Dissipation for Internal SolitonTransmitting over Different Submarine Ridges

Fundamental experiments were carried out in a wave flume on internal solitary wave （ISW） of depression-type propagating over a submerged ridge. The seabed ridge included either triangular or semicircular shape - regarded as topographic obstacles. Influenced by the submarine ridge, the transmitted waves were found to always consist of a leading pulse （a solitary wave） followed by a dispersive wave train. The wave profile propagating over a triangular ridge was similar to that caused by a semicircular obstacle. Apparently, the smooth face of a semicircular ridge produced time lag of wave propagation. From experimental results available, the reduction in wave energy induced by a semicircular ridge was larger than that by a triangular one. The events of wave distortion, strong breaking, internal bolus, and stratification mixing happened in case that the crest of an ISW was great enough to interact with the topographic obstacle. The reduction in wave energy was induced by strong breaking, and it depended on the ridge height rather than the geometric shape of the ridge.

Accuracy Evaluation of A Diagnostic Test by Detecting Outliers and Influential Observations

Logit regression analysis is widely applied in scientific studies and laboratory experiments, where skewed observations on a data set are often encountered. A number of problems with this method, for example, oudiers and influential observations, can cause overdispersion when a model is fitted. In this study a systematic statistical approach, including the plotting of several indices is used to diagnose the lack-of-fit of a logistic regression model. The outliers and influential observations on data from laboratory experiments are then detected. Specifically we take account of the interaction of an internal sohtary wave （ISW） with an obstacle, i.e., an underwater ridge, and also analyze the effects of the ridge height, the lower layer water depth, and the potential energy on the amplitude-based transmission rate of the ISW. As concluded, the goodness-of-fit of the revised logit regression model is better than that of the model without this approach.

Research on the Supervision and Regulation Mode of Production Safety in China, Germany and Canada

China is a developing country with rapid economic development and has made many remarkable achievements. However, the foundation of production safety is relatively weak, and the situation of production safety is still severe behind the rapid economic growth, and there is still a certain gap compared with Germany, Canada and other developed countries. Therefore, this article expounds the theory of safety production supervision mode, and then taking the supervision mode of production safety of Chinese government as the basic research object, from the development course, organization setup and management configuration and so on combing the current (until 2017) safety production supervision mode in Chinese government, and puts forward the status quo in the face of regulatory problems. In addition ,based on the research and analysis of the safety production supervision mode of the German and Canadian governments, the advanced experience and methods of the safety production supervision mode of the two governments are summarized, and through comparison, some suggestions on the supervision mode of production safety suitable for China’s national conditions are put forward, which provide theoretical support for the Chinese government or enterprise managers to do a good job in the management of production safety.

Computation Analysis of Brand Experience Dimensions: Indian Online Food Delivery Platforms

Online Food Delivery Platforms(OFDPs)has witnessed phenomenal growth in the past few years,especially this year due to the COVID-19 pandemic.This Pandemic has forced many governments across the world to give momentum to OFD services and make their presence among the customers.The Presence of several multinational and national companies in this sector has enhanced the competition and companies are trying to adapt various marketing strategies and exploring the brand experience(BEX)dimension that helps in enhancing the brand equity(BE)of OFDPs.BEXs are critical for building brand loyalty(BL)and making companies profitable.Customers can experience different kinds of brand experiences through feeling,emotions,affection,behavior,and intellect.The present research work is taken up to analyze the factors affecting BEX and its impact on BL and BE of the OFDPs and analyze the mediating role of BL in the relationship between BEX and BE of the OFDPs in the Indian context.A survey of 457 Indian customers was carried out.A questionnaire was used for data collection and a mediation study was used to test hypothesized relationships.Our computational analysis reveals that BEX influences the BL and BE of OFDPs.The study further indicates that BL mediates the relationship between BEX and BE of OFDPs.The effective marketing and relationship management practices will help company to enhance BEX that will enable in enhancing BL and raising BE of their product.It therefore provides a more thorough analysis of BEX constructs and their consequences than previous research.Some of the managerial implication,limitations,and scope of future research are also presented in the study.

Wavelet Based Detection of Outliers in Volatility Time Series Models

We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.

A Method for the Detection of Decrease in Power in PV Systems Using Satellite Data

In this paper, a method to detect a decrease in the output power of photovoltaic systems is proposed. This method is based on using satellite irradiance data. In addition, fault detection is carried out with only one day’s data in this method. Thus, the time elapses since the decrease in output is shorter than with the other methods. In order to mitigate the error in satellite data and improve the accuracy of fault detection, data extraction is carried out, which consists of two steps. In the first step, effective data are extracted by setting a lower irradiance limit. In the second step, “Calculation day” is determined depending on the number of effective data in one day. Fault detection, which is only conducted on the Calculation day, is conducted by comparing the expected power and the measured power. The parameters used in this study were optimized by testing 45 systems that appear normal. Subsequently, 340 systems were analyzed with the proposed method, using optimized parameters. The results showed the effectiveness of our method from the viewpoints of both accuracy and time required. In addition, three data extraction methods were considered to distinguish between the permanent decrease caused by failure, and the temporary decrease caused by partial shade. Fuzzy cluster analysis showed the best result among the three methods used.

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility pattern.The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations.The proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)model.The results show the model's ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast accuracy.The results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH model.The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).