EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method.

Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fß was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020-2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.

Prostaglandin F2 receptor inhibitor overexpression predicts advanced who grades and adverse prognosis in human glioma tissue.

Prostaglandin F2 receptor inhibitor (PTGFRN) promotes neoplastic cell migration and metastasis in some human cancers. However, the role of PTGFRN in human gliomas is still undetermined. First of all, PTGFRN messenger ribonucleic acid (mRNA) overexpression correlated with some poor prognostic factors of glioma after analyzing The Cancer Genome Atlas and Chinese Glioma Genome Atlas database. In order to detect the effect of PTGFRN expression on tumor characteristics of gliomas, U87MG, LN229, and glioblastoma 8401 glioma cell lines were cultured and prepared for western blot analysis and real-time polymerase chain reaction, respectively. The results revealed the overexpression of PTGFRN in all glioma cell lines as compared to normal brain cells. In addition, PTGFRN immunohistochemical (IHC) staining was performed on two sets of glioma tissue microarrays. Consistent with the results of in vitro studies, cytoplasmic PTGFRN immunostaining scores positively correlated with tumor grades and poor prognosis of gliomas. Therefore, PTGFRN IHC staining may be useful for the evaluation of tumor grades and overall survival time to facilitate the tailoring of appropriate treatment strategy. PTGFRN may serve as a potential pharmacologic target for the suppression of gliomagenesis.

Developing A Sustainable Urban-Environmental Quality Evaluation System in China Based on A Hybrid Model.

In China, with the acceleration of urbanization, people pay more attention to the quality of urban environment. Air pollution, vegetation destruction, water waste and pollution, and waste sorting have restricted the sustainable development of urban environment. It is important to evaluate the impact of these environmental concerns as a prerequisite to implement an effective urban environmental sustainability policy. The aim of this paper is to establish a system for evaluating sustainable urban environmental quality in China. We extracted six dimensions and 29 criteria for assessing urban sustainable environment. Then, a fuzzy technique and the best worst method were applied to obtain the weights for the dimensions and criteria. Next, grey possibility values were applied to evaluate the sustainable environmental quality of five cities: Beijing, Shanghai, Shenzhen, Guangzhou, and Hangzhou in China. A sensitivity analysis was performed to identify how the ranking of these five cities changed when varying the weights of each criterion. The results show that pollution control, the natural environment, and water management are the three most important dimensions for urban environmental quality evaluation. We suggest that controlling pollutant emissions, strengthening food waste management, improving clean production processes, and utilizing heat energy are the effective measures to improve the urban environment and achieve sustainable urban environmental development.

Antimuscarinic Use in Females With Overactive Bladder Syndrome Increases the Risk of Depressive Disorder: A 3-Year Follow-up Study.

To date, the relationship between antimuscarinics for overactive bladder (OAB) syndrome and depressive disorder still remains unclear. Therefore, this retrospective cohort study examined the association between antimuscarinic use and the subsequent risk of depressive disorder using a population-based data set. This study used data from the Taiwan Longitudinal Health Insurance Database 2005. We selected 1952 OAB women who received antimuscarinics as the study cohort and 9760 OAB women who did not receive antimuscarinics as the comparison cohort. Each subject was tracked for 3 years from her index date to determine all those who were subsequently diagnosed with depressive disorder. Results indicated that the adjusted hazard ratio (HR) for depressive disorder in OAB women who received antimuscarinics was 1.38 (95% confidence interval [CI], 1.15-1.64) compared with those OAB women who did not receive antimuscarinics. In addition, the adjusted HRs for subsequent depressive disorder for OAB women aged 18-39, 40-59, and ≥60 years who received antimuscarinics were 1.83 (95%CI, 1.27-2.64), 1.36 (95%CI, 1.03-1.81), and 1.16 (95%CI, 0.86-1.56), respectively, compared with those OAB women who did not receive antimuscarinics. We concluded that women with OAB who received antimuscarinics had a significantly higher risk of subsequent depressive disorder compared with those OAB women who did not receive antimuscarinics. Accordingly, clinicians should be alert to the relationship between antimuscarinics usage and depressive disorder in OAB women and provide appropriate instructions for these patients.

Modeling the prediction of business intelligence system effectiveness.

Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

Physician characteristics and prescription drug use during pregnancy: a population-based study.

PURPOSE: We aimed to explore the relationship between physician characteristics and their prescribing behavior regarding category D and X drugs for pregnant women by using a population-based data set in Taiwan. METHODS: The sampled population for the study included 14,430 women. These women received a total of 198,420 prescriptions during pregnancy. We performed multivariate logistic regression analysis by using generalized estimated equations to assess the odds ratio (OR) of the prescription for categories D and X drugs among doctors after adjusting for maternal age and chronic disease. RESULTS: Of the total 198,420 prescriptions, 4.2% were prescribed category D and X drugs. The covariate-adjusted odds for physicians aged between 40 and 49 years and 50 and 59 years for prescribing category D and X drugs to pregnant women were 1.22 (95% confidence interval [95% CI], 1.15-1.31) and 1.51 (95% CI, 1.40-1.64) times that of physicians aged between 30 and 39 years, respectively. Male physicians were less likely to prescribe category D and X drugs to pregnant women than female physicians (OR, 0.69; 95% CI, 0.63-0.75). In addition, physicians specializing in "other" specialties were more likely (OR, 1.46; 95% CI, 1.41-1.54) to prescribe category D and X drugs compared with those specializing in obstetrics/gynecology, whereas physicians practicing in central Taiwan were less likely (OR, 0.85; 95% CI, 0.80-0.89) than their counterparts in other regions of Taiwan to prescribe category D and X drugs. CONCLUSIONS: We conclude that physician characteristics, including sex, age, specialty, and practice location, were associated with the prescription of category D and X drugs for pregnant women.

Feature-based recommendations for one-to-one marketing.

Most recommendation systems face challenges from products that change with time, such as popular or seasonal products, since traditional market basket analysis or collaborative filtering analysis are unable to recommend new products to customers due to the fact that the products are not yet purchased by customers. Although the recommendation systems can find customer groups that have similar interests as target customers, brand new products often lack ratings and comments. Similarly, products that are less often purchased, such as furniture and home appliances, have fewer records of ratings; therefore, the chances of being recommended are often lower. This research attempts to analyze customers' purchasing behaviors based on product features from transaction records and product feature databases. Customers' preferences toward particular features of products are analyzed and then rules of customer interest profiles are thus drawn in order to recommend customers products that have potential attraction with customers. The advantage of this research is its ability of recommending to customers brand new products or rarely purchased products as long as they fit customer interest profiles; a deduction which traditional market basket analysis and collaborative filtering methods are unable to do. This research uses a two-stage clustering technique to find customers that have similar interests as target customers and recommend products to fit customers' potential requirements. Customers' interest profiles can explain recommendation results and the interests on particular features of products can be referenced for product development, while a one-to-one marketing strategy can improve profitability for companies.