Should the Preference-based Index Set of Values Be Country- or Disease-specific? An Analysis Using Data From Women With Symptoms of Overactive Bladder

Background: Overactive bladder (OAB) and urinary incontinence (UI) are common conditions among women. However, no studies have evaluated the utility value of this population using different country-specific value sets. We aimed to 1) verify the difference between the preference-based index extracted from the Short Form six dimensions (SF-6Dv1) questionnaire in women with OAB using different country-specific value sets; 2) translate and cross-culturally adapt the King's Health Questionnaire Five Dimension (KHQ-5D) into Brazilian Portuguese; and 3) examine the association between utility index obtained by the SF-6Dv1 and KHQ-5D. Methods: This cross-sectional study included 387 women over 18 years of age with OAB symptoms, divided into groups with and without UI. All participants answered the sociodemographic questionnaire, KHQ, KHQ-5D, and SF-6Dv1. To the statistical analysis a two-way mixed ANOVA was applied to verify the interaction between the presence of UI and utility index obtained from different country-specific value sets. Post-hoc multiple comparisons were applied following the main analysis. Spearman’s test was applied to verify the correlation between the utility values of SF-6Dv1 and KHQ-5D. The significance level was set at 5%. Results: We evaluated 298 women classified according to the presence of UI (119 without UI vs. 179 with UI). The main analysis showed a statistically significant interaction between the presence of UI and the utility index obtained from the different countries (p = 0.005, Cohen’s d= 0.02). The post-hoc analyses showed that there was a statistically significant main effect of the utility index obtained from different countries (p <0.001, d = 0.63) and in the presence of UI (p = 0.012, d = 0.02). The correlations between the utility indices obtained from different countries using the SF-6Dv1 and KHQ-5D were significant, positive, and small. Conclusions: The differences between the indices obtained in different countries and groups with and without UI, assessed using the SF-6Dv1, are shown. The correlation between general and specifics preference-based measures was small; therefore, the SF-6Dv1 should be used with caution in cost-utility studies for this population. We recommend that in women with OAB, the value set for GPBM be obtained in countries where cost utility is applied.

Minimum-monitor-unit optimization via a stochastic coordinate descent method

Objective: Deliverable proton spots are subject to the minimum monitor-unit (MMU) constraint. The MMU optimization problem with relatively large MMU threshold remains mathematically challenging due to its strong nonconvexity. However, the MMU optimization is fundamental to proton radiotherapy (RT), including efficient IMPT, proton arc delivery (ARC), and FLASH-RT. This work aims to develop a new optimization algorithm that is effective in solving the MMU problem. Approach: Our new algorithm is primarily based on stochastic coordinate decent (SCD) method. It involves three major steps: first to decouple the determination of active sets for dose-volume-histogram (DVH) planning constraints from the MMU problem via iterative convex relaxation method; second to handle the nonconvexity of the MMU constraint via SCD to localize the index set of nonzero spots; third to solve convex subproblems projected to this convex set of nonzero spots via projected gradient descent method. Main results: Our new method SCD is validated and compared with alternating direction method of multipliers (ADMM) for IMPT and ARC. The results suggest SCD had better plan quality than ADMM, e.g., the improvement of conformal index (CI) from 0.51 to 0.71 during IMPT, and from 0.22 to 0.86 during ARC for the lung case. Moreover, SCD successfully handled the nonconvexity from large MMU threshold that ADMM failed to handle, in the sense that (1) the plan quality from ARC was worse than IMPT (e.g., CI was 0.51 with IMPT and 0.22 with ARC for the lung case), when ADMM was used; (2) in contrast, with SCD, ARC achieved better plan quality than IMPT (e.g., CI was 0.71 with IMPT and 0.86 with ARC for the lung case), which is compatible with more optimization degrees of freedom from ARC compared to IMPT. Significance: To the best of our knowledge, our new MMU optimization method via SCD can effectively handle the nonconvexity from large MMU threshold that none of the current methods can solve. Therefore, we have developed a unique MMU optimization algorithm via SCD that can be used for efficient IMPT, proton arc delivery (ARC), FLASH-RT, and other particle RT applications where large MMU threshold is desirable (e.g., for the delivery of high dose rates or/and a large number of spots).

Comparison of Robust Estimators’ Performance for Detecting Outliers in Multivariate Data

In multivariate data, outliers are difficult to detect especially when the dimension of the data increase. Mahalanobis distance (MD) has been one of the classical methods to detect outliers for multivariate data. However, the classical mean and covariance matrix in MD suffered from masking and swamping effects if the data contain outliers. Due to this problem, many studies used a robust estimator instead of the classical estimator of mean and covariance matrix. In this study, the performance of five robust estimators namely Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME), Index Set Equality (ISE), and Test on Covariance (TOC) are investigated and compared. FMCD has been widely used and is known as among the best robust estimator. However, there are certain conditions that FMCD still lacks. MVV, CME, ISE and TOC are innovative of FMCD. These four robust estimators improve the last step of the FMCD algorithm. Hence, the objective of this study is to observe the performance of these five estimator to detect outliers in multivariate data particularly TOC as TOC is the latest robust estimator. Simulation studies are conducted for two outlier scenarios with various conditions. There are three performance measures, which are pout, pmask and pswamp used to measure the performance of the robust estimators. It is found that the TOC gives better performance in pswamp for most conditions. TOC gives better results for pout and pmask for certain conditions.

Geospatial Analysis of Agriculture Land Loss in Qena City, Upper Egypt

Although urbanization presents opportunities for new urban developments, it may have serious problems on environment and land use/cover patterns. The present study aims to evaluate the performance of built‑up delineation index set (BDIS) for mapping agricultural land loss in Upper Egypt. Three Landsat images were obtained for the years 1986, 2000, and 2016 and utilized as inputs to calculate the BDIS variables. Then a supervised classification technique (i.e., support vector machine) was used to classify the images. The findings showed that urban areas have witnessed a dramatic expansion at a growing rate of 44.1% during the 30 years. As a result, the loss of the agricultural land was found to be approximately 64.83 ha, which represents -4%, during the same period because of the urban expansion and the illegal construction of settlements. These findings would support the local decision makers in urban and agriculture land management authorities to develop sustainable development plans that control the spatiotemporal urban expansion and agricultural land loss.

A Prediction Method for Postconstruction Settlement of Pile-Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

This paper presents a postconstruction settlement prediction method for pile-soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. In this method, the variation range of postconstruction settlement can be obtained from a simple calculation based on the basic data of actual engineering. Firstly, according to the characteristics of influencing factors in the construction of soft soil subgrade, the evaluation index set and two-level factor index sets were selected. The grading standards of the evaluation index and factor index were determined according to the allowable value of the standard and the numerical simulation results. Secondly, each factor index was standardized, and the normal distribution function in the form of exponential was used to construct the standard membership function for the first and second factor indexes. Finally, the comprehensive evaluation matrix of postconstruction settlement of composite subgrade was constructed based on the entropy weight method. The variation range of postconstruction settlement was predicted by the principle of maximum membership. The example analysis shows that the predicted results of the prediction method and the field measurement method are in good agreement, indicating that the proposed method can realize the postconstruction settlement prediction of composite subgrade, and the results are more accurate and more instructive.

The compliance of French purchasing behaviors with a healthy and sustainable diet: a 1-yr follow-up of regular customers in hypermarkets

In France, hypermarkets are the main shopping sites for food products. Therefore, the food-purchasing profiles of their regular customers may be a relevant indicator of the sustainability and health potentials of consumed diets. Knowing this information can be a step to address the issue of global health. The main objective of this study was to assess the sustainability and health potential of food-purchasing behaviors among regular adult customers, with or without children, of a leading French retailer. Secondarily, the cost of a sustainable food shopping cart was evaluated as regards the regular one, as calculated in this study. Purchasing receipts corresponding to 38,168 different food products were collected during one consecutive month for each four seasons in 2019 to assess compliance with a newly developed holistic indicator of food system sustainability, i.e., the 3V rule, recommending food consumption to be ‘Vegetal’/plant-based (≈15% animal calories/day), ‘Vrai’/real (max. 15% ultra-processed food calories/day, UPF) and ‘Varié’/varied. Participants were 708 regular buyers (aged ≥18 with different socio-economic profiles, with and without children) in 122 French hypermarkets. The plant rule was based on the animal and plant origin of food ingredients, including mixed products; the ‘real’ rule was evaluated with the Siga score according to the degree of processing to identify UPFs. The varied rule was defined based on a combination of food ‘categories × families’. The effect of children and season on the purchased animal and UPF calories and on the variety index was also evaluated. Multivariate and decision tree analyses were applied to compare consumers for their 3V rule profile similarities and differences, and to look for impacts of the presence or absence of children. Customers' purchases were far from the 3V rule, with a median of 41% animal and 61% UPF calories and a median variety index of 25% (compared to the consumer with the highest index set to 100%). There was no difference in purchased animal and UPF percentages neither according to seasons nor the presence of children. However, the presence of children was associated with a higher variety index (+33%, P < 0.05). Finally, the more the consumers purchased varied, the less they purchased UPFs. Compared to the average food basket, a 3V-based basket would cost 4.6% less. To make this basket accessible to everyone and to orientate consumer's purchasing behaviors toward more sustainable and healthier products, and hence food systems, hypermarkets should promote healthy eating and reassess their food offerings.

Identifying patients at high risk of inappropriate drug dosing in periods with renal dysfunction

Introduction: Dosing of renally cleared drugs in patients with kidney failure often deviates from clinical guidelines but little is known about what is predictive of receiving inappropriate doses. Methods and materials: We combined data from the Danish National Patient Register and in-hospital data on drug administrations and estimated glomerular filtration rates for admissions between 1 October 2009 and 1 June 2016, from a pool of about 2.9 million persons. We trained artificial neural network and linear logistic ridge regression models to predict the risk of five outcomes (>0, ≥1, ≥2, ≥3 and ≥5 inappropriate doses daily) with index set 24 hours after admission. We used time-series validation for evaluating discrimination, calibration, clinical utility and explanations. Results: Of 52,451 admissions included, 42,250 (81%) were used for model development. The median age was 77 years; 50% of admissions were of women. ≥5 drugs were used between admission start and index in 23,124 admissions (44%); the most common drug classes were analgesics, systemic antibacterials, diuretics, antithrombotics, and antacids. The neural network models had better discriminative power (all AUROCs between 0.77 and 0.81) and were better calibrated than their linear counterparts. The main prediction drivers were use of anti-inflammatory, antidiabetic and anti-Parkison's drugs as well as having a diagnosis of chronic kidney failure. Sex and age affected predictions but slightly. Conclusion: Our models can flag patients at high risk of receiving at least one inappropriate dose daily in a controlled in-silico setting. A prospective clinical study may confirm this holds in real-life settings and translates into benefits in hard endpoints.

Minimal Representations of a Face of a Convex Polyhedron and Some Applications

In this paper, we propose a method for determining all minimal representations of a face of a polyhedron defined by a system of linear inequalities. Main difficulties for determining prime and minimal representations of a face are that the deletion of one redundant constraint can change the redundancy of other constraints and the number of descriptor index pairs for the face can be huge. To reduce computational efforts in finding all minimal representations of a face, we prove and use properties that deleting strongly redundant constraints does not change the redundancy of other constraints and all minimal representations of a face can be found only in the set of all prime representations of the face corresponding to the maximal descriptor index set for it. The proposed method is based on a top-down search strategy, is easy to implement, and has many computational advantages. Based on minimal representations of a face, a reduction of degeneracy degrees of the face and ideas to improve some known methods for finding all maximal efficient faces in multiple objective linear programming are presented. Numerical examples are given to illustrate the method.

Pengaruh Makroekonomi dan Indeks Global terhadap Indeks Harga Saham Gabungan Selama Pandemi COVID-19 di Indonesia

The COVID-19 pandemic has affected many sectors, one of which is the capital market. The Coronavirus has claimed lives and can shake the order of life of a country. From an economic point of view, almost all countries experience a recession, a reduction in economic activity, increased unemployment, and a decline in people's purchasing power. This research examines the effect of the BI interest rate, exchange rate, inflation, SSEC index, KLSE index, SET index, and DJIA index on the Composite Stock Price Index. The research population is daily data during the COVID-19 pandemic in Indonesia from March 2020 to November 2020. The sampling technique uses purposive sampling. The number of samples is 111 data. The data analysis method uses multiple linear regression with IBM SPSS 25 software tools. The results show that the rupiah exchange rate against the US dollar has a negative effect and the Kuala Lumpur Stock Exchange has a positive effect on the Composite Stock Price Index, while the BI interest rate, inflation, SSEC index, the SET index and the DJIA index have no impact on the Composite Stock Price Index. However, all independent variables simultaneously affect the Composite Stock Price Index.