Explore the vast range of titles available.

PurposeA variety of diagnostic methods are available to validate the performance of population pharmacokinetic models. Internal validation, which applies these methods to the model building dataset and to additional data generated through Monte Carlo simulations, is often sufficient, but external validation, which requires a new dataset, is considered a more rigorous approach, especially if the model is to be used for predictive purposes. Our first objective was to validate a previously published population pharmacokinetic model of darunavir, an HIV protease inhibitor boosted with ritonavir or cobicistat. Our second objective was to use this model to derive optimal sampling strategies that maximize the amount of information collected with as few pharmacokinetic samples as possible.MethodsA validation dataset comprising 164 sparsely sampled individuals using ritonavir-boosted darunavir was used for validation. Standard plots of predictions and residuals, NPDE, visual predictive check, and bootstrapping were applied to both the validation set and the combined learning/validation set in NONMEM to assess model performance. D-optimal designs for darunavir were then calculated in PopED and further evaluated in NONMEM through simulations.ResultsExternal validation confirmed model robustness and accuracy in most scenarios but also highlighted several limitations. The best one-, two-, and three-point sampling strategies were determined to be pre-dose (0 h); 0 and 4 h; and 1, 4, and 19 h, respectively. A combination of samples at 0, 1, and 4 h was comparable to the optimal three-point strategy. These could be used to reliably estimate individual pharmacokinetic parameters, although with fewer samples, precision decreased and the number of outliers increased significantly.ConclusionsOptimal sampling strategies derived from this model could be used in clinical practice to enhance therapeutic drug monitoring or to conduct additional pharmacokinetic studies.

Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.

Producing accurate and reliable inference from animal social network analysis depends on the sampling strategy during data collection. An increasing number of studies now use large-scale deployment of GPS tags to collect data on social behaviour. However, these can rarely capture whole populations or sample at very high frequencies. To date, little guidance exists when making prior decisions about how to maximise sampling effort to ensure that the data collected can be used to construct reliable social networks. We use a simulation-based approach to generate a functional understanding of how the accuracy of various network metrics is affected by decisions along three fundamental axes of sampling effort: coverage, frequency and duration. Researchers often face trade-offs between these three sampling axes, for example due to resource limitations, and thus we identify which axes need to be prioritised as well as the effectiveness of different deployment and analytical strategies. We found that the sampling level across the three axes has different consequences depending on the social network metrics that are estimated. For example, global metrics are more sensitive than local metrics to the proportion of the population tracked, and that among local metrics some are more sensitive to sampling duration than others. Our research demonstrates the importance of establishing an optimal sampling configuration for drawing relevant and robust inferences and presents a range of practical advice for designing GPS based sampling strategies in accordance with the research objectives.

Surrogate models are popular tool to approximate the functional relationship of expensive simulation models in multiple scientific and engineering disciplines. Successful use of surrogate models can provide significant savings of computational cost. However, with a variety of surrogate model approaches available in literature, it is a difficult task to select an appropriate one at hand. In this paper, we present an overview of surrogate model approaches with an emphasis of their application for variance-based global sensitivity analysis, including polynomial regression model, high-dimensional model representation, state-dependent parameter, polynomial chaos expansion, Kriging/Gaussian Process, support vector regression, radial basis function, and low rank tensor approximation. The accuracy and efficiency of these approaches are compared with several benchmark examples. The strengths and weaknesses of these surrogate models are discussed, and the recommendations are provided for different types of applications. For ease of implementations, the packages, as well as toolboxes, of surrogate model techniques and their applications for global sensitivity analysis are collected.

Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data’s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area’s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods.

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation.

Background Researchers in the field of rare diseases are often confronted with difficulties in achieving sufficiently large sample sizes, given the small case numbers and geographical dispersion of patients. We aimed to evaluate three different sampling strategies that have proven effective for other hard-to-reach populations and compare their effectiveness in the context of rare diseases. Methods Within a cross-sectional online study, we compared three sampling strategies (respondent-driven sampling, online-based sampling, and location-based sampling) for their effectiveness in recruiting patients with three rare diseases. Additionally, we compared characteristics of recruited patients. All participants completed an online questionnaire using REDCap. Measures Our primary outcome was the number of patients recruited by each sampling strategy. We further assessed study perception, sociodemographic and clinical characteristics, as well as validated measures to assess depression severity (PHQ-9), anxiety severity (GAD-7), illness cognitions (ICQ), health-related quality of life (SF-12) and psychological burden through somatic symptoms (SSD-12). Results A total of N  = 254 individuals accessed the survey website and N  = 225 completed the sociodemographic characteristics and were included in the analysis. Mean age was 42.53 years ( SD  = 13.06) and N  = 156 (69%) participants were female. Online-based sampling yielded the highest number of participants ( N  = 184, 82% (95% CI [79%, 85%])), followed by location-based sampling ( N  = 22, 10% (95% CI [4%, 16%])) and respondent-driven sampling ( N  = 19, 8% (95% CI [2%, 14%])). Patient characteristics differed significantly regarding gender and satisfaction with medical care, with online-sampling having the highest share of female participants and patients recruited via location-based sampling reporting a higher satisfaction with their overall care. Across all three sampling strategies, participants showed typical features of populations affected by rare diseases such as high rates of depression and anxiety symptoms and reduced quality of life. Conclusions Our study identified online-based sampling as the most effective recruitment strategy for patients with rare diseases. It may be the most promising approach, especially with limited recruitment periods. Potential biases such as gender imbalances should be considered. We encountered substantial challenges with respondent-driven and location-based sampling. Addressing these challenges in future studies may help to make better use of the potential that lies in these sampling strategies.

Accurate analysis of uranium content in powder is great significant for environmental protection and supporting clean energy. However, precise analysis of uranium-containing powders faces numerous challenges, such as high radioactivity and uneven powder distribution. This investigation employs a rigorously designed experimental-comparison approach to quantitatively elucidate the influence of particle size on blending homogeneity, and to systematically delineate the mechanisms by which powder homogeneity, spatial sampling location (dimensional distribution) and sample quantity affect the accuracy of uranium-content determination. The results demonstrate that the blending homogeneity of the powder increases as particle size decreases. Powder homogeneity constitutes the decisive determinant of analytical accuracy: the higher the homogeneity, the greater the accuracy. Sampling strategy directly governs representativeness: concentrating sampling within a localized zone decreases accuracy, whereas increasing the number of samples markedly enhances the reliability of the result. On the basis of these findings, an optimized sampling protocol for uranium-bearing powders has been established and validated: the powder is comminuted until it completely passes through an 80-mesh sieve (aperture ≈ 180 µm); nine sampling points are then selected uniformly along three mutually orthogonal axes (length, width and height) within the container. Validation experiments confirm that this protocol achieves analytical accuracy approaching 100%.

In the last two centuries, since the dawn of modern geology, heavy minerals have been used to investigate sediment provenance and for many other scientific or practical applications. Not always, however, with the correct approach. Difficulties are diverse, not just technical and related to the identification of tiny grains, but also procedural and conceptual. Even the definition of “heavy minerals” is elusive, and possibly impossible. Sampling is critical. In many environments (e.g., beaches), both absolute and relative heavy mineral abundances invariably increase or decrease locally to different degrees owing to hydraulic-sorting processes, so that samples close to \"neutral composition\" are hard to obtain. Several widely shared opinions are misleading. Choosing a narrow size-window for analysis leads to increased bias, not to increased accuracy or precision. Only point-counting provides real volume percentages, whereas grain-counting distorts results in favor of smaller minerals. This paper also briefly reviews the heavy mineral associations typically found in diverse plate-tectonic settings. A mineralogical assemblage, however, only reproduces the mineralogy of source rocks, which does not correlate univocally with the geodynamic setting in which those source rocks were formed and assembled. Moreover, it is affected by environmental bias, and by diagenetic bias on top in the case of ancient sandstones. One fruitful way to extract information on both provenance and sedimentological processes is to look for anomalies in mineralogical–textural relationships (e.g., denser minerals bigger than lower-density minerals; harder minerals better rounded than softer minerals; less durable minerals increasing with stratal age and stratigraphic depth). To minimize mistakes, it is necessary to invariably combine heavy mineral investigations with the petrographic analysis of bulk sand. Analysis of thin sections allows us to see also those source rocks that do not shed significant amounts of heavy minerals, such as limestone or granite, and helps us to assess heavy mineral concentration, the “outer” message carrying the key to decipher the “inner message” contained in the heavy mineral suite. The task becomes thorny indeed when dealing with samples with strong diagenetic overprint, which is, unfortunately, the case of most ancient sandstones. Diagenesis is the Moloch that devours all grains that are not chemically resistant, leaving a meager residue difficult or even impossible to interpret when diagenetic effects accumulate through multiple sedimentary cycles. We have conceived this friendly little handbook to help the student facing these problems, hoping that it may serve the purpose.

In recent years, the safety of engineering systems is seriously threatened by increasingly complex and uncertain engineering environment, and the reliability analysis of engineering structure has attracted increasing attention. The sampling methods are widely used because of its simplicity and universality. However, their applications are limited by the expensive computational cost. To ease the computation burden, a new bubble sampling method (BSM) is proposed in this study. Its core idea is to generate several bubbles in the safe and failure domains, in which the performance function signs of samples located in these bubbles can be directly determined and are unnecessary to be computed. In this way, the number of function calls is greatly reduced. Moreover, a new bubble optimization model is developed, in which the uniform sampling strategy is adopted. Several numerical and engineering applications are validated to demonstrate the performances of the proposed BSM, which confirms its computational efficiency and accuracy.