Modeling Censored Mobility Demand through Quantile Regression Neural Networks

Shared mobility services require accurate demand models for effective service planning. On one hand, modeling the full probability distribution of demand is advantageous, because the full uncertainty structure preserves valuable information for decision making. On the other hand, demand is often observed through usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have shown them to perform well under such conditions, and in the last two decades, several works have proposed to implement them flexibly through Neural Networks (CQRNN). However, apparently no works have yet applied CQRNN in the Transport domain. We address this gap by applying CQRNN to datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark, as well as common synthetic baseline datasets. The results show that CQRNN can estimate the intended distributions better than both censorship-unaware models and parametric censored models.Comment: 13 pages, 7 figures, 4 table

Capturing Uncertainty in Fatigue Life Data

Time-to-failure (TTF) data, also referred to as life data, are investigated across a wide range of scientific disciplines and collected mainly through scientific experiments with the main objective of predicting performance in service conditions. Fatigue life data are times, measured in cycles, until complete fracture of a material in response to a cyclical loading. Fatigue life data have large variation, which is often overlooked or not rigorously investigated when developing predictive life models. This research develops a statistical model to capture dispersion in fatigue life data which can be used to extend deterministic life models into probabilistic life models. Additionally, a predictive life model is developed using failure-time regression methods. The predictive life and dispersion models are investigated as dual-response using nonparametric methods. After model adequacy is examined, a Bayesian extension and other applications of this model are discussed

Processing Data from Social Dilemma Experiments: A Bayesian Comparison of Parametric Estimators

Observed choices in Social Dilemma Games usually take the form of bounded integers. We propose a doubly-truncated count data framework to process such data. We compare this framework to past approaches based on ordered outcomes and truncated continuous densities using Bayesian estimation and model selection techniques. We find that all three frameworks (i) support the presence of unobserved heterogeneity in individual decision-making, and (ii) agree on the ranking of regulatory treatment effects. The count data framework exhibits superior efficiency and produces more informative predictive distributions for outcomes of interest. The continuous framework fails to allocate adequate probability mass to boundary outcomes, which are often of pivotal importance in these games.Social dilemma games; Hierarchical modeling; Bayesian simulation; Common property resource

Economic Valuation of Black-faced Spoonbill Conservation in Macao

The general objective of this study is to estimate the economic benefits of black-faced Spoonbill conservation in Macao based on public preferences. The specific objectives are as follows to investigate the public's awareness, attitudes and behaviors regarding black-faced Spoonbill conservation in Macao; to estimate the public's willingness to pay (WTP) for the conservation of black-faced Spoonbills in Macao; to identify the factors that affect the WTP; to determine the cost and benefits of a conservation program for black-faced Spoonbills in Macao, to recommend potential funding mechanisms; to run an experiment on hypothetical and real WTP in Macao to validate the large scale CVM study.economic valuation, Macao

Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data

Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub

Added predictive value of high-throughput molecular data to clinical data, and its validation

Hundreds of ''molecular signatures'' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set