Probabilistic Forecasting in Decision-Making: New Methods and Applications

This thesis develops new methods to generate probabilistic forecasts and applies these methods to solve operations problems in practice. The first chapter introduces a new product life cycle model, the tilted-Gompertz model, which can predict the distribution of period sales and cumulative sales over a product's life cycle. The tilted-Gompertz model is developed by exponential tilting the Gompertz model, which has been widely applied in modelling human mortality. Due to the tilting parameter, this new model is flexible and capable of describing a wider range of shapes compared to existing life cycle models. In two empirical studies, one on the adoption of new products and the other on search interest in social networking websites, I find that the tilted-Gompertz model performs well on quantile forecasting and point forecasting, when compared to other leading life-cycle models. In the second chapter, I develop a new exponential smoothing model that can capture life-cycle trends. This new exponential smoothing model can also be viewed as a tilted-Gompertz model with time-varying parameters. The model can adapt to local changes in the time series due to the smoothing parameters in the exponential smoothing formulation. When estimating the parameters, prior information is included in the regularization terms of the model. In the empirical studies, the new exponential smoothing model outperforms several leading benchmark models in predicting quantiles on a rolling basis. In the final chapter, I develop a predictive system that predicts distributions of passengers' connection times and transfer passenger flows at an airport using machine learning methods. The predictive system is based on regression trees and copula-based simulations. London Heathrow airport currently uses this proposed system and has reported significant accuracy improvements over their legacy systems

Beyond Dyads: Decision support for online multiparty negotiation, coalition formation and negotiation outcomes

Ph.DDOCTOR OF PHILOSOPH

Effect of electro-acupuncture on gene expression in heart of rats with stress-induced pre-hypertension based on gene chip technology

AbstractObjectiveTo explore electro-acupuncture's (EA's) effect on gene expression in heart of rats with stress-induced pre-hypertension and try to reveal its biological mechanism based on gene chip technology.MethodsTwenty-seven Wistar male rats were randomly divided into 3 groups. The stress-induced hypertensive rat model was prepared by electric foot-shocks combined with generated noise. Molding cycle lasted for 14 days and EA intervene was applied on rats in model + EA group during model preparation. Rat Gene 2.0 Sense Target Array technology was used for the determination of gene expression profiles and the screened key genes were verified by real-time quantitative polymerase chain reaction (RT-PCR) method.ResultsCompared with blank control group, 390 genes were changed in model group; compared with model control group, 330 genes were changed in model+EA group. Significance analysis of gene function showed that the differentially expressed genes are those involved in biological process, molecular function and cellular components. RT-PCR result of the screened key genes is consistent with that of gene chip test.ConclutionEA could significantly lower blood pressure of stress-induced pre-hypertension rats and affect its gene expression profile in heart. Genes that related to the contraction of vascular smooth muscle may be involved in EA's anti-hypertensive mechanism

A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs betterthan BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.</p

Evaluation of Stability and Biocompatibility of Chitosan/Sodium Tripolyphosphate and Chitosan/Flaxseed Gum Composite Nanoparticles Loaded with Bighead Carp Peptides

Chitosan nanoparticle is becoming an excellent carrier for the delivery of bioactive components due to the advantages of simple preparation, low cost and high biocompatibility. Previous studies have shown that chitosan/sodium tripolyphosphate (CS/TPP) and chitosan/flaxseed gum (CS/FG) nanoparticles loaded with bighead carp peptides (BCP) have the advantages of small particle size, high encapsulation rate and significant slow-release effect. This study explored the effects of ionic strength, pH, simulated digestion and storage time on the preparation of chitosan/sodium tripolyphosphate (CS/TPP-BCP) and chitosan/flaxseed gum (CS/FG-BCP) nanoparticles, and evaluated the extracellular lactate dehydrogenase content and antioxidant capacity in vivo of Caco-2 cells treated with the chitosan nanoparticles and their cellular uptake. The results showed that the two kinds of chitosan nanoparticles were stable under acidic conditions and sensitive to a solution with opposite charges. The stability of the nanoparticles loaded with bighead peptides was higher than that of free peptides and both nanoparticles showed higher biocompatibility and cell uptake

Integrated analysis of single-cell and Bulk RNA sequencing reveals a malignancy-related signature in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD), the most common histotype of lung cancer, may have variable prognosis due to molecular variations. The research strived to establish a prognostic model based on malignancy-related risk score (MRRS) in LUAD.MethodsWe applied the single-cell RNA sequencing (scRNA-seq) data from Tumor Immune Single Cell Hub database to recognize malignancy-related geneset. Meanwhile, we extracted RNA-seq data from The Cancer Genome Atlas database. The GSE68465 and GSE72094 datasets from the Gene Expression Omnibus database were downloaded to validate the prognostic signature. Random survival forest analysis screened MRRS with prognostic significance. Multivariate Cox analysis was leveraged to establish the MRRS. Furthermore, the biological functions, gene mutations, and immune landscape were investigated to uncover the underlying mechanisms of the malignancy-related signature. In addition, we used qRT-PCR to explore the expression profile of MRRS-constructed genes in LUAD cells.ResultsThe scRNA-seq analysis revealed the markers genes of malignant celltype. The MRRS composed of 7 malignancy-related genes was constructed for each patient, which was shown to be an independent prognostic factor. The results of the GSE68465 and GSE72094 datasets validated MRRS’s prognostic value. Further analysis demonstrated that MRRS was involved in oncogenic pathways, genetic mutations, and immune functions. Moreover, the results of qRT-PCR were consistent with bioinformatics analysis.ConclusionOur research recognized a novel malignancy-related signature for predicting the prognosis of LUAD patients and highlighted a promising prognostic and treatment marker for LUAD patients

Utility of clinical metagenomics in diagnosing malignancies in a cohort of patients with Epstein-Barr virus positivity

BackgroundsDifferentiation between benign and malignant diseases in EBV-positive patients poses a significant challenge due to the lack of efficient diagnostic tools. Metagenomic Next-Generation Sequencing (mNGS) is commonly used to identify pathogens of patients with fevers of unknown-origin (FUO). Recent studies have extended the application of Next-Generation Sequencing (NGS) in identifying tumors in body fluids and cerebrospinal fluids. In light of these, we conducted this study to develop and apply metagenomic methods to validate their role in identifying EBV-associated malignant disease.MethodsWe enrolled 29 patients with positive EBV results in the cohort of FUO in the Department of Infectious Diseases of Huashan Hospital affiliated with Fudan University from 2018 to 2019. Upon enrollment, these patients were grouped for benign diseases, CAEBV, and malignant diseases according to their final diagnosis, and CNV analysis was retrospectively performed in 2022 using samples from 2018 to 2019.ResultsAmong the 29 patients. 16 of them were diagnosed with benign diseases, 3 patients were diagnosed with CAEBV and 10 patients were with malignant diseases. 29 blood samples from 29 patients were tested for mNGS. Among all 10 patients with malignant diagnosis, CNV analysis suggested neoplasms in 9 patients. Of all 19 patients with benign or CAEBV diagnosis, 2 patients showed abnormal CNV results. The sensitivity and specificity of CNV analysis for the identification for tumors were 90% and 89.5%, separately.ConclusionsThe application of mNGS could assist in the identification of microbial infection and malignancies in EBV-related diseases. Our results demonstrate that CNV detection through mNGS is faster compared to conventional oncology tests. Moreover, the convenient collection of peripheral blood samples adds to the advantages of this approach

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead