Model Discrepancy Quantification in Simulation-Based Design of Dynamical Systems

Reduction of fouling and contamination by predictive kinetic models

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real-time traffic information to travellers. To help travellers better make their route choice decisions, there is a strong need to predict traffic congestion and disseminate the predicted congestion information to travellers. This paper offers some insights and predictions on how ATIS information provision is becoming more pervasive due to recent advances in telecommunication systems. The paper also discusses how ATIS systems will likely evolve based on the experiences of Information Service Providers (ISP) and ATIS modelling specialists. Then, it reviews four types of prediction models: 1) simulation models; 2) dynamic traffic assignment (DTA) models; 3) statistical models; and 4) heuristic models. The functional requirements and capabilities of the four types of prediction models are discussed and summarised. Furthermore, a comprehensive prediction procedure is presented, which combines the four types of prediction models.

This article contains description how to create prediction models for more efficient utilization of running IT resources. Prediction model creation is presented as one of the most important steps within the process of the knowledge discovery. Presented models were created based on real data from a local IT company by means of the programming language R. This article also marginally covers the steps, which are to be carried out before the actual modelling. The steps to mention are the following: available data understanding and their subsequent preprocessing for further work, individual attributes analysis using data visualization by means of graphs and tracking certain behavior and correlations in data. First of all, designing models using linear and multiple regression will be followed in detail. Then, the methods of new values prediction will be described using the R language. Finally, the different types of prediction models will be considered using appropriate indicators. The results obtained should assist and simplify the methods of the knowledge discovery in certain enterprises based on the available databases, and also help to choose the most suitable prediction models learning method.

Potatoes are an important crop in the world; they are the main source of food for a large number of people globally and also provide an income for many people. The true forecasting of potato yields is a determining factor for the rational use and maximization of agricultural practices, responsible management of the resources, and wider regions’ food security. The latest discoveries in machine learning and deep learning provide new directions to yield prediction models more accurately and sparingly. From the study, we evaluated different types of predictive models, including K-nearest neighbors (KNN), gradient boosting, XGBoost, and multilayer perceptron that use machine learning, as well as graph neural networks (GNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTM), which are popular in deep learning models. These models are evaluated on the basis of some performance measures like mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to know how much they accurately predict the potato yields. The terminal results show that although gradient boosting and XGBoost algorithms are good at potato yield prediction, GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data. Gradient boosting resulted in an MSE of 0.03438 and an R2 of 0.49168, while XGBoost had an MSE of 0.03583 and an R2 of 0.35106. Out of all deep learning models, GNNs displayed an MSE of 0.02363 and an R2 of 0.51719, excelling in the overall performance. LSTMs and GRUs were reported to be very promising as well, with LSTMs comprehending an MSE of 0.03177 and GRUs grabbing an MSE of 0.03150. These findings underscore the potential of advanced predictive models to support sustainable agricultural practices and informed decision-making in the context of potato farming.

RATIONALE. Sex differences have been observed in the incidence, severity, mortality, and outcomes of acute respiratory distress syndrome (ARDS) and COVID-19. However, the molecular basis for the differences between males and females remains unclear. METHODS. Bronchoalveolar lavage (BAL) samples collected from NIH-supported ARDSNet trials were provided by BioLINCC. To generate proteomics datasets, we purified extracellular vesicles (EVs) and then EV proteins, which were subsequently analyzed them using mass spectrometry. The proteomics datasets were precleaned and differentially expressed proteins were clustered to identify sex-specific endotypes. The prediction models for the identified endotypes were developed by training a univariate logistic regression model, and model performance was evaluated based on three-class accuracy.RESULTS. There were 57 ARDS patients and 49 healthy controls. We identified 6,616 proteins in BAL-EVs. Among these, the expression of 861 proteins was significantly upregulated or downregulated in patients compared to controls. K-means clustering of the demographics-correlated proteins revealed two distinct endotypes. These endotypes could be clearly differentiated based on gender, demographics, p/f ratio, smoking status, and various functional annotations. The prediction model developed based on proteomic datasets achieved an area under the curve (AUC) of 0.993 (sensitivity 94.59%, specificity 95.00%). When transformed into clinical settings, the new model derived from clinical variables showed an AUC value above 0.750. The prediction performance was improved if we combined omics data with clinical settings to develop the model (AUC 0.797), demonstrating the excellent performance of these three types of prediction models. CONCLUSION. Our study provides molecular mechanisms and prediction models for the sex differences in ARDS. Sex may serve as an independent predictor of the development, responses to treatments, mortality, and long-term outcomes associated with ARDS.

Predictive analytics has gained a lot of reputation in the emerging technology Big data. Predictive analytics is an advanced form of analytics. Predictive analytics goes beyond data mining. A huge amount of medical data is available today regarding the disease, their symptoms, reasons for illness, and their effects on health. But this data is not analysed properly to predict or to study a disease. The aim of this paper is to give a detailed version of predictive models from base to state-of-art, describing various types of predictive models, steps to develop a predictive model, their applications in health care in a broader way and particularly in diabetes.

In this paper, we describe a general process on how to integrate different types of predictive models within an organization to fully leverage the benefits of predictive modeling. The three major predictive modeling applications discussed in this paper are marketing, pricing, and GBAAlgorithm models. These applications have been well applied and published over the past several years for the Property and Casualty Manufacturing Industry, but this paper and discussions focused on their individual application. We believe that significant value can be realized if they are fully integrated, offering manufacturing companies the opportunity to take an enterprise wide view of managing their business through analytics. Therefore, the paper will discuss a general process on how they can be integrated and how the integrated result can assist insurance companies with managing the complex insurance business, such as minimizing the GBA(Gross Building Area) Algorithm cycle and achieving profitable growth and reacting to external market forces faster than their competition.

Ahlstrom Corp, Finland

In this paper, a least-mean-squares (LMS) finite memory (FM) estimator for a stochastic discrete-time state space model is obtained by taking the conditional expectation of the estimated state given a finite number of inputs and outputs measured on the recent finite horizon. Any a priori state information is not involved and any arbitrary constraints are not imposed. For a general discrete-time state space model with both system and measurement noises, the LMS FM estimator is represented in a closed-form. It turns out that the proposed LMS FM estimator has the unbiased property and the linear structure with respect to inputs and outputs on the recent finite horizon.

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated. A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model. The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model. PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Many cities have already installed bike-sharing systems for several years now, but especially in recent years with the rise of micro-mobility, many efforts are being made worldwide to improve the operation of these systems. Technology has an essential role to play in the success of micro-mobility schemes, including bike-sharing systems. In this paper, it is examined if a state-of-the-art mobile application (app) can contribute to increasing the usage levels of such a system. It is also seeking to identify groups of travelers, who are more likely to be affected by the sophisticated app. With this aim, a questionnaire survey was designed and addressed to the users of the bike-sharing system of the city of Thessaloniki, Greece, as well as to other residents of the city. Through a descriptive analysis, the most useful services that an app can provide are identified. Most importantly, two different types of predictive models (i.e., classification tree and binary logit model) were applied in order to identify groups of users who are more likely to shift to or to use the bike-sharing system due to the sophisticated app. The results of the two predictive models confirm that people of younger ages and those who are not currently users of the system are those most likely to be attracted to the system due to such an app. Other factors, such as car usage frequency, education, and income also appeared to have slight impact on travelers’ intention to use the system more often due to the app.

Changes in environments over 20years and the assessment of individual long-term exposure to air pollution for epidemiology.

Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.

Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining

An integrated recommender system for improved accuracy and aggregate diversity