Search results for "predictive models"
showing 10 items of 14 documents
Predicting shifting sustainability trade-offs in marine finfish aquaculture under climate change
2018
Defining sustainability goals is a crucial but difficult task because it often involves the quantification of multiple interrelated and sometimes conflicting components. This complexity may be exacerbated by climate change, which will increase environmental vulnerability in aquaculture and potentially compromise the ability to meet the needs of a growing human population. Here, we developed an approach to inform sustainable aquaculture by quantifying spatio-temporal shifts in critical trade-offs between environmental costs and benefits using the time to reach the commercial size as a possible proxy of economic implications of aquaculture under climate change. Our results indicate that optim…
Macrophytes in boreal streams: Characterizing and predicting native occurrence and abundance to assess human impact
2016
Abstract Macrophytes are a structurally and functionally essential element of stream ecosystems and therefore indispensable in assessment, protection and restoration of streams. Modelling based on continuous environmental gradients offers a potential approach to predict natural variability of communities and thereby improve detection of anthropogenic community change. Using data from minimally disturbed streams, we described natural macrophyte assemblages in pool and riffle habitats separately and in combination, and explored their variation across large scale environmental gradients. Specifically, we developed RIVPACS-type models to predict the presence and abundance of macrophyte taxa at …
Prediction of incident type 2 diabetes mellitus based on a twenty-year follow-up of the Ventimiglia heart study.
2011
A novel algorithm to predict incident type 2 diabetes mellitus (iT2DM) is presented considering data from a 20-year prospective study in a Southern Italy population. Eight hundred and fifty-eight out of 1,351 subjects (24-85 years range of age) were selected. Incident type 2 diabetes was diagnosed in 103 patients in a 20-year follow-up. The Finnish Diabetes Risk Score (FINDRISC) and the Framingham Offspring Study simple clinical model (FOS) have been used as reference algorithms. Two custom algorithms have been created using Cox parametric hazard models followed by PROBIT analyses: the first one (VHSRISK) includes all the study subjects and the second one (VHS95RISK) evaluates separately su…
Predictive model to identify the risk of losing protective sensibility of the foot in patients with diabetes mellitus
2019
Diabetic neuropathy is defined as the presence of symptoms and signs of peripheral nerve dysfunction in diabetics. The aim of this study is to develop a predictive logistic model to identify the risk of losing protective sensitivity in the foot. This descriptive cross‐sectional study included 111 patients diagnosed with diabetes mellitus. Participants completed a questionnaire designed to evaluate neuropathic symptoms, and multivariate analysis was subsequently performed to identify an optimal predictive model. The explanatory capacity was evaluated by calculating the R (2) coefficient of Nagelkerke. Predictive capacity was evaluated by calculating sensitivity, specificity, and estimation o…
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
How to formulate an accident prediction model for urban intersections.
2009
Several safety prediction models and methods have been developed to eliminate the relationship between the expected accident frequency and various urban intersection geometry and operational attributes. It is generally accepted that accident rates tend to be higher at intersections than on through sections of a road. This is particularly frequent in urban area where roads are characterized by intersections in close succession; moreover, the safe and effective operations of the urban road system can be significantly affected by safety conditions at intersections. In this paper models and methods designed to understand and to predict the accident process at urban intersections are reviewed. I…
Dynamic Modeling and Driving Cycle Prediction for a Racing Series Hybrid Car
2014
International audience; This paper presents Noao, a plug-in series hybrid racing car equipped with an engine/generator set as range extender. To determine the velocity profile, i.e., performance of the car and its power profile, a dynamic model for this car is developed using pedal position as input. This value is easy to measure, representative for race cycles, and presents a novelty. The model is validated with the results from experiments. An analysis based on the map of Magny-Cours racing circuit and drivers pedal action on certain zones of the circuit is formulated and is used as a prediction tool to determine drivers inputs on other racing circuits and generate driving schedules. The …
Identification of predictive biomarkers for the efficacy of nivolumab in patients with advanced non-small cell cancer.
2019
The recent introduction of immunotherapy has disrupted the management of non-small cell lung cancer (NSCLC). Nivolumab, an antibody targeting the immune checkpoint inhibitor PD-1, has shown remarkable results in seconde-line setting after failure of standard first-line chemotherapy. However, only a quarter of patients benefits from this therapy. To date, no predictive biomarker of the therapeutic efficacy of nivolumab has been identified in a clear and consensual manner. The research for predictive biomarkers of efficacy or resistance to this treatment is, therefore, a major challenge.The emergence of high-throughput sequencing over the past decade has had a significant impact on clinical a…
CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease
2023
AbstractThis study was aimed to investigate the predictive value of the radiomics features extracted from pericoronaric adipose tissue — around the anterior interventricular artery (IVA) — to assess the condition of coronary arteries compared with the use of clinical characteristics alone (i.e., risk factors). Clinical and radiomic data of 118 patients were retrospectively analyzed. In total, 93 radiomics features were extracted for each ROI around the IVA, and 13 clinical features were used to build different machine learning models finalized to predict the impairment (or otherwise) of coronary arteries. Pericoronaric radiomic features improved prediction above the use of risk factors alon…
A Quantum-Inspired Classifier for Early Web Bot Detection
2022
This paper introduces a novel approach, inspired by the principles of Quantum Computing, to address web bot detection in terms of real-time classification of an incoming data stream of HTTP request headers, in order to ensure the shortest decision time with the highest accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each HTTP request on the preceding ones. Starting from the a-posteriori probability of each request to belong to a particular class, it is possible to assign a Qubit state representing a combination of the aforementioned probabilities for all available observations of the time series. By levera…