scholarly journals Securing Your Relationship: Quality of Intimate Relationships During the COVID-19 Pandemic Can Be Predicted by Attachment Style

2021 ◽  
Vol 12 ◽  
Author(s):  
Stephanie J. Eder ◽  
Andrew A. Nicholson ◽  
Michal M. Stefanczyk ◽  
Michał Pieniak ◽  
Judit Martínez-Molina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relationship Quality ◽  
Attachment Style ◽  
Linear Models ◽  
Self Report ◽  
Learning Models ◽  
Anxious Attachment ◽  
Major Predictor ◽  
Machine Learning Models

The COVID-19 pandemic along with the restrictions that were introduced within Europe starting in spring 2020 allows for the identification of predictors for relationship quality during unstable and stressful times. The present study began as strict measures were enforced in response to the rising spread of the COVID-19 virus within Austria, Poland, Spain and Czech Republic. Here, we investigated quality of romantic relationships among 313 participants as movement restrictions were implemented and subsequently phased out cross-nationally. Participants completed self-report questionnaires over a period of 7 weeks, where we predicted relationship quality and change in relationship quality using machine learning models that included a variety of potential predictors related to psychological, demographic and environmental variables. On average, our machine learning models predicted 29% (linear models) and 22% (non-linear models) of the variance with regard to relationship quality. Here, the most important predictors consisted of attachment style (anxious attachment being more influential than avoidant), age, and number of conflicts within the relationship. Interestingly, environmental factors such as the local severity of the pandemic did not exert a measurable influence with respect to predicting relationship quality. As opposed to overall relationship quality, the change in relationship quality during lockdown restrictions could not be predicted accurately by our machine learning models when utilizing our selected features. In conclusion, we demonstrate cross-culturally that attachment security is a major predictor of relationship quality during COVID-19 lockdown restrictions, whereas fear, pathogenic threat, sexual behavior, and the severity of governmental regulations did not significantly influence the accuracy of prediction.

scholarly journals Securing your relationship: Quality of intimate relationships during the COVID-19 pandemic can be predicted by attachment style

2020 ◽  
Author(s):  
Stephanie Josephine Eder ◽  
Andrew Nicholson ◽  
Michał Stefańczyk ◽  
Michał Pieniak ◽  
Judit Martínez Molina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relationship Quality ◽  
Attachment Style ◽  
Linear Models ◽  
Self Report ◽  
Learning Tools ◽  
Learning Models ◽  
Anxious Attachment ◽  
Machine Learning Models

The COVID-19 pandemic along with the restrictions that were introduced within Europe starting spring 2020 allows for the identification of predictors for relationship quality during unstable and stressful times.The present study began as strict measures were enforced in response to the rising spread of the COVID-19 within Austria, Poland, Spain and Czech Republic. Here, we investigated quality of romantic relationships among 313 participants as movement restrictions were implemented and subsequently phased out cross-nationally. Participants completed self-report questionnaires over a period of seven weeks, where we predicted relationship quality and change in relationship quality using machine learning models that include a variety of potential predictors related to psychological, demographic and environmental variables.On average, our machine learning tools predicted 29% (linear models) and 22% (non-linear models) of the variance with regard to relationship quality. Here, the most important predictors consisted of attachment style (anxious attachment being more influential than avoidance), age, and number of conflicts within the relationship. Interestingly, environmental factors such as the local severity of the pandemic did not exert a measurable influence with respect to predicting relationship quality. As opposed to overall relationship quality, the change in relationship quality during lockdown restrictions could not be predicted accurately by our machine learning models when utilizing our selected features.In conclusion, we demonstrate cross-culturally that attachment security is a major predictor of relationship quality during COVID-19 lockdown restrictions, whereas sexual behavior, fear, pathogenic threat and the severity of governmental regulations did not significantly influence the accuracy of prediction.

scholarly journals Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

2018 ◽  
Author(s):  
Noé Sturm ◽  
Jiangming Sun ◽  
Yves Vandriessche ◽  
Andreas Mayr ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Throughput ◽  
Scaffold Hopping ◽  
Learning Models ◽  
Industrial Data ◽  
Structural Descriptors ◽  
Bioactivity Profile ◽  
Machine Learning Models

<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets. </div><div>Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP. </div><div><br></div>

scholarly journals Cyber-Physical LPG Debutanizer Distillation Columns: Machine Learning-Based Soft Sensors for Product Quality Monitoring

2021 ◽  
Author(s):  
Jože M. Rožanec ◽  
Elena Trajkova ◽  
Jinzhi Lu ◽  
Nikolaos Sarantinoudis ◽  
Georgios Arampatzis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Product Quality ◽  
Learning Models ◽  
State Monitoring ◽  
Soft Sensors ◽  
Distillation Columns ◽  
Operational Conditions ◽  
Equipment State ◽  
Machine Learning Models

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

scholarly journals Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

2021 ◽  
Vol 11 (24) ◽  
pp. 11790
Author(s):  
Jože Martin Rožanec ◽  
Elena Trajkova ◽  
Jinzhi Lu ◽  
Nikolaos Sarantinoudis ◽  
George Arampatzis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Product Quality ◽  
Learning Models ◽  
State Monitoring ◽  
Soft Sensors ◽  
Distillation Columns ◽  
Operational Conditions ◽  
Equipment State ◽  
Machine Learning Models

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

scholarly journals Machine Learning the Redox Potentials of Phenazine Derivatives: A Comparative Study on Molecular Features

2021 ◽  
Author(s):  
Siddharth Ghule ◽  
Sayan Bagchi ◽  
Kumar Vanka
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Linear Models ◽  
High Accuracy ◽  
Active Species ◽  
Learning Models ◽  
Training Set ◽  
Molecular Features ◽  
Redox Active ◽  
Machine Learning Models

<div>Electricity generation is a major contributing factor for greenhouse gas emissions. Energy storage systems available today have a combined capacity to store less than 1% of the electricity being consumed worldwide. Redox Flow Batteries (RFBs) are promising candidates for green and efficient energy storage systems. RFBs are being used in renewable energy systems, but their widespread adoption is limited due to high production costs and toxicity associated with the transition-metal-based redox-active species. Therefore, cheaper and greener alternative organic redox-active species are being investigated. Recent reports have shown organic molecules based on phenazine are promising candidates for redox-active species in RFBs. However, the large number of available organic compounds makes the conventional experimental and DFT methods impractical to screen thousands of molecules in a reasonable amount of time. In contrast, machine-learning models have low development time, short prediction time, and high accuracy; thus, are being heavily investigated for virtual screening applications. In this work, we developed machine-learning models to predict the redox potential of phenazine derivatives in DME solvent using a small dataset of 185 molecules. 2D, 3D, and Molecular Fingerprint features were computed using readily available and easy-to-use python libraries, making our approach easily adaptable to similar work. Twenty linear and non-linear machine-learning models were investigated in this work. These models achieved excellent performance on the unseen data (i.e., R<sup>2</sup> > 0.98, MSE < 0.008 V2 and MAE < 0.07 V). Model performance was assessed in a consistent manner using the training and evaluation pipeline developed in this work. We showed that 2D molecular features are most informative and achieve the best prediction accuracy among four feature sets. We also showed that often less preferred but relatively faster linear models could perform better than non-linear models when the feature set contains different types of features (i.e., 2D, 3D, and Molecular Fingerprints). Further investigations revealed that it is possible to reduce the training and inference time without sacrificing prediction accuracy by using a small subset of features. Moreover, models were able to predict the previously reported promising redox-active compounds with high accuracy. Also, significantly low prediction errors were observed for the functional groups. Although some functional groups had only one compound in the training set, best-performing models could achieve errors (MAPE) less than 10%. The major source of error was a lack of data near-zero and in the positive region. Therefore, this work shows that it is possible to develop accurate machine-learning models that could potentially screen millions of compounds in a short amount of time with a small training set and limited number of easy to compute features. Thus, results obtained in this report would help in the adoption of green energy by accelerating the field of materials discovery for energy storage applications.</div>

scholarly journals Simple Linear Cancer Risk Prediction Models with Novel Features Outperform Complex Approaches

2021 ◽  
Author(s):  
Scott Kulm ◽  
Lior Kofman ◽  
Jason Mezey ◽  
Olivier Elemento
Keyword(s):  
Machine Learning ◽  
Cancer Survival ◽  
Linear Models ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Learning Performance ◽  
Health Study ◽  
Learning Models ◽  
The Uk ◽  
Machine Learning Models

ABSTRACTA patient’s risk for cancer is usually estimated through simple linear models that sum effect sizes of proven risk factors. In theory, more advanced machine learning models can be used for the same task. Using data from the UK Biobank, a large prospective health study, we have developed linear and machine learning models for the prediction of 12 different cancers diagnoses within a 10 year time span. We find that the top machine learning algorithm, XGBoost (XGB), trained on 707 features generated an average area under the receiver operator curve of 0.736 (with a range of 0.65-0.85). Linear models trained with only 10 features were found to be statistically indifferent from the machine learning performance. The linear models were significantly more accurate than the prominent QCancer models (p = 0.0019), which are trained on 45 million patient records and available to over 4,000 United Kingdom general practices. The increase in accuracy may be caused by the consideration of often omitted feature types, including survey answers, census records, and genetic information. This approach led to the discovery of significant novel risk features, including self-reported happiness with own health (relevant to 12 cancers), measured testosterone (relevant to 8 cancers), and ICD codes for rehabilitation procedures (relevant to 3 cancers). These ten feature models can be easily implemented within the clinic, allowing for personalized screening schedules that may increase the cancer survival within a population.

PSIII-5 Predicting live weight using linear body measurements in growing beef calves

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 285-285
Author(s):  
Vanessa Rotondo ◽  
Dan Tulpan ◽  
Katharine M Wood ◽  
Marlene Paibomesai ◽  
Vern R Osborne
Keyword(s):  
Machine Learning ◽  
Body Weight ◽  
Linear Models ◽  
Live Weight ◽  
Body Measurements ◽  
Learning Models ◽  
Linear Measurements ◽  
Beef Calves ◽  
Machine Learning Models ◽  
Linear Body

Abstract The objective of this study is to investigate how linear body measurements relate to and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 103 Angus cross calves were enrolled in the study from wk 2 - 8. Calves were weighed and linear measurements were collected weekly, such as: poll to nose, width across the eyes (WE), width across the right ear, neck length, wither height, heart girth (HG), midpiece height (MH), midpiece circumference, midpiece width (MW), midpiece depth (MD), hook height, hook width, pin height, top of pin bones width (PW), width across the ends of pin bones, nose to tail body length, the length between the withers and pins, forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. To assess relationships between traits and to fit a model to predict BW, data were analyzed using the Weka (The University of Waikato, New Zealand) software using both linear regression (LR) and random forest (RF) machine learning models. The models were trained using a 10-fold cross-validation approach. The automatically derived LR model used 11 traits to fit the data to weekly BW (r2 = 0.97), where the traits with the highest coefficients were HG, PW and WE. The RF model improved further the BW predictions (r2= 0.98). Additionally, sex differences were examined. Although the BW model continued to fit well (r2 0.97), some of the top linear traits differed. The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in beef calves, and that machine learning can further improve the model fit.

scholarly journals Effect of river flow on the quality of estuarine and coastal waters using machine learning models

2018 ◽  
Vol 12 (1) ◽  
pp. 810-823 ◽  
Author(s):  
Mohamad Javad Alizadeh ◽  
Mohamad Reza Kavianpour ◽  
Malihe Danesh ◽  
Jason Adolf ◽  
Shahabbodin Shamshirband ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coastal Waters ◽  
River Flow ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals DeepSatData: Building large scale datasets of satellite images for training machine learning models

2021 ◽  
Author(s):  
Michael Tarasiou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Of View ◽  
Learning Models ◽  
Ground Truth Data ◽  
Machine Learning Models ◽  
Sentinel 2

This paper presents DeepSatData a pipeline for automatically generating satellite imagery datasets for training machine learning models. We also discuss design considerations with emphasis on dense classification tasks, e.g. semantic segmentation. The implementation presented makes use of freely available Sentinel-2 data which allows the generation of large scale datasets required for training deep neural networks (DNN). We discuss issues faced from the point of view of DNN training and evaluation such as checking the quality of ground truth data and comment on the scalability of the approach.

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