Subset Of Variables Research Articles

A Conflict-Driven Interface Between Symbolic Planning and Nonlinear Constraint Solving

Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that represent (in)feasible subsets of continuous variables and constraints, and encodes this knowledge in the logical description. As a foundation for this algorithm, we introduce Planning with Nonlinear Transition Constraints (PNTC), a novel planning formulation that clarifies the exact assumptions our algorithm requires and can be applied to model Task and Motion Planning (TAMP) efficiently. Our experimental results show that our framework significantly outperforms alternative optimization-based approaches for TAMP.

Design of Reinforced Fuzzy Model Driven to Feature Selection Through Univariable-Based Correlation and Multivariable-Based Determination Coefficient Analysis

In this article, we introduce a design methodology of reinforced fuzzy models based both on univariate analysis and multivariable analysis to cope with high-dimensional problems. This approach is aimed at reducing the design process and curbing computing overhead inherently associated with the increasing volume of data in terms of both their number and the dimensionality of the feature space. The critical features of the proposed fuzzy models are highlighted as follows: First, the essential input variables of the model are selected by running the univariable and multivariable analyses. In univariate analysis, input variables with a strong linear relationship with the output variable are selected through correlation analysis completed for each input space and output space. On the contrary, in multivariable analysis, input variables are chosen by comparing the determination coefficients obtained from the subsets of input variables. Second, according to the analysis of the input variable, we construct two different kinds of fuzzy models. The first fuzzy model comprises the design of the univariable-based fuzzy model (UFM) and its aggregation. The UFMs are made by the individual input variables selected from the univariable analysis using a correlation coefficient. The subspaces formed by correlation analysis are applied for determining the centers of the membership function (MF). The results produced by individual fuzzy models are aggregated through some <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -conorms. The second fuzzy model is with the fuzzy clustering for improving the form of fuzzy space in the premise part of a fuzzy rule. To curb the dramatic increase in size of fuzzy rule in high-dimensional problems, the clustering space is employed as the fuzzy space, and the partition matrix produced by fuzzy provided the required degrees of the MF. Experimental studies include a suite of synthetic and publicly available data. The superiority of the proposed design methodology was demonstrated by using 34 publicly available datasets and also compared with the conventional models associated with the fuzzy rule-based models as well as the state-of-the-art models reported in the literature.

Mothers’ Willingness to Sacrifice for Their Children: An Exploratory Approach

Intensive mothering ideology emphasizes mothers’ sacrifice for their children. Despite the increase in women’s social and economic participation, mothers continue to experience conflicts between career and family obligations due to the conventional idea of “good mothers.” Do all mothers prioritize their children’s needs? This study focuses on whether mothers are willing to sacrifice their happiness and whether such beliefs change over time. We explore the factors contributing to mothers’ intention to sacrifice and its change from two theoretical perspectives, rational choice model and linked lives perspective. Using the first three waves of the Panel Study on Korean Children, the findings show that almost half of all mothers are not willing to sacrifice their own happiness for their children at each wave. Both rational choice and linked lives perspectives contribute to explaining mothers’ view of sacrifice in the baseline year. The change in mothers’ view on sacrifice over time is significantly influenced by a subset of variables including their spouse’s positions on sacrifice and social exchange experience with their children (the linked lives perspective), and the emotional values that mothers place on babies (the rational choice model).

Cucumber powdery mildew detection method based on hyperspectra-terahertz.

To explore the use of information technology in detecting crop diseases, a method based on hyperspectra-terahertz for detecting cucumber powdery mildew is proposed. Specifically, a method of effective hyperspectrum establishment, a method of spectral preprocessing, a method of selecting the feature wavelength, and a method of establishing discriminant models are studied. Firstly, the effective spectral information under visible light and near infrared is preprocessed by Savitzky-Golay (SG) smoothing, discrete wavelet transform, and move sliding window, which determine the optimal preprocessing method to be wavelet transform. Then stepwise discriminant analysis is used to select the feature wavelengths in the visible and near-infrared bands, forming the feature space. According to the features, a linear discriminant model is established for the wave bands, and the average recognition rate of cucumber powdery mildew is 93% in the whole wave band. The preprocessing method of terahertz data, the screening method of terahertz effective spectrum, the selection method of feature wavelength and the establishment method of classification model are studied. Python 3.8 is used to preprocess the terahertz raw data and establish the terahertz effective spectral data set for subsequent processing. Through iterative variable subset optimization - iterative retaining informative variables (IVSO-IRIV), the terahertz effective spectrum is screened twice to form the terahertz feature space. After that, the optimal regularization parameter and regularization solution methods are selected, and a sparse representation classification model is established. The accuracy of cucumber powdery mildew identification under the terahertz scale is 87.78%. The extraction and analysis methods of terahertz and hyperspectral feature images are studied, and more details of lesion samples are restored. Hence, the use of hyperspectral and terahertz technology can realize the detection of cucumber powdery mildew, which provides a basis for research on the hyperspectral and terahertz technology in detection of crop diseases.

Vigor identification of maize seeds by using hyperspectral imaging combined with multivariate data analysis

As an important food crop in China, rapid and accurate discrimination of maize seed vigor is crucial for agricultural development. In this study, 1,680 maize seeds were subjected to artificial accelerated aging treatment with “Zhengdan 958″ as the research object to verify the difference in vigor between damaged and healthy seeds based on the results of the standard germination tests. A Hyperspectral imaging (HSI) system was used to obtain spectral information of samples, and the original spectra were preprocessed using second-order Savitzky-Golay smoothing (SG-2), first derivative (FD), detrending (DE), standard normal variate (SNV), and multiplicative scatter correction (MSC) methods. The 49, 60, 54, and 40 numbers of feature wavelengths (nm) were extracted from the processed spectra using successive projections algorithm (SPA), uninformative variable elimination (UVE), interval random frog (IRF), and iteratively variable subset optimization (IVSO), respectively. The decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) models were constructed based on full wavelength and feature wavelength, of which the best model was DE-UVE-ANN, and its identification accuracy reached 95.24 %. The experimental results show that the UVE algorithm is the most effective method for preprocessing, and the accuracy of LDA and ANN models built based on it is above 85.71 % and 89.76 %, respectively. In addition, the hyperspectral images were visualized based on an object-oriented approach to observe intuitive identification results. The results indicate that the proposed method is instructive for the vigor identification of maize seeds.

Simultaneous clustering and variable selection: A novel algorithm and model selection procedure.

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a new clustering algorithm, called Cardinality K-means or CKM, and by proposing a novel model selection strategy. CKM is able to perform simultaneous clustering and variable selection with high stability. In two simulation studies and an empirical demonstration with genetic data, CKM consistently outperformed competing methods in terms of recovering cluster partitions and identifying signaling variables. Meanwhile, our novel model selection strategy determines the number of clusters based on a subset of variables that are most likely to be signaling variables. Through a simulation study, this strategy was found to result in a more accurate estimation of the number of clusters compared to the conventional strategy that utilizes the full set of variables. Our proposed CKM algorithm, together with the novel model selection strategy, has been implemented in a freely accessible R package.

Examining virtual driving test performance and its relationship to individuals with HIV-associated neurocognitive disorders.

SignificanceExisting screening tools for HIV-associated neurocognitive disorders (HAND) are often clinically impractical for detecting milder forms of impairment. The formal diagnosis of HAND requires an assessment of both cognition and impairment in activities of daily living (ADL). To address the critical need for identifying patients who may have disability associated with HAND, we implemented a low-cost screening tool, the Virtual Driving Test (VDT) platform, in a vulnerable cohort of people with HIV (PWH). The VDT presents an opportunity to cost-effectively screen for milder forms of impairment while providing practical guidance for a cognitively demanding ADL.ObjectivesWe aimed to: (1) evaluate whether VDT performance variables were associated with a HAND diagnosis and if so; (2) systematically identify a manageable subset of variables for use in a future screening model for HAND. As a secondary objective, we examined the relative associations of identified variables with impairment within the individual domains used to diagnose HAND.MethodsIn a cross-sectional design, 62 PWH were recruited from an established HIV cohort and completed a comprehensive neuropsychological assessment (CNPA), followed by a self-directed VDT. Dichotomized diagnoses of HAND-specific impairment and impairment within each of the seven CNPA domains were ascertained. A systematic variable selection process was used to reduce the large amount of VDT data generated, to a smaller subset of VDT variables, estimated to be associated with HAND. In addition, we examined associations between the identified variables and impairment within each of the CNPA domains.ResultsMore than half of the participants (N = 35) had a confirmed presence of HAND. A subset of twenty VDT performance variables was isolated and then ranked by the strength of its estimated associations with HAND. In addition, several variables within the final subset had statistically significant associations with impairment in motor function, executive function, and attention and working memory, consistent with previous research.ConclusionWe identified a subset of VDT performance variables that are associated with HAND and assess relevant functional abilities among individuals with HAND. Additional research is required to develop and validate a predictive HAND screening model incorporating this subset.

Dimensionality reduction and unsupervised classification for high-fidelity reacting flow simulations

The development of reduced-order combustion models able to accurately reproduce the physics of reactive systems has been a highly challenging aspect of numerical combustion research in the recent past. The complexity of the problem can be reduced by identifying and using low-dimensional manifolds able to account for turbulence-chemistry interactions. Recently, Principal Components Analysis (PCA) has shown its potential in reducing the dimensionality of a chemically reactive system while minimizing the reconstruction error. The present work demonstrates the application of the Manifold Generated by Local PCA (MG-L-PCA) approach in direct numerical simulation (DNS) of turbulent flames. The approach is enhanced with an unsupervised clustering based on Vector Quantization PCA (VQPCA) and an on-the-flyPCA-based classification technique. The reduced model is then applied on a three-dimensional (3D) turbulent premixed NH3/air flame by transporting only a subset of the original state-space variables on the computational grid and using the PCA basis to reconstruct the non-transported variables. Results are compared with both a detailed reaction mechanism and a Computational Singular Perturbation (CSP) reduced skeletal mechanism. A comparison between training the reduced model using one-dimensional (1D) and 3D data sets is also included. Overall, the MG-L-PCA allows not only for a reduction in the number of transport equations, but also a significant reduction in the stiffness of the system, while providing highly accurate results.

A supervised machine learning model for determining lubricant oil operating conditions

AbstractMachine learning tools for analysing lubricating oil data have enabled better information in the condition based maintenance (CBM) approach to the process of diagnosing and predicting failures in diesel‐powered vehicle fleets. With the increase in the number of sensors inserted in vehicles, it is possible for companies to stockpile large quantities of information in real time. As such, the development of data‐driven tools will enable accurate identification of the level of wear of a system, evolving CBM into a more reliable and dynamic approach. Following this type of data‐based analysis focusing on determining the wear of systems and equipment, this paper presents an intelligent system for assessing the condition of lubricating oil in automotive diesel engines. To this end, we analyse the use of raw data obtained from the sensors installed in the car and evaluate in conjunction with the insertion of engineered features designed the best way to determine the operating state of the oils. The results presented in this analysis show that to explain 90% of the variation in the original data only the variables kinematic viscosity, dynamic viscosity, engine oil temperature and OSF_v3 are needed. After evaluating the quality of the variables, we conducted an experimental study to analyse the performance of various machine learning algorithms, taking into account the number of features as input data. The results show that the proposed system has the ability to identify the operating conditions of lubricating oil using seven variables as input to a model based on gradient boosting, obtaining a recall result of 93%, precision of 96% and F1‐score of 94%. We conducted a set of additional studies to understand how different subsets of variables affected the performance of the models, and the results show that the best combination includes information regarding the engine speed, coolant and oil temperature, oil pressure, the oil stress factor (OSF_v3), kinematic viscosity, and the dynamic viscosity.

Ensemble Markov Chain Monte Carlo with Teleporting Walkers

We introduce an ensemble Markov chain Monte Carlo approach to sampling from a probability density with known likelihood. This method upgrades an underlying Markov chain by allowing an ensemble of such chains to interact via a process in which one chain's state is cloned as another's is deleted. This effective teleportation of states can overcome issues of metastability in the underlying chain, as the scheme enjoys rapid mixing once the modes of the target density have been populated. We derive a mean-field limit for the evolution of the ensemble. We analyze the global and local convergence of this mean-field limit, showing asymptotic convergence independent of the spectral gap of the underlying Markov chain, and moreover we interpret the limiting evolution as a gradient flow. We explain how interaction can be applied selectively to a subset of state variables in order to maintain advantage on very high-dimensional problems. Finally we present the application of our methodology to Bayesian hyperparameter estimation for Gaussian process regression.

Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400–1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.

Tabu-Search-Based Combinatorial Subset Selection Approach to Support Investigation of Built Environment and Traffic Safety Relationship

Traffic crashes are a leading cause of death globally, with an increasing rate in urban areas. Thus, this study focuses on the relationship between built environment (BE) and traffic safety (TS), by constructing a relationship model using BE variables. The aim of this paper is to determine the best subset of BE variables through a generalizable methodology. The BE is operationalized through the D-classification (e.g., density, diversity, and design), and various datasets are collected from different agencies. TS is operationalized through motor vehicle involved (MOT) and vulnerable road user (VRU)-involved crash frequencies at the zonal level. A preliminary GIS-based process is conducted to associate the crash data at the census block group (BG) level, followed by examining the BE-TS relationships through a series of negative binomial models optimized for subset selection. The model generation is performed automatically by an embedded Tabu Search procedure. Two case studies are presented: a single-county case (Leon County, Florida, U.S.) and a tri-county case (Miami-Dade, Broward, and Palm Beach Counties, Florida, U.S.). Results show that some BE variables such as total population, age of housing stock, number of bus stops, and traffic volume have consistently positive relationships with crash occurrences. In contrast, several factors show varying effects by crash type or location. For example, motorized mode percentage has a negative relation with crash occurrences in the single-county case whereas it is insignificant for the tri-county case where the non-motorized mode percentage has a positive effect on crash occurrences.

Hypothesis Tests for Structured Rank Correlation Matrices

Joint modeling of a large number of variables often requires dimension reduction strategies that lead to structural assumptions of the underlying correlation matrix, such as equal pair-wise correlations within subsets of variables. The underlying correlation matrix is thus of interest for both model specification and model validation. In this article, we develop tests of the hypothesis that the entries of the Kendall rank correlation matrix are linear combinations of a smaller number of parameters. The asymptotic behavior of the proposed test statistics is investigated both when the dimension is fixed and when it grows with the sample size. We pay special attention to the restricted hypothesis of partial exchangeability, which contains full exchangeability as a special case. We show that under partial exchangeability, the test statistics and their large-sample distributions simplify, which leads to computational advantages and better performance of the tests. We propose various scalable numerical strategies for implementation of the proposed procedures, investigate their behavior through simulations and power calculations under local alternatives, and demonstrate their use on a real dataset of mean sea levels at various geographical locations. Supplementary materials for this article are available online.

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE).

Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients’ demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with (RFE). The proposed method is compared with other reference methods such as linear discriminant analysis (LDA) and kernel-based SVM variants including the linear, polynomial, and radial kernels coupled with REF for predicting ICU admissions of COVID-19 patients. An initial performance assessment indicated that the SVM with weighted radial kernels coupled with REF outperformed the other classification methods in discriminating between ICU and non-ICU admissions in COVID-19 patients. Furthermore, applying the Recursive Feature Elimination (RFE) with weighted radial kernel SVM identified a significant set of variables that can predict and statistically distinguish ICU from non-ICU COVID-19 patients. The patients’ weight, PCR Ct Value, CCL19, INF-β, BLC, INR, PT, PTT, CKMB, HB, platelets, RBC, urea, creatinine and albumin results were found to be the significant predicting features. We believe that weighted radial kernel SVM can be used as an assisting ML approach to guide hospital decision makers in resource allocation and mobilization between intensive care and isolation units. We model the data retrospectively on a selected subset of patient-derived variables based on previous knowledge of ICU admission and this needs to be trained in order to forecast prospectively.

Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem

We propose machine learning-based (ML) methods to accelerate convergence to global solutions for the AC Optimal Power Flow (AC-OPF) problem. In particular, for the non-convex AC-OPF problem, optimality-based bound tightening (OBBT) has been observed to be a very effective approach for tightening the variable domains, thus leading to tight convex relaxations that are nearly global optimum solutions. However, by construction, OBBT is computationally expensive even on medium-scaled power networks. To address this issue, we propose a novel ML-based policy to replace the exhaustive algorithm of OBBT by choosing a subset of variables whose tightening of bounds can still contribute to the best improvement of the convex relaxation of the AC-OPF problem. To this end, we leverage historical data of load profiles for a test system to learn a map between the system loading and subset selection of variables which will need to participate in the OBBT algorithm, thus enabling us to find near-global optimal solutions at faster run-times. Finally, we present detailed numerical studies on a few medium-sized benchmark instances, on which we observe up to 6.3× speed-up in OBBT run times.

Manifold-informed state vector subset for reduced-order modeling

Reduced-order models (ROMs) for turbulent combustion rely on identifying a small number of parameters that can effectively describe the complexity of reacting flows. With the advent of data-driven approaches, ROMs can be trained on datasets representing the thermo-chemical state-space in simple reacting systems. For low-Mach flows, the full state vector that serves as a training dataset is typically composed of temperature and chemical composition. The dataset is projected onto a lower-dimensional basis and the evolution of the complex system is tracked on a lower-dimensional manifold. This approach allows for substantial reduction of the number of transport equations to solve in combustion simulations, but the quality of the manifold topology is a decisive aspect in successful modeling. To mitigate manifold challenges, several authors advocate reducing the state vector to only a subset of major variables when training ROMs. However, this reduction is often done ad hoc and without giving detailed insights into the effect of removing certain variables on the resulting low-dimensional data projection. In this work, we present a quantitative manifold-informed method for selecting the subset of state variables that minimizes unwanted behaviors in manifold topologies. While many authors in the past have focused on selecting major species, we show that a mixture of major and minor species can be beneficial to improving the quality of low-dimensional data representations. The desired effects include reducing non-uniqueness and spatial gradients in the dependent variable space. Finally, we demonstrate improvements in regressibility of manifolds built from the optimal state vector subset as opposed to the full state vector.

Robust ranking by ensembling of diverse models and assessment metrics

We propose an ensemble of classification models formed using different assessment metrics. For a given metric, a classifier performs feature selection and combines models based on different subsets of feature variables which we call phalanxes. This first step, which employs the algorithm of phalanx formation, identifies strong and diverse subsets of feature variables. A second phase of ensembling aggregates classifiers across diverse assessment metrics. The proposed method is applied to protein homology data to mine homologous proteins, where the feature variables used for developing classifiers are various measures of similarity scores of proteins, and found robust for ranking both evolutionary close and distant homologous proteins.

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

Spatial-temporal Variations of Discarded Ichthyofauna from a Small-scale Shrimp Fishery: Influence of Ecological Patterns in a Yucatan Coastal Lagoon

Sauz-Sánchez, J.J.; López-Rocha, J.A.; Arceo-Carranza, D., and Poot-López, G.R., 2022. Spatial-temporal variations of discarded ichthyofauna from a small-scale shrimp fishery: Influence of ecological patterns in a Yucatan coastal lagoon. Journal of Coastal Research, 38(4), 860–869. Coconut Creek (Florida), ISSN 0749-0208. Spatial-temporal variations of discarded catches associated with a shrimp-small scale fishery from the Celestun lagoon, Yucatan, Mexico, were analyzed throughout seasonal and spatial (inner, middle, and external estuarine zones) physicochemical gradients to observe the influence of ecological patterns on discarded ichthyofauna. Differences in abundance, biomass, composition, and physicochemical variables between seasons and among zones were tested using a two-way ANOVA and an analysis of dissimilarity, respectively. The best subset of environmental variables (BIOENV) function was used to observe the influence of physicochemical gradients on the discarded fish community. A total of 1611 individuals belonging to 36 species of discarded fish were identified, with mostly an abundance of juveniles occurring for the following species: Achirus lineatus, Archosargus probatocephalus, Cynoscion nebulosus, Eucinostomus gula, Floridichthys polyommus, Lagodon rhomboides, Lucania parva, Mayaheros urophthalmus, and Sphoeroides testudineus. Hence, the data shows a strong selection pressure on juvenile fish. Significant differences in abundance, species composition, and physicochemical variables were observed between seasons and zones. Results from the BIOENV indicated that temperature, salinity, and pH gradients, generated by underground freshwater discharges, influenced the spatial-temporal variations of discarded ichthyofauna from the Celestun lagoon. Furthermore, reproductive seasons, and capabilities to tolerate salinity concentrations also spatially structures a discarded fish community. The results showed the ecological complexity in which fish discards occur, in addition, suggesting measures to reduce discards by including ecological and social aspects to maintain the sustainability of this artisanal shrimp fishery.

Predictors of Adherence to Stroke Prevention in the BALKAN-AF Study: A Machine-Learning Approach.

Background Compared with usual care, guideline-adherent stroke prevention strategy, based on the ABC (Atrial fibrillation Better Care) pathway, is associated with better outcomes. Given that stroke prevention is central to atrial fibrillation (AF) management, improved efforts to determining predictors of adherence with ‘A’ (avoid stroke) component of the ABC pathway are needed. Purpose We tested the hypothesis that more sophisticated methodology using machine learning (ML) algorithms could do this. Methods In this post-hoc analysis of the BALKAN-AF dataset, ML algorithms and logistic regression were tested. The feature selection process identified a subset of variables that were most relevant for creating the model. Adherence with the ‘A’ criterion of the ABC pathway was defined as the use of oral anticoagulants (OAC) in patients with AF with a CHA 2 DS 2 -VASc score of 0 (male) or 1 (female). Results Among 2,712 enrolled patients, complete data on ‘A’-adherent management were available in 2,671 individuals (mean age 66.0 ± 12.8; 44.5% female). Based on ML algorithms, independent predictors of ‘A-criterion adherent management’ were paroxysmal AF, center in capital city, and first-diagnosed AF. Hypertrophic cardiomyopathy, chronic kidney disease with chronic dialysis, and sleep apnea were independently associated with a lower likelihood of ‘A’-criterion adherent management. ML evaluated predictors of adherence with the ‘A’ criterion of the ABC pathway derived an area under the receiver-operator curve of 0.710 (95%CI 0.67–0.75) for random forest with fine tuning.Conclusions Machine learning identified paroxysmal AF, treatment center in the capital city, and first-diagnosed AF as predictors of adherence to the A pathway; and hypertrophic cardiomyopathy, chronic kidney disease with chronic dialysis, and sleep apnea as predictors of non adherence.