Search Results (83)

Left ventricular systolic dysfunction (LVSD) is associated with increased mortality and is sometimes reversible when found early. Artificial intelligence (AI)-enabled electrocardiogram (ECG) has emerged as an efficient screening tool for LVSD, but has not been validated in left bundle branch block (LBBB) patients. The clinical significance of developing an AI prediction model for LBBB patients lies in the fact that LBBB can be a cause, consequence, or both of LVSD. This pilot study was designed to develop an AI model for LVSD detection in the LBBB population using a limited dataset. ECG data from 508 patients with sinus rhythm and LBBB were labeled based on an LVSD threshold of 35%. To enhance the performance of a model derived from such a small and skewed dataset, we combined an autoencoder-based anomaly detection model with a convolutional neural network (CNN). We used a lead-wise ensemble technique for the final classification. Experimental results showed an accuracy of 0.81, precision of 0.87, recall of 0.56, and an area under the receiver operating characteristic curve of 0.75 in LVSD prediction among LBBB patients. Despite the limited dataset size, our study findings suggest the potential of deep learning techniques in detecting LVSD in patients with LBBB. Full article

The Gini coefficient, while widely used to quantify inequality in biological size distributions, lacks the capacity to resolve directional asymmetry inherent in Lorenz curves, a critical limitation for understanding skewed resource allocation strategies. To address this, we extend our prior geometric framework of the rotated and right-shifted Lorenz curve (RRLC) by introducing two original asymmetry metrics: the positional shift ratio (P_L, defined as $x_c/\sqrt{2}$, where $x_c$ is the x-coordinate of the RRLC’s maximum value point) and the area ratio (P_A, defined as A_L/(A_L + A_R), where A_L and A_R denote the areas under the left and right segments of the RRLC). These indices uniquely dissect contributions of dominant versus small individuals to overall inequality, with P_L reflecting the peak position of the RRLC and P_A quantifying the area dominance of its left segment. Theoretically, P_L directly links to the classical Lorenz asymmetry coefficient S (defined as $S=x_c^{\prime }+y_c^{\prime }$, where $\left(x_c^{\prime },y_c^{\prime }\right)$ is the tangent point on the original Lorenz curve with a 45° slope) through S = 2 − 2P_L, bridging geometric transformation and parametric asymmetry analysis. Applied to 480 Shibataea chinensis Nakai shoots, our analysis revealed that over 99% exhibited pronounced left-skewed distributions, where abundant large leaves drove the majority of leaf area inequality, challenging assumptions of symmetry in plant canopy resource allocation. The framework’s robustness was further validated by the strong correlation between P_A and P_L. By transforming abstract Lorenz curves into interpretable bell-shaped performance curves, this work provides a novel toolkit for analyzing asymmetric size distributions in ecology. The proposed metrics can be applied to refine light-use models, monitor phenotypic plasticity under environmental stress, and scale trait variations across biological hierarchies, thereby advancing both theoretical and applied research in plant ecology. Full article

Traditional 3D radar can only detect the planar characteristic information of a target. Thus, it cannot describe its spatial geometric characteristics, which is critical for accurate vehicle classification. To overcome these limitations, this paper investigates elevation features using 4D millimeter-wave radar data and presents a maximum likelihood estimation (MLE)-based vehicle classification method. The elevation data collected by 4D radar from a real road scenario are applied for further analysis. By establishing radar coordinate systems and geodetic coordinate systems, the distribution feature of vehicles’ elevation is analyzed by spatial geometric transformation referring to the radar installation parameters, and a Gaussian-based probability distribution model is subsequently proposed. Further, the data-driven parameter optimization on likelihood probabilities of different vehicle samples is performed using a large-scale elevation dataset, and an MLE-based vehicle classification method is presented for identifying small and large vehicles. The experimental results show that there are significant differences in elevation distribution from two different vehicle types, where large vehicles exhibit a wider range of left-skewed distribution in different cross-sections, while small vehicles are more concentrated with a right-skewed distribution. The Gaussian-based MLE method achieves an accuracy of 92%, precision of 87% and recall of 98%, demonstrating excellent performance for traffic monitoring and related applications. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Given the increasing number of phenomena that demand interpretation and investigation, developing new distributions and families of distributions has become increasingly essential. This article introduces a novel family of distributions based on the exponentiated reciprocal of the hazard rate function named the new Lomax-G family of distributions. We demonstrate the family’s flexibility to predict a wide range of lifetime events by deriving its cumulative and probability density functions. The new Lomax–Weibull distribution (NLW) is studied as a sub-model, with analytical and graphical evidence indicating its efficiency for reliability analysis and complex data modeling. The NLW density encompasses a variety of shapes, such as symmetrical, semi-symmetrical, right-skewed, left-skewed, and inverted J shapes. Furthermore, its hazard function exhibits a broad range of asymmetric forms. Five estimation techniques for determining the parameters of the proposed NLW distribution include the maximum likelihood, percentile, least squares, weighted least squares, and Cramér–von Mises methods. The performance of the estimators of the studied inferential methods is investigated through a comparative Monte Carlo simulation study and numerical demonstration. Additionally, the effectiveness of the NLW is validated by means of four real-world datasets. The results indicate that the NLW distribution provides a more accurate fit than several competing models. Full article

Species are disappearing worldwide, and changes in climate and land use are commonly assumed to be the most important causes. Organisms are counteracting the negative effects of environmental factors on their survival by evolving various defence strategies, which positively affect their fitness. Here, the question addressed is: can evolution shape these defence strategies so that they positively affect the fitness of an organism? This question is complex and depends on the taxa and environmental factors. Therefore, here, only a special case of this question is studied in deceptive species of orchids: reproductive success (RS, ratio of the number of fruits to the number of flowers produced by a plant during the whole season), a commonly used measure of fitness is used to develop a model describing how RS affects the number of flowers, n, of a plant. This model predicts that: (i) the resulting relationship between RS and n is a positively skewed parabola, (ii) the distribution of the numbers of individuals with a specific number (n) of flowers, NI(n), also resembles a parabola and is also positively skewed, and that (iii) the peak of the distribution of NI is to the left of the peak of RS. A large set of data is presented that supports these predictions. If the data set is small, the concave positively skewed parabolic RS–n dependence is obscured by other factors. Full article

This paper studies new characterizations and expressions of the weak group (WG) inverse and its dual over the quaternion skew field. We introduce a dual to the weak group inverse for the first time in the literature and give some new characterizations for both the WG inverse and its dual, named the right and left weak group inverses for quaternion matrices. In particular, determinantal representations of the right and left WG inverses are given as direct methods for their constructions. Our other results are related to solving the two-sided constrained quaternion matrix equation $\mathbf{AXB}=\mathbf{C}$ and the according approximation problem that could be expressed in terms of the right and left WG inverse solutions. Within the framework of the theory of noncommutative row–column determinants, we derive Cramer’s rules for computing these solutions based on determinantal representations of the right and left WG inverses. A numerical example is given to illustrate the gained results. Full article

To investigate the distribution characteristics of the micropore structure in high-performance self-compacting concrete (C80), high-resolution X-ray computed tomography, AVIZO software (version 2024.1), and scanning electron microscopy were employed to observe and analyze the internal pore structure of C80 self-compacting concrete specimens. The main conclusions are as follows: There is a large number of pore structures within the carbonate rock-based high-performance self-compacting concrete. At a testing precision range of 10 μm, the micropores exhibit a circular feature with good overall circularity. Observations through SEM, scanning electron microscopy, reveal that there are micro-cracks or interconnected crack structures within the high-performance concrete, with widths ranging from 0.5 to 2 μm, and the sample contains tiny voids of 3 to 10 μm. A statistical analysis of the micropores within the carbonate rock-based self-compacting concrete indicates that the pore diameter follows a three-parameter normal distribution. Due to the limitations of experimental observation and precision, the experimental statistical results show a positively skewed (high peak and left-skewed) phenomenon. This paper proposes a “correction of skewed peak” method for the analysis and discussion of the calculation of the “third parameter C” in the statistical results. The results show that the method proposed in this paper can quickly, objectively, and optimally determine the third parameter, compensating for the missing data not accounted for below 10 μm and the limitations of the finite number of experimental samples, providing a reference for examining the distribution of pores within concrete. Full article

Non-negative distributions are important tools in various fields. Given the importance of achieving a good fit, the literature offers hundreds of different models, from the very simple to the highly flexible. In this paper, we consider the power–Pareto model, which is defined by its quantile function. This distribution has three parameters, allowing the model to take different shapes, including symmetrical and left- and right-skewed. We provide different distributional characteristics and discuss parameter estimation. In addition to the already-known Maximum Likelihood and Least Squares of the logarithm of the order statistics estimation methods, we propose several additional methods. A simulation study and an application to two datasets are conducted to illustrate the performance of the estimation methods. Full article

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system’s robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis. Full article

Consumer interest in foods enriched with phytochemical compounds for health benefits has prompted plant breeders to focus on developing new cultivars with an enhanced content of specific compounds. Studies regarding the exploration of germplasms of species of great economic importance, such as maize, could be useful in this task. This study aimed to assess the physical grain traits and phenolic compound variations (including anthocyanins, flavonoids, and proanthocyanidins) in blue-purple maize accessions from various Mexican races. We examined 207 accessions from 21 Mexican maize races, evaluating physical grain traits such as weight of one hundred grains (W100G), endosperm type (ET), pigment location, and grain color. Phenolic composition analysis encompassed total soluble phenolics (TSP), total anthocyanin content (TAC), flavonoids (FLAV), and proanthocyanidins (PAs). The predominant endosperm type was floury, with W100G values indicating a large grain size and the pigment primarily located in the aleurone layer. Among phenolic composition variables, only TSP exhibited a normal distribution, while others skewed towards the left side. A hierarchical analysis of phenolic composition data revealed three distinct groups comprising different numbers of Mexican varieties, with TAC proving the most effective for grouping. Our comprehensive exploration of maize diversity featuring blue-purple grain coloration has led to the identification of novel maize varieties with outstanding phenolic contents. Full article

Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications. Full article

Sylvester-polynomial-conjugate matrix equations unify many well-known versions and generalizations of the Sylvester matrix equation $AX-XB=C$ which have a wide range of applications. In this paper, we present a general approach to Sylvester-polynomial-conjugate matrix equations via groupoids, vector spaces, and matrices over skew polynomial rings. The obtained results are applied to Sylvester-polynomial-conjugate matrix equations over complex numbers and quaternions. The main role in our approach is played by skew polynomial rings, which are well-known tools in algebra to provide examples of asymmetry between left-sided and right-sided versions of many ring objects. Full article

Background: Cognitive assessments for patients with neurocognitive disorders are mostly measured by the Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) as screening tools. These cognitive scores are usually left-skewed and the results of the association analysis might not be robust. This study aims to study the distribution of the cognitive outcomes and to discuss potential solutions. Materials and Methods: In this retrospective cohort study of individuals with subjective cognitive decline or mild cognitive impairment, the inverse-transformed cognitive outcomes are modelled using different statistical distributions. The robustness of the proposed models are checked under different scenarios: with intercept-only, models with covariates, and with and without bootstrapping. Results: The main results were based on the VCAT score and validated via the MoCA score. The findings suggested that the inverse transformation method improved the modelling the cognitive scores compared to the conventional methods using the original cognitive scores. The association of the baseline characteristics (age, gender, and years of education) and the cognitive outcomes were reported as estimates and 95% confidence intervals. Bootstrap methods improved the estimate precision and the bootstrapped standard errors of the estimates were more robust. Cognitive outcomes were widely analysed using linear regression models with the default normal distribution as a conventional method. We compared the results of our suggested models with the normal distribution under various scenarios. Goodness-of-fit measurements were compared between the proposed models and conventional methods. Conclusions: The findings support the use of the inverse transformation method to model the cognitive outcomes instead of the original cognitive scores for early-stage neurocognitive disorders where the cognitive outcomes are left-skewed. Full article

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the “garbage in, garbage out” issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks. Full article