Data Distribution Research Articles

Validation of predictive equations for aerobic fitness in young healthy male students of the University of Ibadan

BackgroundMaximum oxygen consumption is a measure of an individual’s cardiorespiratory fitness which is a singular predictor of an array of diseases. Several exercise and non-exercise assessments are frequently compared to know which method(s) provide the most accurate estimation of aerobic capacity due to difficulties in using the direct method. There is a need to know if an estimation method is the right fit for a population without huge overestimation or underestimation due to ethnical variation. This study was undertaken to assess the estimations of the cardiorespiratory fitness of healthy African males by the submaximal exercise-based and the non-exercise-based equations in undergraduate students of the University of Ibadan, Nigeria.ResultsVO2 was significantly higher in the undergraduate students of the University of Ibadan in all the equations used (44.38 ± 39.07, 62.46 ± 27.61, 44.38 ± 39.07, 62.46 ± 27.61, 46.37 ± 3.31, 46.16 ± 3.64, 47.08 ± 3.19). The two submaximal exercises compared using the Bland–Altman Plot showed a high degree of agreement, further linear regression performed showed no proportional bias on the distribution of data around the mean difference line (p > 0.05). Cross-validation using the Bland–Altman plot and linear regression for the five non–exercise predicted equations with the YMCA submaximal exercise test and Bruce submaximal exercise test all showed a significance difference of (P < 0.05) showed that data from all the methods provided proportional bias on the distribution of data around the mean difference line.ConclusionThis study asserts that caution should be taken when using a non-exercise equation to predict VO2 in the African population. It is therefore recommended to carry out various assessment methods of VO2 estimation in a wider population using various protocols, and also develop a predictive equation for VO2 specific to the African population.

System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring

Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed noise and linear shift-invariant point-spread functions are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic three-dimensional imaging. SSAI-3D enables robust axial deblurring by generating a diverse, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D is applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of three-dimensional heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.

Half logistic exponentiated inverse Rayleigh distribution: Properties and application to life time data.

This paper presents a novel extension of the exponentiated inverse Rayleigh distribution called the half-logistic exponentiated inverse Rayleigh distribution. This extension improves the flexibility of the distribution for modeling lifetime data for both monotonic and non-monotonic hazard rates. The statistical properties of the half-logistic exponentiated inverse Rayleigh distribution, such as the quantiles, moments, reliability, and hazard function, are examined. In particular, we provide several techniques to estimate the half-logistic exponentiated inverse Rayleigh distribution parameters: weighted least squares, Cramér-Von Mises, maximum likelihood, maximum product spacings and ordinary least squares methods. Moreover, numerical simulations were performed to evaluate these estimation methods for both small and large samples through Monte Carlo simulations, and the finding reveals that the maximum likelihood estimation was the best among all estimation methods since it comprises small mean square error compared to other estimation methods. We employ real-world lifetime data to demonstrate the performance of the newly generated distribution compared to other distributions through practical application. The results show that the half-logistic exponentiated inverse Rayleigh distribution performs better than alternative versions of the Rayleigh distributions.

A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts.

Functional contractility of young soccer players' muscles in the context of playing positions

The high-intensity activity of soccer players during a match are affected by the basic contractile properties of lower limb muscles. Several studies have confirmed that playing position crucially determines absolute performance and physical load and also determines the intensity of fast movements during matches. However, little is known whether muscle contractile properties vary in relation to a specific playing position. The aim of this paper is to describe differences in functional contractility of muscles in young soccer players in the context of playing position. U14-U15 soccer players (n = 127) were divided into 6 groups based on their playing position: G - Goalkeeper (n = 14), FB - Fullback (n = 23), CB – Centre-back (n = 28), W – Winger (n = 15), M – midfielder, (n = 28), F - Forward (n = 19). Functional contractility of the biceps femoris and rectus femoris in the dominant lower limb was assessed from the following variables: contraction time (tc), maximal displacement of the muscle contraction (dm) and contraction velocity (vc) measured by a non-invasive method of tensiomyography using a TMG S2 system (TMG-BMC Ltd, Slovenia). The Shapiro-Wilk test showed a violation of normality of the data distribution. Differences in contractile properties in terms of the playing position factor were evaluated using Kruskal-Wallis analysis of variance (K-W Anova) with multiple pairwise comparisons. The K-W Anova results showed significant differences in the maximal amplitude (dm) and velocity of contraction (vc) for the biceps femoris muscle (H = 19.008; p = 0.002 and H = 24.421; p = 0.001, respectively). In the variable of time of contraction (tc), a significant difference between the playing positions was identified for the rectus femoris muscle (H = 18.257; p = 0.003). The results of the study suggest that different playing positions of young soccer players are associated with specific muscle contractility requirements. These differences suggest that muscle characteristics are influenced by the functional demands of each playing position. The findings point to the need for personalised training interventions that consider the specific biomechanical and functional demands of playing positions, which may contribute to performance enhancement and injury prevention.

Twofold Auxiliary Information Under Two-Phase Sampling: An Improved Family of Double-Transformed Variance Estimators

Outlier values and rankings are important for emphasizing data distribution variability, which improves the accuracy and effectiveness of variance estimations. To enhance the estimation of finite population variance in a two-phase sampling framework, this study presents an improved class of double exponential-type estimators by utilizing the outlier values and ranks of an auxiliary variable. A theoretical analysis is conducted to derive the biases and mean squared errors (MSEs) of these estimators using first-order approximations. A comprehensive simulation study is then performed to analyze the performance of the proposed estimators. The results clearly show that the new estimators provide more precise estimates, achieving a higher percentage relative efficiency (PRE) across all simulated scenarios. Furthermore, three data sets are analyzed to further confirm the efficiency of the proposed estimators as compared to other existing estimators. These results emphasize the potential of the proposed class of estimators to optimize variance estimation techniques, making it a more cost-effective and accurate choice for researchers using two-phase sampling in a variety of domains.

Gender trends in orthopedic surgical residency programs in Canada over 20 years.

Gender disparities exist in several surgical specialties, particularly in orthopedic surgery. The purpose of this study was to determine the current trends in gender diversity among orthopedic surgical residents in Canada over the last 20 years. We analyzed gender distribution data for orthopedic trainees from the Canadian Resident Matching Service (CaRMS) for 2013-2022 and the Canadian Post-MD Education Registry for 2000-2022 using linear and quadratic regressions. More male (4.7%) than female medical students (1.9%) applied to an orthopedics program (p < 0.001). The proportion of male applicants entering CaRMS who applied to orthopedics followed a quadratic (U-shaped) distribution over time (p = 0.01). The proportion of female applicants remained unchanged from 2013 to 2022 (p > 0.9). However, for matching results, among the applicants who applied to orthopedic surgery, there was no gender effect (men 56.1% matched, women 50.3% matched; p = 0.3). The proportion of residency spots offered to female applicants remained at around 30%, without significant changes over time (p = 0.1). The number of female orthopedic graduates increased linearly from 2000 to 2021 (p < 0.001), projected to reach gender equalization (at 50%) by 2060. Female residents experienced higher attrition in residency (3.4%) than male residents (2.1%; p = 0.05), and this gender difference is decreasing over time (p = 0.03). Over 2 decades, women have shown consistently lower rates of application to orthopedic surgery programs than their male colleagues. Women who matched experienced higher attrition rates than men, although this appears to be improving over time.

Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning

Abstract With the advancement of astronomical observation technology and the substantial increase in data volume, traditional methods for pulsar identification are increasingly challenged by the dynamic nature of data distributions. To address this, our study introduces a multimodal incremental learning approach utilizing Bayesian neural networks. This method enables the model to adapt to new data distributions while preserving the knowledge of previous data. In our experiments, we utilized pulsar data sets from two telescopes and compared our new method with traditional techniques. The research results demonstrate that our method performs comparably to traditional methods across all evaluation metrics, while showing a significant improvement in handling data distribution discrepancy, with the F1-score increasing from approximately 70% to over 95%. Specifically, our model achieved an accuracy of 97.93%, a recall of 96.13%, and an F1-score of 97.02% under conditions of distributional disparities. These findings not only confirm the model's capability to adapt to dynamic data environments but also effectively address the challenges of data distribution discrepancy, significantly enhancing the predictive accuracy of pulsar identification in the context of evolving and variable radio frequency interference environments.

Sources and Trends of CO, O3, and Aerosols at the Mount Bachelor Observatory (2004–2022)

Understanding baseline O3 is important as it defines the fraction of O3 coming from global sources and not subject to local control. We report the occurrence and sources of high baseline ozone days, defined as a day where the daily maximum 8 h average (MDA8) exceeds 70 ppb, as observed at the Mount Bachelor Observatory (MBO, 2.8 km asl) in Central Oregon from 2004 to 2022. We used various indicators and enhancement ratios to categorize each high-O3 day: carbon monoxide (CO), aerosol scattering, the water vapor mixing ratio (WV), the aerosol scattering-to-CO ratio, backward trajectories, and the NOAA Hazard Mapping System Fire and Smoke maps. Using these, we identified four causes of high-O3 days at the MBO: Upper Troposphere/Lower Stratosphere intrusions (UTLS), Asian long-range transport (ALRT), a mixed UTLS/ALRT category, and events enhanced by wildfire emissions. Wildfire sources were further divided into two categories: smoke transported in the boundary layer to the MBO and smoke transported in the free troposphere from more distant fires. Over the 19-year period, 167 high-ozone days were identified, with an increasing fraction due to contributions from wildfire emissions and a decreasing fraction of ALRT events. We further evaluated trends in the O3 and CO data distributions by season. For O3, we found an overall increase in the mean and median values of 2.2 and 1.5 ppb, respectively, from the earliest part of the record (2004–2013) compared to the later part (2014–2022), but no significant linear trends in any season. For CO, we found a significant positive trend in the summer 95th percentiles, associated with increasing fires in the Western U.S., and a strong negative trend in the springtime values at all percentiles (1.6% yr−1 for 50th percentile). This decline was likely associated with decreasing emissions from East Asia. Overall, our findings are consistent with the positive trend in wildfires in the Western United States and the efforts in Asia to decrease emissions. This work demonstrates the changing influence of these two source categories on global background O3 and CO.

Unsupervised domain adaptation teacher–student network for retinal vessel segmentation via full-resolution refined model

Retinal blood vessels are the only blood vessels in the human body that can be observed non-invasively. Changes in vessel morphology are closely associated with hypertension, diabetes, cardiovascular disease and other systemic diseases, and computers can help doctors identify these changes by automatically segmenting blood vessels in fundus images. If we train a highly accurate segmentation model on one dataset (source domain) and apply it to another dataset (target domain) with a different data distribution, the segmentation accuracy will drop sharply, which is called the domain shift problem. This paper proposes a novel unsupervised domain adaptation method to address this problem. It uses a teacher–student framework to generate pseudo labels for the target domain image, and trains the student network with a combination of source domain loss and domain adaptation loss; finally, the weights of the teacher network are updated from the exponential moving average of the student network and used for the target domain segmentation. We reconstructed the encoder and decoder of the network into a full-resolution refined model by computing the training loss at multiple semantic levels and multiple label resolutions. We validated our method on two publicly available datasets DRIVE and STARE. From STARE to DRIVE, the accuracy, sensitivity, and specificity are 0.9633, 0.8616,and 0.9733, respectively. From DRIVE to STARE, the accuracy, sensitivity, and specificity are 0.9687, 0.8470, and 0.9785, respectively. Our method outperforms most state-of-the-art unsupervised methods. Compared with domain adaptation methods, our method also has the best F1 score (0.8053) from STARE to DRIVE and a competitive F1 score (0.8001) from DRIVE to STARE.

Federated Learning: An Approach for Managing Data Privacy and Security in Collaborative Learning

Abstract: In the field of machine learning, federated learning (FL) has become a breakthrough paradigm, provided a decentralized method of training models while solving issues with data security, privacy, and scalability. This study offers a thorough analysis of FL, including an examination of its underlying theories, its varieties, and a comparison with more conventional machine learning techniques. We explore the drawbacks of conventional machine learning techniques, especially when sensitive and distributed data is involved. We also explain how FL addresses these drawbacks by leveraging collaborative learning across decentralized devices or servers. We also highlight the various fields in which FL finds application, including healthcare, industries, IoT, mobile devices, and education, demonstrating its potential to deliver tailored services and predictive analytics while maintaining data privacy. Furthermore, we address the main obstacles to FL adoption, such as costly communication, heterogeneous systems, statistical heterogeneity, and privacy concerns, and we suggest possible directions for future research to effectively overcome these obstacles. In order to facilitate future study and growth in this quickly developing discipline, this review attempts to shed light on the advances and challenges of FL.

Density Peak Clustering Algorithm Based on Shared Neighbors and Natural Neighbors and Analysis of Electricity Consumption Patterns

ABSTRACTThe Density Peaks Clustering (DPC) algorithm is well‐known for its simplicity and efficiency in clustering data of arbitrary shapes. However, it faces challenges such as inconsistent local density definitions and sample assignment errors. This paper introduces the Shared Neighbors and Natural Neighbors Density Peaks Clustering (SN‐DPC) algorithm to address these issues. SN‐DPC redefines local density by incorporating weighted shared neighbors, which enhances the density contribution from distant samples and provides a better representation of the data distribution. It also establishes a new similarity measure between samples using shared and natural neighbors, which increases intra‐cluster similarity and reduces assignment errors, thereby improving clustering performance. Compared with DPC‐CE, IDPC‐FA, DPCSA, FNDPC, and traditional DPC, SN‐DPC demonstrated superior effectiveness on both synthetic and real datasets. When applied to the analysis of electricity consumption patterns, it more accurately identified load consumption patterns and usage habits.

View adaptive multi-object tracking method based on depth relationship cues

Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. This paper proposed View Adaptive Multi-Object Tracking Method Based on Depth Relationship Cues (ViewTrack) to enable MOT to adapt to the scene's dynamic changes. Firstly, based on exploiting the depth relationships between objects by using the position information of the bounding box, a view-type recognition method based on depth relationship cues (VTRM) is proposed to perceive the changes of depth and view within the dynamic scene. Secondly, by adjusting the interval partitioning strategy to adapt to the changes in view characteristics, a view adaptive partitioning method for tracklet sets and detection sets (VAPM) is proposed to achieve sparse decomposition in occluded scenes. Then, combining pedestrian displacement with Intersection over Union (IoU), a displacement modulated Intersection over Union method (DMIoU) is proposed to improve the association accuracy between detection and tracklet boxes. Finally, the comparison results with 12 representative methods demonstrate that ViewTrack outperforms multiple metrics on the benchmark datasets. The code is available at https://github.com/Hamor404/ViewTrack.

Evaluating the Impact of Climate Change on the Asia Habitat Suitability of Troides helena Using the MaxEnt Model

Butterflies are highly sensitive to climate change, and Troides helena, as an endangered butterfly species, is also affected by these changes. To enhance the conservation of T. helena and effectively plan its protected areas, it is crucial to understand the potential impacts of climate change on its distribution. This study utilized a MaxEnt model in combination with ArcGIS technology to predict the global potential suitable habitats of T. helena under current and future climate conditions, using the species’ distribution data and relevant environmental variables. The results indicated that the MaxEnt model provided a good prediction accuracy for the distribution of T. helena. Under the current climate scenario, the species is primarily distributed in tropical regions, with high suitability areas concentrated in tropical rainforest climates. In future climate scenarios, the suitable habitat areas for T. helena in medium and high suitability categories generally show an expansion trend, which increases over time. Especially under the SSP5-8.5 scenario, by the 2090s, the area of high suitability for T. helena is projected to increase by 42.85%. The analysis of key environmental factors revealed that precipitation of the wettest quarter (Bio16) was the most significant environmental factor affecting the distribution of T. helena. The species has high demands for precipitation and temperature and can adapt to future climate warming. This study is valuable for identifying the optimal conservation areas for T. helena and provides a reference for future conservation efforts.

Nontarget Catches of Green and Brown Lacewings (Insecta: Neuroptera: Chrysopidae, Hemerobiidae) Collected by Light- and Volatile-Baited Traps in the Transcarpathian Lowland (W Ukraine)

Distribution data on the lacewing fauna of the data-deficient Transcarpathian Lowland (West Ukraine) were provided. The attractivity of phenylacetaldehyde-(FLO) and isoamyl alcohol-based (SBL) lures designed for trapping lepidopteran pests to lacewings was also studied and compared to the efficiency of light traps traditionally used in studies on neuropterans. In the three-year study, 374 individuals of 10 species were caught. Although the light trap was the most efficient method, the efficiency of the tested lures could also be proved. Regarding abundances, FLO was significantly more efficient than the SBL lure. The lures could supplement the checklist of the fauna with two species and attracted an especially high number of Chrysoperla species. In the case of parallel use with light traps, they serve as an efficient standardised combined method for trapping lacewings, both in faunistic studies and plant protection applications.

A STUDY ON THE IMPACT OF SUBSCRIPTION-BASED MODELS ON CONSUMER BUYING BEHAVIOUR WITH RESPECT TO E-COMMERCE PLATFORMS

Subscription services have transformed the e-commerce landscape from its early origins to a $196.35 billion industry, providing benefits such as convenience and predictable revenue streams. This study examines the impact of subscription-based models on consumer buying behaviour within the e-commerce sector, focusing on factors influencing adoption, customer loyalty, and satisfaction. Employing a descriptive research design with a sample of 206 respondents from Ahmedabad, the study utilized non-parametric statistical methods due to non- normal data distribution. The findings reveal a significant relationship between subscription duration and overall spending, a strong correlation between consumer satisfaction and loyalty, and a notable impact of perceived service quality on subscription continuation. These insights highlight the critical role of high service quality in retaining and expanding subscription bases and offer practical recommendations for businesses to optimize their subscription models. Future research should explore a broader range of e-commerce platforms and diverse consumer demographics to enhance understanding of subscription dynamics.

Compact complementary slot resonator antenna with L-Probe feed excitation for wireless communication

ABSTRACT This study presents a concise design of a complementary slot resonator antenna employing the L-probe feed excitation. The antenna features a compact size of 0.086λ0 × 0.086λ0 × 0.006λ0, unlike the wire-loaded miniaturised slot antenna, which has a greater height. The extensive electrical path length of the complementary slot resonator results in the proposed antenna resonating at 434 MHz, a frequency substantially lower than the wavelength (λ0). The work explores essential parameters, such as return loss, radiation patterns and efficiency, highlighting a consistent correlation between simulation and measurement results through a parametric study. In simulation, the antenna exhibits an efficiency of 92.19%, while in practical measurements, it maintains a high efficiency of 89.73%. In addition, the study incorporates simulated input impedance and surface current distribution data to enhance the comprehensive analysis of the antenna design. The antenna is fabricated using copper metal due to its excellent electrical conductivity properties and the achieved resonant frequency has applications in various wireless communication scenarios.

An enhanced Transformer framework with incremental learning for online stock price prediction.

To address the limitations of existing stock price prediction models in handling real-time data streams-such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices-this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model's adaptability to dynamic changes. To ensure that the model retains prior knowledge while integrating new information, a time series elastic weight consolidation (TSEWC) algorithm is introduced to enable efficient incremental training with incoming data. Experiments conducted on five publicly available datasets demonstrate that the proposed method not only effectively captures the temporal information in the data but also fully exploits the correlations among multi-dimensional features, significantly improving stock price prediction accuracy. Notably, the method shows robust performance in coping with non-stationary and frequently changing financial market data.

In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated and analyzed the effects of the subject, device, position, and orientation variabilities on DL HAR models using the HARVAR and REALDISP datasets. The Maximum Mean Discrepancy (MMD) was used to quantify shifts in the data distribution caused by these variabilities, and the relationship between the distribution shifts and model performance was drawn. Our HARVAR results show that different types of variability significantly degraded the DL model performance, with an inverse relationship between the data distribution shifts and performance. The compounding effect of multiple variabilities studied using REALDISP further underscores the challenges of generalizing DL HAR models to real-world conditions. Analyzing these impacts highlights the need for more robust models that generalize effectively to real-world settings. The MMD proved valuable for explaining the performance drops, emphasizing its utility in evaluating distribution shifts in HAR data.

Navigating Tabular Data Synthesis Research Understanding User Needs and Tool Capabilities

In an era of rapidly advancing data-driven applications, there is a growing demand for data in both research and practice. Synthetic data have emerged as an alternative when there are not enough real data available or when these data may not be shared (e.g., due to privacy regulations). Synthesizing tabular data presents unique and complex challenges, especially handling (i) missing values, (ii) dataset imbalance, (iii) diverse column types, and (iv) complex data distributions, as well as preserving (v) column correlations, (vi) temporal dependencies, and (vii) integrity constraints (e.g., functional dependencies) present in the original dataset. Although significant progress has been made recently in the development of generational models, there is no one-size-fitsall solution for tabular data today and choosing the right tool for a particular use case remains a difficult task. In this paper, we survey the state of the art in Tabular Data Synthesis (TDS) and examine user needs by defining a set of functional and non-functional requirements. We also evaluate the reported performance of 37 TDS research tools on these requirements, develop a tool selection guide to help users find a suitable TDS tool for their use case, and identify open challenges in TDS research, especially with respect to data management.