Measure Of Distribution Research Articles

CORRELATION BETWEEN BMI AND GONADOTROPIN HORMONES IN THE FEMALE GENDER

Introduction: The pituitary gland affects growth and development. Its main function is in the activity of sexual/reproductive hormones and in the functioning of the glands (thyroid gland, adrenal glands and gonads). Gonadotropin hormones (gonadostimulins) are so named because they stimulate the function of the gonads - male and female sex glands (testicles and ovaries) in which sex cells - sperm and egg cells are created. Overweight is a state of metabolic disorders where excess body fat accumulates to the extent that it can negatively affect human health. Obesity as a BMI factor is related to the functioning of the thyroid gland with its hormones and the pituitary gland with gonadotropin hormones. Purpose of the study: This paper aims to present the correlation between BMI and gonadotropin hormones in reproductive and postmenopausal women. The aim of the paper will also consist in the status of gonadotropin hormones and the functional state of these hormones by comparing them with metabolic disorders such as BMI, obesity and the synthesis of follicle stimulating hormone and gonadotropin hormone. Material and method: A total of 20 patients will be included in the research, divided according to age group into two categories. The first group will include patients aged 10-40 years, the second group patients over 41 years old. Blood (serum) samples will be taken from patients for analyzing gonadotropin hormones: FSH, LH, PRL and thyroid hormones: TSH, FT4, FT3. They will be analyzed with the modern automatic fluorescent enzyme immunoassay method (Vidas-Biomerie). The BMI of the body will be measured with a metric formula where the weight of the patient (in kg) divided by the height of the patient (in m2) set to the square power will give us the measure of body index-BMI (%). SPSS – Software package version 20.0 Numerical series were analyzed using central tendency measures (average, median, minimum values, maximum values), as well as distribution measures (standard deviation); To determine the regularity of the frequency distribution of the examined variables, the Shapiro-Wilk W Test was used; Non-parametric tests for two independent parameters were used to test the significance and difference between some numerical parameters with irregular frequency distribution (Mann Whitney U test); To determine statistical significance, a two-way analysis was used with a significance level (means) of p<0.05. Results: The results of the research will give a real insight into the data of gonadotropic hormones FSH, LH, PRL and thyroid hormones TSH, FT4, FT3 of the female sex in the Tetova region. The results of the research will also provide information about the correlation of BMI and gonadotropin and thyroid hormones. Conclusion: From the analyzes performed on patients of different age groups we come to the conclusion that: • Gonadotropin hormonal disorders can lead to the phenomenon of metabolic diseases such as BMI. • These disorders can have consequences such as: polycystic ovary diseases in women, Hashimoto's genetic syndrome, Graves, infertility in women, etc.

Studying the Impacts of Meteorological Factors on Distribution of Cloud Horizontal Scales Based on Active Satellite

AbstractAs a significant macrophysical property, cloud horizontal scales play a role in cloud radiation, precipitation and vertical cloud overlap. Until now, however, the mechanisms behind the variations in cloud scale distribution have received far less attention. This study utilizes active satellite data from 2007 to 2016 to investigate the spatiotemporal distribution of cloud horizontal scales, and explains the variations through two meteorological factors: wind shear and atmospheric stability. Cloud scales exhibit a distinct power‐law behavior when scale break is not considered, and the power‐law exponent β is a characteristic measure of cloud scale distribution. A smaller power‐law exponent β indicates a higher frequency of large clouds. During boreal summer season, the amount of large clouds is extremely large south of the 40°S but rather small between 10°S and 20°S. As wind shear decreases or atmospheric stability increases, more large clouds occur globally. The underlying mechanisms might be associated with cloud entrainment which can be promoted by wind shear but inhibited by atmospheric stability. However, our analysis of the impacts of these two factors on cloud scale distribution across different regions and heights reveals that both wind shear and atmospheric stability play dual roles on the values of the exponent β. The potential physical mechanisms, including the effects of precipitation, are further discussed. It is observed that precipitation also exerts a dual impact on the values of the exponent β. These findings underscore the significance of considering the impacts of meteorological factors on cloud scale distribution in numerical weather prediction models.

Dynamic data reconciliation for enhancing the prediction performance of long short-term memory network

Abstract In modern process industries, long short-term memory (LSTM) network is widely used for data-driven modeling. Constrained by measuring instruments and environments, the measured datasets are generally with Gaussian/non-Gaussian distributed measurement noise. The noisy datasets will impact the modeling accuracy of the LSTM network and decrease the prediction performance of it. Aiming at addressing prediction performance impairment of the LSTM network under noisy datasets with Gaussian/non-Gaussian distribution, this study introduces dynamic data reconciliation (DDR) both into LSTM network training and into LSTM network test. Results show that DDR improves not only the data quality based on noisy datasets and the training outputs via the Bayesian formula in the model training step, but also the prediction performance based on offline measured information and the test outputs. The implementation scheme of DDR for Gaussian and non-Gaussian distributed noise is purposely designed. The effectiveness of DDR on the LSTM model is verified in a numerical example and a case involving a set of shared wind power datasets.

Understanding Strain Signals from Low-Frequency Distributed Acoustic Sensing In-Well Measurements: Experimental and Numerical Modeling

Summary Over the past few decades, the potential of distributed measurements using in-well fiberoptic data has been demonstrated. In particular, low-frequency distributed acoustic sensing (LF-DAS) has experienced rapid growth toward monitoring wells for integrity and production purposes. Despite technological advancements, applications in this space have been hindered by a limited understanding of the data. The richness of detail in the data is a double-edged sword; while it provides substantial amounts of information to guide the operator, it simultaneously complicates the task of deciphering the underlying signals. To address this issue, it would be advantageous to devise a straightforward approach to understand the nature of common signals due to strain encountered during routine well operations. We present an experimental method for interpreting tubing strain signals using a long spring that is fixed at both ends, mimicking the tubing in a well. Tracking the position of coils over time allows us to record the displacement when a force is applied to the spring. We show that the displacement observed from such an experiment is similar to what we observe in LF-DAS data from wells in operation. Typical signals, such as the pistoning effect on a valve or strain caused by fluid flow, are compared with experimental data. More complex phenomena, such as stick/slip friction and thermal expansion, are modeled using a mass-spring system and compared with wellbore examples. Developing a fundamental understanding of the signals will allow for real-time identification of events, facilitated by fiber-optic data, substantially enhancing operational outcomes by preempting integrity issues and promoting production optimization.

Conflicts between large carnivores and farmers/beekeepers in the Ukrainian Carpathians: Structure, dynamics, spatial distribution and effective coexistence measures

Conflicts between large сarnivores and humans have been common in mountainous regions, with the Carpathians being particularly affected. Recently, interest among researchers and wildlife managers in studying and managing these conflicts has grown significantly. Unfortunately, the coexistence between humans and wildlife and its implications for nature and society have been largely overlooked in Ukraine. This study analyzed conflicts between large carnivores (Canis lupus, Ursus arctos and Lynx lynx) and farmers/beekeepers within designated model territories in the Ukrainian Carpathians, focusing on the effectiveness of conflict prevention strategies implemented between 2018 and 2023. Among the stakeholders analyzed, 79.0% were farmers (livestock owners) and 21.0% were beekeepers. It was observed that 73.0% of farms and apiaries experienced conflicts with large carnivores during the study period; incidents included 64.8% with wolf attacks, 31.3% with bear attacks, and 24.2% with lynx attacks. Notably, conflicts involving livestock owners and large carnivores decreased nearly fivefold, from 54.9% in 2018 to 11.0% in 2023. Similarly, bear-related conflicts among beekeepers nearly halved, from 29.2% to 16.8%. The adoption of electric fences significantly increased among farms and apiaries from none to 33.9%, and the proportion of farmers using dogs for livestock protection without experiencing large carnivore conflicts rose from 3.3% to 20.9%. These pilot findings highlight the effectiveness of such measures in reducing conflicts and improving coexistence quality. The results provide valuable insights into the structure, dynamics, and spatial distribution of conflicts in the Ukrainian Carpathians, underscoring their importance for the conservation of rare species and the implementation of effective management strategies for wild animal populations, sustainable farming, and habitat conservation in regions inhabited by large carnivores. An integrated approach that considers the needs of both human communities and wildlife, involving collaboration among scientific bodies, public organizations, and government entities at various levels, is crucial for devising and executing effective conflict management strategies in diverse landscapes.

Will the poverty‐related UN Sustainable Development Goals be met? New projections

AbstractIn this paper, we discuss the literature and consider the historical relationship between growth and a set of poverty‐related Sustainable Development Goals (SDGs), specifically extreme monetary poverty, undernutrition, stunting, child mortality, maternal mortality and access to clean water. We then make projections for 2030. We find that it is very likely that global poverty‐related SDGs will not be met and by a considerable distance. The implication of this, we argue, is that more emphasis is needed on both policies to raise growth rates (i.e., build productive capacities) and distributive policy measures such as the introduction or expansion of income transfers, and ensuring investments in public goods are sufficient.

Pullback Pressure Gradient-An Emerging Concept in Patients with Coronary Artery Disease.

Fractional flow reserve fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is currently recommended in the management of patients with stable coronary artery disease (CAD). Pullback pressure gradient (PPG) index is an emerging concept that provides a fully quantitative measure of the longitudinal distribution of CAD. It can be derived from FFR, as well as other non-hyperemic indices, and is a novel metric of assessing the focality or diffuseness of CAD. PPG adds a second domain to the assessment of CAD, beyond ischemia as measured by FFR, and may enable clinicians to better inform their patients about the status of their CAD but may also predict potential outcomes before revascularization. In this article, we will provide an in-depth review on the concept of PPG index and its correlation to pre and post revascularization ischemia. We will assess the relationship between PPG index and plaque characteristics and how this is translated into any difference in procedural and long-term clinical outcomes.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

Multivariate range Value-at-Risk and covariance risk measures for elliptical and log-elliptical distributions

In this article, we propose the multivariate range Value-at-Risk (MRVaR) and the multivariate range covariance (MRCov) as two risk measures and explore their desirable properties in risk management. To facilitate analytical analyses, we derive explicit expressions of the MRVaR and the MRCov in the context of the multivariate (log-)elliptical distribution family. Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples. As an application, we propose a range-based mean-variance framework for optimal portfolio selection.

Tensile modulus measurement of composite materials based on distributed optical fiber strain monitoring technology

In this paper, a method was proposed to measure the elastic modulus of each layer-material in multi-layer cylindrical structures by OFDR in a large temperature range. Distributed measurement technology was used to carry out accurate measurements of strain distribution and numerical value on specimens. The accurate force-strain relationship can be obtained if the specimen is small or there is inevitable non-uniformity on the specimen. In this paper, a tensile test platform was built, and the tensile tests of optical fiber (−51.6 °C to 82.15 °C), polymer double-layer specimen (−54 °C to 63 °C), and composite three-layer specimen (−46 °C to 62 °C) were carried out. The elastic modulus of the three kinds of material and their relationships with temperature were obtained. By comparing with the standard test results, the accuracy and reliability of the test were verified.

Surrogate Biomarker to Identify Obesity and Predict Cardiovascular Disease Risk: A Systematic Review

The study investigates the relationship between obesity and cardiovascular disease (CVD) risk, comparing the use of body mass index (BMI) versus other biomarkers in predicting CVD risk among obese individuals. It conducts a systematic literature review following the PRISMA guidelines, searching databases for relevant articles published from 2017-2022. The review analyzes 12 eligible articles and finds that factors beyond just BMI, such as genetics, physical activity, metabolic disorders, previous heart disease history, nutrition, fat distribution, and changes in BMI, can significantly impact the prognosis of heart disease in obese individuals. Importantly, the study shows that measures of fat distribution, like waist-to-height ratio, waist circumference, log-transformed body shape index (LBSIZ), and the ratio of visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT), are superior to BMI in predicting CVD risk among those with obesity. The key takeaway is that while obesity is strongly linked to CVD risk, BMI alone often fails to predict that risk accurately. Fat distribution measures may be a more effective tool for identifying obesity status and predicting associated CVD risk compared to relying solely on BMI.

Image-assisted 2D partition denoising method for distributed optical fiber vibration measurements

The traditional one-dimensional denoising method, commonly applied in vibration signal processing, would encounter issues as the calculation complexity, difficulty in maintaining spatial consistency and inability to address noise distributed spatially when measurements are from multiple points. Distributed optical fiber vibration sensing technology produces measurements densely distributed on both time and spatial axes, accompanied by inevitable noise from demodulation error, uneven packaging of sensing components and environmental interference, in which condition proposing new two-dimensional denoising method becomes particularly important. Focusing on the above spatiotemporal denoising target, this paper proposes a new image-assisted 2D partition denoising method by introducing the morphological methods, improving the traditional empirical mode decomposition, and raising the partition denoising strategy. The experimental and simulated distributed vibration measurements of a cantilever beam were carried out to verify the proposed method from the perspective of modal analysis implemented by the DATA-driven stochastic subspace identification. The analysis results indicate that the proposed method could handle the spatiotemporal noise simultaneously and is in favor of improving the continuity of mode identification results especially.

Regional fat distribution and hepatic fibrosis and steatosis severity in patients with nonalcoholic fatty liver disease and type 2 diabetes.

Epidemiologic findings suggest that measures of body fat distribution predict health outcomes independent of the overall body fat assessed by body mass index (BMI). This study aimed to evaluate the associations of overall and regional body fat with the severity of hepatic steatosis and fibrosis in type 2 diabetic patients with non-alcoholic fatty liver disease. Bioelectric impedance analysis and two newly developed anthropometric indices, namely, A Body Shape Index (ABSI) and Body Roundness Index (BRI), were used to estimate the body fat. Based on fibroscan parameters, significant hepatic fibrosis and severe steatosis were defined as ≥F2 and >66%, respectively. Higher total body fat (odds ratio [OR] 1.107, 95% confidence intervals (CI) 1.038-1.182, p=0.002), trunk fat (OR 1.136, 95% CI 1.034-1.248, p=0.008) and leg fat (OR 1.381, 95% CI 1.139-1.674, p=0.001) were associated with liver fibrosis. However, in contrast to the total body fat (OR 1.088, 95% CI 1.017-1.164, p=0.014) and leg fat (OR 1.317, 95% CI 1.066-1.628, p=0.011), the trunk fat was not associated with severe hepatic steatosis. BRI performed better than trunk, leg and total body fat in predicting hepatic steatosis (OR 2.186, 95% CI 1.370-3.487, p=0.001) and fibrosis (OR 2.132, 95% CI 1.419-3.204, p<0.001). Moreover, the trunk to leg fat ratio and ABSI were not independent predictors of either steatosis or fibrosis (p>0.05). BRI revealed a superior predictive ability for identifying the degree of hepatic steatosis and stiffness than other obesity indices. Additionally, higher levels of adiposity in the trunk, legs, and overall body were linked to an increased risk of developing liver fibrosis. Although trunk fat did not show an association with severe hepatic steatosis, an increase in leg and total fat was related to liver steatosis.

An entropy-based measurement for understanding origin-destination trip distributions: a case study of New York City taxis

ABSTRACT A comprehensive understanding of human mobility patterns in urban areas is essential for urban development and transportation planning. In this study, we create entropy-based measurements to capture the geographical distribution diversity of trip origins and destinations. Specifically, we develop origin-entropy and destination-entropy based on taxi and ride-sharing trip records. The origin-entropy for a given zone accounts for all the trips that originate from this zone and calculates the level of geographical distribution diversity of these trips’ destinations. Likewise, the destination-entropy for a given zone considers all the trips that end in this zone and calculates the level of geographical distribution diversity of these trips’ origins. Furthermore, we have created an interactive geovisualization that enables researchers to delve into and juxtapose the spatial and temporal dynamics of origin and destination entropy, in conjunction with trip counts for both origins and destinations. Results indicate that entropy-based measurements effectively capture shifts in the diversity of trips’ geographical origins and destinations, reflecting changes in travel decisions due to major events like the COVID-19 pandemic. These measurements, alongside trip counts, offer a more comprehensive understanding of urban human flows.

Analysis of Relationships Between Co-Symmetric Dissimilarity Measures of Probability Distributions with Involutive Negations

Analysis of Relationships Between Co-Symmetric Dissimilarity Measures of Probability Distributions with Involutive Negations

Robust CO2 plume imaging by joint tomographic inversion using distributed pressure and temperature measurements

The scientific community has become increasingly interested in geological CO2 sequestration and CO2 enhanced oil recovery (EOR). The tracking of the CO2 propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO2 injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO2 plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO2 propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO2 geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO2 movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO2-EOR and/or associated geological storage projects.

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

Geographical and Taxonomic Notes on Scyphiphora &lt;I&gt;Hydrophyllacea gaertn&lt;/I&gt;. F. (Rubiaceae)

Scyphiphora hydrophyllacea, a disjunctly distributed species occurs only in two localities along the Gautami Godavari estuary in Andhra Pradesh for the entire Indian Peninsula. Details on its distribution, description and conservation measures taken are given in the paper.

Fisher and Bayes-Fisher information measures for finite mixture distributions

. In this work, we consider Fisher information and Bayes-Fisher information measures for the mixing parameter vector of a finite mixture density function and develop some associated results for this model. We provide several interesting connections between these measures and some known informational measures, such as chi-square divergence, Shannon entropy, Kullback-Leibler, Jeffreys, and Jensen-Shannon divergences. Finally, to demonstrate the usefulness of the Bayes-Fisher information measure, we apply it to a real example in image processing and present some numerical results. Our findings in this regard show that this information measure is an effective criteria for quantifying the similarity between two images.

Enhancing cardiovascular risk prediction through AI-enabled calcium-omics

Whole-heart coronary calcium Agatston score is a well-established predictor of major adverse cardiovascular events (MACE), but it does not account for individual calcification features related to the pathophysiology of the disease (e.g., multiple-vessel disease, spread of the disease along the vessel, stable calcifications, numbers of lesions, and density). We used novel, hand-crafted calcification features (calcium-omics); Cox time-to-event modeling; elastic net; and up and down synthetic sampling methods for imbalanced data, to assess MACE risk. We used 2457 CT calcium score (CTCS) images enriched for MACE events from our large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). Among calcium-omics features, numbers of calcifications, LAD mass, and diffusivity (a measure of spatial distribution) were especially important determinants of increased risk, with dense calcification (> 1000HU, stable calcifications) associated with reduced risk Our calcium-omics model with (training/testing, 80/20) gave C-index (80.5%/71.6%) and 2-year AUC (82.4%/74.8%). Although the C-index is notoriously impervious to model improvements, calcium-omics compared favorably to Agatston and gave a significant difference (P < 0.001). The calcium-omics model identified 73.5% of MACE cases in the high-risk group, a 13.2% improvement as compared to Agatston, suggesting that calcium-omics could be used to better identity candidates for intensive follow-up and therapies. The categorical net-reclassification index was NRI = 0.153. Our findings from this exploratory study suggest the utility of calcium-omics in improved risk prediction. These promising results will pave the way for more extensive, multi-institutional studies of calcium-omics.