Estimation Of Distribution Research Articles

Locally differentially private frequency distribution estimation with relative error optimization

Locally differentially private frequency distribution estimation with relative error optimization

Comparative study on excess distribution estimation in iid settings

. This study considers excess distribution estimation in iid settings. There are two ways for the estimation; the fitting to the generalized Pareto distribution and the fully non parametric estimation. The fitting estimator is justified by the approximation proven in the extreme value theory; however, the accuracy depends on how extremely large the target is. The non parametric estimator does not need an approximation and has the advantage of wide applicability. This study conducts both theoretical and numerical comparative study on excess distribution estimation. Asymptotic convergence rates of two estimators are obtained, and the mean integrated squared errors are numerically surveyed by simulation study. An illustrative example of Abisko rainfall amount is presented.

Predictive Modeling of Future Full-Ocean Depth SSPs Utilizing Hierarchical Long Short-Term Memory Neural Networks

The spatial-temporal distribution of underwater sound speed plays a critical role in determining the propagation mode of underwater acoustic signals. Therefore, rapid estimation and prediction of sound speed distribution are imperative for facilitating underwater positioning, navigation, and timing (PNT) services. While sound speed profile (SSP) inversion methods offer quicker response times compared to direct measurement methods, these methods often focus on constructing spatial sound velocity fields and heavily rely on sonar observation data, thus imposing stringent requirements on data sources. To delve into the temporal distribution pattern of sound speed and achieve SSP prediction without relying on sonar observation data, we introduce the hierarchical long short-term memory (H-LSTM) neural network for SSP prediction. Our method enables the estimation of sound speed distribution without the need for on-site data measurement, significantly enhancing time efficiency. Compared to other state-of-the-art approaches, the H-LSTM model achieves a root mean square error (RMSE) of less than 1 m/s in predicting monthly average sound speed distribution. Its prediction accuracy has improved several-fold over alternative methods, which validates the robust capability of our proposed model in predicting SSP.

Accuracy of Polarimetric Radar ZDR Estimates: Implications for the Quantitative Observation of Meteorological and Nonmeteorological Echoes

Abstract This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths.

Optimized rejection sampling for estimating facies probabilities from seismic data

Seismic inversion for facies has nonunique solutions. There are invariably many vertical facies arrays that are consistent with both a data trace and the prior information. Stochastic sampling algorithms set within a Bayesian framework can provide an estimate of the posterior probability distribution of facies arrays by finding the arrays with relatively high posterior probabilities for each data trace. Sample-by-sample facies probabilities can be estimated by measuring the proportions of each facies type at each sample location from the set of posterior facies arrays. To enable the estimation of probabilities of facies mixtures and to obtain high-quality images of facies probability curves, facies must be modeled at high resolution. The facies arrays, or vectors, on which the sampling algorithm operates, must also be long enough to allow for vertical coupling caused by the wavelet. This results in very large sample spaces. The posterior probability distribution is highly nonconvex, which, combined with the large sample space, severely challenges conventional stochastic sampling methods in obtaining convergence of the estimated posterior distribution. The posterior sets of vectors from conventional methods tend to be either correlated or have low predictabilities, resulting in biased or noisy facies probability estimates, respectively. However, accurate estimates of facies probabilities can be obtained from a relatively small number of posterior facies vectors (about 100), provided that they are uncorrelated and have high predictabilities. Full convergence of the posterior distribution is not required. A hybrid algorithm optimized rejection sampling can be designed specifically for the seismic facies probability inversion problem by combining independent sampling of the prior, which ensures posterior vectors are uncorrelated, with an optimization step to obtain high predictabilities. Tests on both real and synthetic data demonstrate better results than conventional rejection sampling and Markov chain Monte Carlo methods.

Parameters estimation of the alpha-stable distribution and applications to financial data

Parameters estimation of the alpha-stable distribution and applications to financial data

Particle Sizing and Surface Area Measurements: A Comparative Assessment of Commercial Air Permeability and Laser Light Diffraction Instruments

Six different commercial powders, finer than 45 μm, were used for examining the effects of particle characteristics on mean particle size and specific surface area. The measurements were carried out using the most commonly used air permeability- and laser light diffraction (scattering) techniques. As the air permeability method has been used as a benchmark for decades in the powder metallurgy (P/M) industry, the physical phenomena that govern the passage of gas through the powder bed under laminar flow conditions were also presented. The experimental data indicate that both methods give similar results for spherical powders. The advantage of laser light systems over gas permeameters is the ability to provide additional information on particle size distribution. Irregularly shaped powders should be analyzed by both techniques, relying on gas permeametry for surface area measurements and on laser light diffraction for the estimation of mean particle size and size distribution. Application of scanning electron microscopy as a complementary technique was found very helpful in the interpretation of data through visualization of individual particles.

Heat Transfer of Casson Nanofluid Flow Between Double Disks: Using Buongiorno Model

Purpose: Fluid thermal efficiency is crucial in major industrial sectors. Traditional fluids often lack the heat transmission needed for various operations. To address this, researchers introduced metallic and non-metallic nano additives, creating a new type of fluid called Nanofluid. This article explores the squeezing flow of Casson nanofluid between parallel disks, considering suction/injection, thermophysical impacts, thermal radiation, and chemical reaction. Methodology: The study uses the Buongiorno nanofluid theory to analyze thermophoresis and Brownian motion, reducing partial differential equations to ordinary differential equations. Numerical technique (shooting approach) is employed to solve these equations, and the results are presented graphically. Conclusions: The study reveals that axial velocity decreases as the Casson fluid parameter increases. Near the intersection point of the velocity field with varying magnitudes of the linear thermal convection parameter, radial velocity increases. It is observed that the thermal field intensifies with a higher linear thermal convection parameter. Additionally, elevated values of the thermal radiation parameter lead to an increase in temperature distribution estimates. Applications of Current Model: Casson fluid, classified as a non-Newtonian fluid, exhibits characteristics of both an elastic solid at low shear strain and a Newtonian fluid at high stress. It is characterized by infinite viscosity at zero shear rate and infinite viscosity at infinite shear rate. Examples of Casson fluids in everyday life include tomato juice, human blood, soup, and orange juice.

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

Long-Term Changes in the Permafrost Temperature and Surface Frost Number in Northeast China

The permafrost in Northeast China is experiencing rapid degradation due to the influence of climate change and human activities, profoundly impacting the local ecological environment and engineering construction. Understanding the spatiotemporal dynamics of long-term permafrost in this region is crucial; however, systematic research on this topic remains scarce. This study combines meteorological station data, MODIS land surface temperature (LST) datasets, and borehole locations to apply the surface frost number (SFn) model. This approach enables the simulation and estimation of the spatial distribution and changes in the area of the surface frost number without vegetation effects (SFnv) and permafrost temperature (PT) in Northeast China from 1971 to 2020. The area of the SFnv > 0.49 within the permafrost region decreased substantially from approximately 44.353 × 104 km2 to 19.909 × 104 km2 between 1971 and 2020, with a notable change in 1988. The area of permafrost calculated using PT < 0 was slightly smaller, declining from 39.388 × 104 km2 to 29.852 × 104 km2. There was also a significant increase in the area with PT ranging from −1 °C to 0 °C, indicating a decline in permafrost stability. Approximately 10.926 × 104 km2 of stable permafrost has been transformed into semi-stable and unstable permafrost. Moreover, from 1982 to 2020, the NDVI was negatively correlated with the area of stable permafrost and positively correlated with the area of transitional or unstable permafrost. Vegetation cover decreased as transitional or unstable permafrost degraded. These findings provide valuable information for permafrost research and engineering development in cold regions, as well as for future planning and adaptation strategies.

Micromechanical analysis of fiber-reinforced ceramic matrix composites by a geometrically nonlinear hierarchical quadrature element model

The stress fields of fiber-reinforced ceramic matrix composites (CMCs) under longitudinal tension are predicted using a non-linear hierarchical quadrature element method (HQEM) in this work. The HQEM combines the differential quadrature method (DQM) with the hierarchical finite element method (HFEM), which is a typical p-version finite element method (FEM). High-order interpolation functions of the HQEM allow accurate evaluation of stress distribution. Fiber, matrix, and interfaces of the CMCs are modeled as linear elastic materials that may have large displacements. The nonlinear HQEM estimation of CMCs stress distributions are validated by comparing with analytical results obtained using classical BHE shear-lag model. Stress distribution in three characteristic stages in the damage evolution process, namely, interface perfectly bonded, interface debonding, and fiber failure are investigated. It is shown that when fiber failure happens, geometric non-linearity must be considered to avoid excessive stress concentration. Furthermore, the stress distribution within three-dimensional cylindrical CMCs cell is studied, which sheds light on the future exploration of three-dimensional CMCs analyses.

EXCHANGE RATE VOLATILITY OF UKRAINE’S MAIN TRADING PARTNERS

Ukraine, as an open economy under conditions of Russian aggression, is highly vulnerable to the import of exogenous shocks. This is embodied in exchange rate volatility and the emergence of corresponding risks. Most studies are aimed at direct, explicit, or transactional risks. They reflect the negative impact of exchange rate fluctuations on economic entities' cash flows in foreign economic activities. However, changes in currency prices can also affect the subjects oriented to the local market and not carry out operations in foreign currencies by changing competitiveness. One speaks about the indirect, implicit, or economic currency risk in this case. The latter generally arises in the event of volatility in the exchange rates of currencies of importing countries or competitors. The study is devoted to the analysis of indirect currency risks, which reflect potential adaptations of competitiveness due to changes in exchange rates. For this purpose, the currencies of the countries that account for about 80% of Ukraine's trade turnover over the last 10 years, namely the euro, Japanese yen, British pound, Swiss franc, yuan, Indian rupee, Czech krona, Polish zloty, Hungarian forint, and Turkish lira were chosen. The article aims to assess the volatility of exchange rates of Ukraine's main trading partners as a stage of analyzing economic currency risks. The paper uses statistical methods for volatility estimation and distribution characteristics, namely standard deviation, skewness, and kurtosis. The obtained results show that some currencies have a high level of asymmetry and excess, indicating possible unpredictable rate changes. The calculated indicators' generalization allows identifying currencies with relatively high and low risk. The article complements the results of previous studies, as it covers both major reserve currencies and currencies of leading countries whose markets are developing globally and in Europe. Estimates of asymmetry and excess exchange rate distributions are essential for effective currency risk management.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection.

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

Multiple luminescence dating on heated materials at the nanzuo archaeological site, central Chinese Loess Plateau

Nanzuo is a key Neolithic archaeological site in the central Chinese Loess Plateau (CCLP). The site is important for understanding the origin and development of the Chinese civilization, yet, it lacks a robust chronological framework. The current work, for the first time, dates two different types of heated materials (pottery and burnt soil) from a profile near the main excavation of the Nanzuo archaeological site using various luminescence dating techniques. These techniques included single grain (SG) quartz OSL and K-feldspar post-infrared infrared-stimulated (pIRIR) dating for burnt soil samples and pottery, and pIRIR dating for pottery slices using the rock surface luminescence technique (RSL). The reliability of luminescence dating was investigated by comparing SG quartz, K-feldspar and pottery slice ages with radiocarbon (14C) ages obtained from the same cultural layer. The results show that the SG quartz OSL and K-feldspar pIRIR225 age estimates of the two coarse potteries are consistent with the 14C ages, however, the age estimates of the pottery slice (NZ22-CPS150) are significantly younger, which is primarily explained by underestimation in equivalent doses (De). Moreover, it is noted that while there is a considerable variation in the De of coarse pottery slices with depth, no significant correlation is observed between K- content and De. Heterogeneous K content was revealed by μ-XRF scanning on coarse pottery slices. Conversely, fine pottery displayed homogeneous K-content distribution and age estimates consistent with the 14C ages. Thus fine pottery slices are suggested to be more suitable for pIRIR dating relative to coarse pottery slices. The underestimation of De values in mixed minerals is observed as the primary reason for underestimated slice ages compared to SG K-feldspar, with grain size of K-feldspar and K content variation playing only a minor role. Therefore, by integrating the 14C ages with the luminescence ages of the pottery, it is inferred that human occupation of the Nanzuo site occurred around 5100 to 4500 years ago, which aligns with the 14C ages obtained from the main section of the Nanzuo site. The current work further concludes that the single-grain luminescence dating method is reliable for pottery. The study recommends to prioritize materials characterized by relatively uniform potassium (K) content for pIRIR dating using pottery slices.

Responses of rainfall partitioning to water conditions in Chinese forests

The process of rainfall partitioning, which consists of throughfall, stemflow, and canopy interception, represents the initial influence of forests on the hydrological cycle. However, the impacts of water conditions on rainfall partitioning amounts remain unexplored, and the overarching patterns of this partitioning at a large scale have not been well defined. In this study, we collected data from Chinese forests to elucidate the contributing factors on rainfall partitioning amounts, and utilized machine learning techniques to predict spatiotemporal trends in rainfall partitioning amounts. Rainfall partitioning ratios were also calculated spatially to assist a better explanation for the response mechanism of the rainfall partitioning amount. The results revealed that the rainfall partitioning amounts were influenced by a combination of meteorological and vegetation factors at the site scale. Rainfall partitioning amounts were mostly influenced by rainfall among meteorological factors, and they were mainly influenced by leaf area index among vegetation factors. Through the utilization of random forest modeling, we achieved robust spatial distribution estimations, yielding R2 values of 0.90 and 0.63 for throughfall and interception quantities, respectively. Regions characterized by high levels of throughfall and canopy interception (mm) were observed in areas characterized by wet conditions (PDSI ≥ 1) in the southern part of the forests. The amounts of throughfall and canopy interception mainly showed an increasing trend, although the trend was not significant (p > 0.05). In addition, the amounts of throughfall and canopy interception were positively affected by PDSI in the majority of Chinese forests at the spatial scale. This study indicated the significance of considering water conditions when examining rainfall partitioning patterns, thereby emphasizing their critical role in the study of the hydrological cycle.

Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems.

Statistical physics investigation of the docking process of fruity odorants on Machilis hrabei MhOR5: New microscopic interpretations

In this paper, the putative phenomenon of adsorption involved in the insect olfactory perception of four fruity odorants on Machilishrabei olfactory receptor MhOR5 was studied at a molecular scale. Thus, the experimental concentration–response curves of propyl acetate, butyl acetate, isobutyl acetate, and prenyl acetate on MhOR5 were adjusted using the monolayer model of ideal gas established using the powerful statistical physics approach, which was selected as the most appropriate model among the others. The fitted model parameters were applied to stereographic and energetic analyze the four olfactory systems. Indeed, the modeling results indicated that the tested fruity key food odorants (KFOs) were docked as 2- or 3-aggregated molecules on one MhOR5 binding site via a nonparallel position (i.e., multi-molecular adsorption mechanism). Energetic parameters were used to characterize the adsorption process involved in the studied systems via the determination of the molar adsorption energies (ranging from 12.91 to 17.52 kJ/mol). Hence, the values of the molar adsorption energies are positive and inferior to 40 kJ/mol; this means that the putative adsorption mechanism presented an exothermic character and a physical type. The docking simulation results proved that the adsorption of propyl acetate, butyl acetate, isobutyl acetate, and prenyl acetate on MhOR5 may occur via physical interactions (weak interactions) like alkyl, pi-alkyl, van der Waals, carbon hydrogen bond, and conventional hydrogen bond. The statistical physics theory may also be utilized to quantitatively characterize Machilishrabei MhOR5 activated by propyl acetate, butyl acetate, isobutyl acetate, and prenyl acetate odorants via the estimation of the different site size distributions (SSDs) and site energy distributions (SEDs).

Abstract B003: Preliminary machine learning integration of DNA methylation-based tumor immune microenvironment deconvolution with histopathological slides for bladder cancer prognostication

Abstract Background: Analyzing the cellular composition of the tumor microenvironment (TME) is crucial for bladder cancer (BCa) prognosis but is hampered by technical and financial limitations of immunofluorescence, flow cytometry and scRNASeq, primarily due to cell disaggregation challenges, antibody selection and cost/throughput constraints. A DNA methylation-based (DNAm) technique, HiTIMED, was introduced, enabling precise differentiation of 17 unique cell types within solid tumors– including those related to tumor growth, angiogenesis, and immune responses– enhancing our ability to investigate the complex interactions within TME tied to exposures, patient outcomes, and treatment efficacy. Automated morphological identification of cellular signatures on histopathological slides has been fraught with challenges. The potential to identify signatures at higher resolution, beyond immune cells, could improve disease management and application of Whole Slide Imaging (WSI) in clinical research. Our research explores direct estimation of HiTIMED-inferred cellular distributions from WSI and benefits of multimodal integration of WSI with HiTIMED proportions for BCa prognostication. Methods: BCa WSI and Illumina Methylation 450k DNAm data for BCa were obtained from TCGA (MIBC, n=456) and Dartmouth-Hitchcock (NMIBC, n=136), divided into training, validation, and testing sets. HiTIMED deconvolved DNAm into 17 cellular components. Survival, stage, and demographics were incorporated. Graph convolutional networks (GCNs) predicted HiTIMED-derived cellular proportions from WSI (WSI2HiTIMED). Logistic regression and Cox Proportional Hazards (CoxPH) were used to compare how standardized cellular proportions estimated by HiTIMED and WSI2HiTIMED each correlate with advanced T-stage (T>2) and survival with age and sex adjustment. We combined a GCN modeling WSI with a neural network using HiTIMED proportions to predict 10-year survival, adjusting for age and sex with CoxPH. This integrated approach was benchmarked against CoxPH models using only imaging or cellular data. Results: HiTIMED and WSI2HiTIMED demonstrated consistent associations with advanced T-stage, respectively: 1) endothelial cells (OR=2.70, p=0.0095; OR=2.24, p=0.033), 2) epithelial cells (OR=0.02, p=0.0005; OR=0.51, p=0.112), 3) lymphocytes (OR=2.04, p=0.0635; OR=2.13, p=0.041), 4) stromal cells (OR=4.30, p=0.001; OR=2.32, p=0.0235), and 5) tumor cells (OR=0.41, p=0.0165; OR=0.42, p=0.020). The prognostic value for survival based on endothelial cell estimates from HiTIMED and WSI2HiTIMED was comparable, with p-values of 0.006 and 0.009, respectively. The C-index for BCa survival prediction improved from 0.730 using WSI GCN-derived hazards to 0.766 with HiTIMED proportions. Conclusion: DNAm offers a cost-effective method for improving cell type estimation from WSI and provides complementary information for BCa analysis. Future efforts will focus on enhancing precision across additional cell types with larger cohorts and exploring synergy with pathologist annotations and spatial transcriptomics. Citation Format: Zarif L. Azher, Ze Zhang, Brock C. Christensen, Lucas A. Salas, Margaret R. Karagas, Louis J. Vaickus, Hideki Fuyura, Keluo Yao, Joshua J. Levy. Preliminary machine learning integration of DNA methylation-based tumor immune microenvironment deconvolution with histopathological slides for bladder cancer prognostication [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr B003.

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

Factors Affecting Soil Bulk Density: A Conceptual Model

Soil bulk density is an important aspect of soil properties, and it can affect agroecology as in diversity, synergies, efficiency, recycling, and resilience. Diversity as in soil low in bulk density allows greater penetration of plant roots allow better access of water and nutrients. The synergistic effect of adequate supply of soil moisture content in forming moderately low soil bulk density for deeper plant roots for better access to water and nutrients. Agroecology efficiency reduces the need of external inputs to reduce environmental impact related to the resulted excessive pollution and wastage. Ultimately, an improved soil bulk density may increase the overall agroecosystem resilience and output, thus, improving the efficiency. Recycling in agroecology involved mass recycling and heat circulation as in nutrient, water, and waste reduction. A high soil bulk density will impede the recycling process the soil. Resilience as in drought resistance is for soil not too loosely packed that cannot hold water or too compacted until flooding water occurs at the surface and too little water infiltrates the soil. In addition, soil bulk density can affect the estimation of soil moisture content and temperature distribution. The ability to understand the factors that influence changes in soil bulk density would improve the predictive ability of the current model. The current study conducts a review on factors affecting the soil bulk density that is divided into soil physical, chemical, biological, environmental, and management practices. A cumulative of more than 50 factors are discussed in the review on how it affects the change in soil bulk density. The current review illustrates the conceptual relation of these factors on soil bulk density. Also, the result of the current work can be used to support future endeavour by turning the conceptual relation into quantifiable predictive model.