Category: Statistics

Sci Rep. 2025 Sep 30;15(1):34086. doi: 10.1038/s41598-025-14641-5.

ABSTRACT

The accumulation of mineral deposits on industrial equipment surfaces poses a major concern in a variety of processes. Gypsum (CaSO₄·2H₂O) is one of the most widely produced minerals in both natural and industrial environments. Currently, intelligent white-box models can serve as a suitable alternative to time-consuming and high-priced experiments, enabling the identification of possible gypsum scaling issues in the chemical and petroleum industries. In this regard, the current study focused on the development of robust mathematical correlations to estimate the solubility of gypsum in aqueous electrolyte solutions. For this purpose, three rigorous techniques of Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH) were implemented on two distinct data banks, including 2288 experimental data-points taken from previously published literature. Solution temperature (T), solution molecular weight (MW), and molal concentrations of monovalent, divalent, and trivalent compounds (m_I, m_II, and m_III) were the input/independent variables employed in the first data bank, whereas solution temperature (T), solution molecular weight (MW), and solution ionic strength (I) were included in the second data bank. The performance and accuracy of correlations were evaluated using various statistical indicators such as Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²). Following multiple statistical and graphical analyses on the novel correlations’ outcomes, it was found that the correlation established by implementing the GMDH technique onto the first data bank (i.e., GMDH-1) performed significantly better than all other correlations, with MAE = 0.01095, RMSE = 0.01482, and R² = 0.8508. The correlations obtained by applying the GEP and GMDH techniques to the second data bank (i.e., GEP-2 and GMDH-2) also revealed a satisfactory level of performance. By comparing the new correlations developed in this study with models reported in previous studies, a reasonable level of agreement was found.

PMID:41028254 | DOI:10.1038/s41598-025-14641-5

Sci Rep. 2025 Sep 30;15(1):34043. doi: 10.1038/s41598-025-14273-9.

ABSTRACT

Accurate estimation of water acidity is essential for characterizing acid mine drainage (AMD) and designing effective remediation strategies. However, conventional approaches, including titration and empirical estimation methods based on iron speciation, often fail to account for site-specific geochemical complexity. This study introduces a high-accuracy, site-specific empirical model for predicting acidity in AMD-impacted waters, developed from field data collected at the Trimpancho mining complex in the Iberian Pyrite Belt (Spain). Using multiple linear regression (MLR), a robust predictive relationship was established based on Cu, Al, Mn, Zn, and pH, achieving a coefficient of determination (R²) of 99.2%. The model significantly outperforms the standard Hedin method, with a lower mean absolute percentage error (13% vs. 29%). Results also reveal strong spatial and seasonal hydrochemical variability, underscoring the limitations of generalized acidity models in such environments. This work demonstrates the applicability of site-calibrated multivariate models as practical tools for enhancing acidity prediction in complex AMD systems.

PMID:41028253 | DOI:10.1038/s41598-025-14273-9

Sci Rep. 2025 Sep 30;15(1):33791. doi: 10.1038/s41598-025-97562-7.

ABSTRACT

Mango is a fruit of great economic importance in India. India is the top mango-producing nation in the world, accounting for over half of global mango output. In order to determine the production capability of the insured orchards, a complete inventory is carried out in situ every three years. The inventory includes counting number of trees, grouping them into yield categories, and assessing damaged ones. Satellite Remote Sensing proves to be a vital tool for estimating ecological parameters such as population density, tree health, volume, biomass, and carbon sequestration rates. The significance of tree counting extends beyond orchard evaluations, playing a vital role in environmental protection, agricultural planning, and crop yield forecast. unfortunately, conventional tree counting methods often require very expensive feature engineering, which leads to more errors as well as lower overall optimization. In order to overcome these obstacles, deep learning-based methods have been used to count trees, exhibiting cutting-edge results in this crucial activity. This paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data. The proposed model, named Bi-directional Feature Pyramid Network (BiFPN)-YOLOv8m an improved version of YOLOv8, employs object detection to effectively separate, locate, and count mango trees with in orchards. A dataset of 1700 training and 300 testing images of mango orchards with trees of various ages is used to evaluate the various YOLOv8 variants, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, including YOLOv9, YOLOv10, and BiFPN-YOLOv8m, with a focus on computational efficiency, accuracy, and speed. Experimental findings show that, even under difficult circumstances, the proposed method continuously outperforms state-of-the-art techniques.

PMID:41028215 | DOI:10.1038/s41598-025-97562-7

Sci Rep. 2025 Sep 30;15(1):34069. doi: 10.1038/s41598-025-14204-8.

ABSTRACT

Improving the machinability of nickel-based superalloys remains a significant challenge in modern manufacturing, particularly for aerospace and high-performance engineering applications. Excessive friction and elevated temperatures during machining often result in rapid tool wear and reduced efficiency. This study investigates the potential of eco-friendly hybrid nanofluids-engineered by combining nanoparticles with complementary thermal and lubricating characteristics-as a sustainable solution to enhance machining performance. Specifically, the performance of three hybrid nanofluid combinations-hexagonal boron nitride/graphite (hBN/Gr), hBN/molybdenum disulfide (MoS₂), and Gr/MoS₂-was evaluated during the milling of Inconel 601 under varied cutting speeds (30-60 m/min) and feed rates (0.05-0.15 mm/rev). Key machining responses such as cutting force, surface roughness, tool wear, temperature, and tool life were analyzed. Among the tested combinations, the hBN/Gr nanofluid demonstrated superior performance, achieving reductions in cutting force (4.17%), surface roughness (21.05%), cutting temperature (8.57%), and tool wear (19.25%), along with an 11.17% improvement in tool life compared to Gr/MoS₂. These enhancements are attributed to the fluid’s optimal viscosity and exceptional tribological behavior at the tool-chip interface. The study offers a novel, environmentally responsible approach to machining Inconel 601, emphasizing the promising role of hybrid nanofluids-particularly hBN/Gr-as next-generation lubricants in sustainable manufacturing.

PMID:41028203 | DOI:10.1038/s41598-025-14204-8

Sci Rep. 2025 Sep 30;15(1):34095. doi: 10.1038/s41598-025-14696-4.

ABSTRACT

The low-frequency variability of the mid-latitude atmosphere involves complex nonlinear and chaotic dynamical processes posing predictability challenges. It is characterized by sporadically recurring, often long-lived patterns of atmospheric circulation of hemispheric scale known as weather regimes. The evolution of these circulation regimes in addition to their link to large-scale teleconnections can help to extend the limits of atmospheric predictability. They also play a key role in sub- and inter-seasonal weather forecasting. Their identification and modeling remains an issue, however, due to their intricacy, including a clear conceptual picture. In recent years, the concept of metastability has been developed to explain regimes formation. This suggests an interpretation of circulation regimes as communities of states in the neighborhood of which the atmospheric system remains abnormally longer than typical baroclinic timescales. Here we develop a new and effective method to identify such communities by constructing and analyzing an operator of the system’s evolution via hidden Markov model (HMM). The method makes use of graph theory and is based on probabilistic approach to partition the HMM transition matrix into weakly interacting blocks – communities of hidden states – associated with regimes. The approach involves nonlinear kernel principal component mapping to consistently embed the system state space for HMM building. Application to northern winter hemisphere using geopotential heights from reanalysis yields four persistent and recurrent circulation regimes. Statistical and dynamical characteristics of these circulation regimes and surface impacts are discussed. In particular, unexpected high correlations are obtained with EL-Niño Southern Oscillation and Pacific decadal oscillation with lead times of up to one year.

PMID:41028162 | DOI:10.1038/s41598-025-14696-4

Sci Rep. 2025 Sep 30;15(1):34061. doi: 10.1038/s41598-025-14465-3.

ABSTRACT

This cross-sectional study aimed to investigate the correlation between the neutrophil-lymphocyte ratio (NLR) and all-cause mortality and cardiovascular mortality in osteoarthritis (OA) patients. We involved 3549 adults with OA from the National Health and Nutrition Examination Survey (NHANES) database (1999-2018). The optimal NLR threshold (2.53) was determined using maximally selected rank statistics. Kaplan-Meier (KM), weighted Cox regression, and restricted cubic spline (RCS) analyses were employed to assess the relationship between the NLR and mortality outcomes, with subgroup and sensitivity analyses evaluating the stability of the observed associations. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to evaluate the NLR prognostic accuracy for mortality across time points. During the 91-month median follow-up period, 843 patients died (256 from cardiovascular disease). Elevated NLR (≥ 2.53) was associated with increased risks of all-cause mortality (HR = 1.82) and cardiovascular mortality (HR = 2.50). Nonlinear correlations of the NLR with mortality outcomes were observed. ROC analysis demonstrated superior NLR predictive capability for all-cause and cardiovascular mortality compared to individual blood cell types. Elevated NLR is independently associated with increased risks of all-cause mortality and cardiovascular mortality in OA patients.

PMID:41028161 | DOI:10.1038/s41598-025-14465-3

Sci Rep. 2025 Sep 30;15(1):34059. doi: 10.1038/s41598-025-14234-2.

ABSTRACT

The integration of machine learning (ML) algorithms with statistical analysis and user-friendly interfaces has become crucial for democratizing advanced analytics across various domains, particularly in digital agriculture. This paper presents ImMLPro (Intelligent Machine Learning Professional), a comprehensive Shiny-based web application that seamlessly integrates R programming, machine learning algorithms, and statistical analysis for continuous variable prediction. The platform addresses the growing need for accessible ML tools that eliminate coding barriers while maintaining analytical rigor. ImMLPro incorporates four state-of-the-art algorithms: Random Forest, XGBoost, Support Vector Machines (SVM), and Neural Networks, providing comparative analysis, hyperparameter optimization, and comprehensive visualization capabilities. The application’s architecture facilitates real-time model training, performance evaluation, and result interpretation through interactive dashboards. Designed with digital agriculture applications in mind but applicable across domains requiring continuous variable prediction, ImMLPro represents a significant advancement in making complex ML algorithms accessible to nonprogramming experts. The platform’s integration of R’s statistical computing power with modern web technologies demonstrates the potential for bridging the gap between sophisticated analytical methods and practical implementation in agricultural decision-making and beyond.

PMID:41028157 | DOI:10.1038/s41598-025-14234-2

Sci Rep. 2025 Sep 30;15(1):34044. doi: 10.1038/s41598-025-13004-4.

ABSTRACT

As a total ankle replacement (TAR) prosthesis has been developed and improved in terms of design and surgical technique, it could be expected to lead to a successful functional outcome in the ankle joint. However, several complications of the TAR procedure may be often caused by an incomplete understanding of the abnormal biomechanics of the ankle joint and the prosthesis design of the TAR. This study was performed to suggest a novel approach to determine the TAR prosthesis size by using an orthopedic digital templating software based on a comparison between X-ray and CT images. This study was examined in a novel approach to determine the prosthesis size by using an orthopedic digital templating software (Orthoview™, Florida, USA) based on the comparison between X-ray and CT images. A total 6 types of clinical foot and ankle images were obtained from x-ray and CT of 55 subjects in the coronal and sagittal plane. The x-ray images magnified as 100% and 115% based on the CT images. All subjects were diagnosed to the ankle osteoarthritis with stage 2-4 according to Takakura’s ankle OA classification. To predict the appropriate component sizes of the TAR prosthesis, the same TAR prosthesis (HINTEGRA, Newdeal, France) was chosen, and the tibial and the the talar component sizes were selected until by adapting to the osteotomized range of the tibia and talus. The unskilled surgeons predicted the sizes of the TAR components before procedure by using the orthopedic digital templating software. These predicted sizes were then compared with the selected sizes by the specialist surgeon during the procedure. The Cohen’s Kappa correlation coefficient was applied to statistically analyze the agreement between the predicted and selected sizes of the TAR components for unskilled and specialist surgeons, respectively. On the CT images, the average agreement rate was relatively higher than on the x-ray images at over 77%. Especially, highest agreement rate was shown at the tibial component in the coronal plane with almost 80%, followed by over 75% in the sagittal plane. In the talar part, the agreement rate was shown to be over 76% in the coronal and sagittal plane, respectively. Overall, the predicted size from the CT image was more consistent with the size selected by the specialist surgeon than the X-ray image. In conclusion, the application of the orthopedic digital templating software based on CT images may expect to provide more complete and detailed visualization to predict the appropriate size of the TAR components than conventional X-ray images which would be limited by relatively lower sensitivity and specificity as well as overlapping the adjacent bones.

PMID:41028141 | DOI:10.1038/s41598-025-13004-4

Sci Rep. 2025 Sep 30;15(1):33984. doi: 10.1038/s41598-025-12134-z.

ABSTRACT

Healthcare governance is essential for ensuring quality service delivery, accountability, and transparency within health systems. However, challenges such as resource limitations, political interference, and inexperienced management hinder effective governance in many regions, including the South Wollo Zone of Northeast Ethiopia. Therefore, this study assessed the healthcare governance practices and their determinants among public hospital managers in South Wollo Zone, Northeast Ethiopia, in 2024. A facility-based cross-sectional study using a mixed-method approach was conducted. Quantitative data were collected from 182 randomly selected hospital managers using simple random sampling. For the qualitative component, purposive sampling was employed to select 10 key informants for in-depth interviews. In this study, good governance was defined as the ability of hospital managers to uphold accountability, transparency, participation, responsiveness, and rule of law in their managerial roles, based on principles adapted from the World Health Organization’s (WHO) health system governance framework. The WHO framework and the UNDP governance principles were used as reference frameworks to guide measurement and analysis of good governance among healthcare managers. For the quantitative analysis, good governance was treated as a single dependent variable, classified dichotomously as good governance or poor governance based on a predefined scoring system. Variables with a p-value < 0.2 in the bivariable logistic regression were considered candidates for multivariable logistic regression, and those with a p-value < 0.05 were considered statistically significant. Quantitative analysis revealed that only 41.20% of the managers demonstrated good healthcare governance practices. Key factors significantly associated with good governance included having governance-related training, access to structured feedback systems, opportunities for peer learning, and freedom from political interference. The qualitative findings supported these results, emphasizing the role of training, feedback, collaboration, and managerial autonomy in strengthening governance practices. To improve governance in public hospitals, strengthening managerial training programs, establishing regular feedback mechanisms, promoting peer-learning opportunities, and minimizing political interference are recommended.

PMID:41028140 | DOI:10.1038/s41598-025-12134-z

Sci Rep. 2025 Sep 30;15(1):34103. doi: 10.1038/s41598-025-19820-y.

ABSTRACT

Soil saturated hydraulic conductivity (Ks) is a critical parameter for modeling water and solute transport in soils. Conventional laboratory measurements of Ks are labor-intensive, costly, and susceptible to measurement errors, underscoring the need for more reliable estimation techniques. This study systematically compares the performance of Ordinary Kriging (OK), Ordinary Co-Kriging (OCK), and Response Surface Methodology (RSM) for Ks estimation, thereby integrating geostatistical and statistical optimization frameworks. Soil samples were collected from 135 locations within the surface layer (0-30 cm), and Ks along with key soil physicochemical properties were determined. In the geostatistical domain, OK based on a spherical semivariogram (R² = 0.81; nugget/sill = 10.19%) yielded moderate predictive ability (R² = 0.70, RMSE = 3.62 mm day^-1, MAE = 10.02 mm day^-1), whereas OCK employing an exponential cross-semivariogram (R² = 0.91; nugget/sill = 0.45%) substantially improved accuracy (R² = 0.85, RMSE = 3.21 mm day^-1, MAE = 9.43 mm day^-1). By contrast, RSM achieved the highest predictive performance, with a quadratic model producing R² = 0.94 and Adeq Precision = 49.2. Optimization within the experimental range indicated a maximum Ks of 137.18 mm day^-1 at 8.9% clay and 86% sand. Collectively, these findings demonstrate that while OK and OCK provide valuable insights into the spatial dependence of Ks, RSM offers superior predictive accuracy and practical applicability for optimizing soil hydraulic functions in water resources and agricultural management.

PMID:41028130 | DOI:10.1038/s41598-025-19820-y