Search Results (1,216)

Background: Routine full blood count (FBC) testing is part of the haematological workup in diabetes management. There is limited information regarding the contributions of individual haematological parameters to regression models for glycated haemoglobin (HbA1c), estimated whole-blood viscosity (eWBV) and the resulting blood viscosity complications. Importantly, because association and prediction represent distinct concepts, this study extends previous work with a focus on comparative and exploratory relationships. The objective was to compare FBC parameters between higher and lower HbA1c and eWBV groups and identify variables contributing to the Akaike Information Criterion (AIC)-based regression model among diabetics. Methods: This laboratory-based mixed quantitative study involved cross-sectional and regression analyses. Fifteen parameters were evaluated, including the following: red blood cell count (RBC) and indices (MCV, MCH, MCHC); platelet count and derived ratios (PRR, PWR, RPR); and white blood cell count (WBC) with lymphocyte ratios (MLR, NLR, PLR). HbA1c and eWBV data were used to create dichotomous subgroups for univariate comparison, followed by exploratory AIC-based model identification of variables. Results: HbA1c, RDW, MCV, and RPR, differed significantly between HbA1c groups (p < 0.1). Regression analysis identified RDW, MCV, RPR, MCH and RBC as contributors to the HbA1c model. For eWBV, five out of seven parameters (HCT, HB, RBC, WBC, and MLR) showed a significant association. Conclusions: These findings highlight haematological parameters with potential values for future predictive model development. Overall, the study supports the usefulness of selected FBC variables as adjuncts in diabetes monitoring with potential utility in understanding glycaemia control and blood viscosity-related complications. Full article

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (C_a) separately from JDLink telematics, aligning model structure with each target’s response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere CH570 chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R²_CV = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; C_a was retained for MLR (R²_CV = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF’s univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure. Full article

If concentrating coal is difficult or impossible using gravity methods (such as jigs, shaking spirals, dense-media drum, and cyclone), which are among the cheapest and simplest options, flotation becomes an alternative. This is due to the differences in surface chemistry properties between the relatively hydrophobic coal and the gangue minerals. On the other hand, flotation methods are far more complex than gravity methods and involve many more parameters that influence concentrate, such as coal particle size, amounts of reagents dosages (e.g., collectors, activators, depressants, and frothers), conditioning times, pulp mixing speeds, flotation times, and pH levels of the pulp medium. In flotation methods with so many variables, determining the combustible recovery (CR) and ash content (AC) of clean coal concentrate that can be obtained may require many experiments. To facilitate these challenging processes, understand the effects of parameters influencing concentration on the flotation method, and estimate the resulting clean coal recovery and ash content, it is necessary to utilize various statistical regression methods. In this study, the effects of six parameters on the flotation of a lignite coal sample with 40% ash content were used to estimate the CR and AC of coal concentrate using multivariate linear regression (MLR) and artificial neural network (ANN) models. As a result, the ANN model demonstrated superior estimate accuracy, with correlation coefficients of 0.988 and 0.963, compared with the MLR models (R² = 0.575 and 0.540) for estimating the ash content (AC, %) and combustible recovery (CR, %) of coal concentrate, respectively. Full article

Source apportionment and the elucidation of driving mechanisms are essential for targeted soil pollution management. This study investigated surface soils across six towns in southern Shimen County, northwestern Hunan Province, where 662 samples were collected to determine the concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn. Multivariate statistics and the APCS-MLR receptor model were integrated to quantify pollution sources, while three machine learning models (RF, XGBoost, and LightGBM) were applied to identify key drivers of the spatial enrichment of Cd. Results showed that Cd was significantly enriched, with a mean concentration of 0.43 mg/kg (3.41 times the provincial background value). The mean concentrations of As, Cr, Cu, Ni, Pb and Zn were 11.97 mg/kg, 81.01 mg/kg, 24.15 mg/kg, 49.25 mg/kg, 29.56 mg/kg and 76.77 mg/kg, respectively, and these PTEs remained at normal background levels. Significant inter-element correlations indicated common sources. Three primary sources were quantified—natural parent material (43.83%), mining activities (30.99%), and mixed sources of coal mining and agricultural inputs (7.84%), with 17.34% attributed to unidentified mixed sources. Natural sources dominated the geogenic enrichment of Cd, Cu, Ni, Pb, and Zn; mining activities governed the accumulation of As, Cr, Cu, and Pb; a mixed source of coal mining and agricultural practices contributed substantially to Cd enrichment. Machine learning identified PM10, topography, strata, and soil type as dominant drivers, with their total feature importance reaching 70.05%. Among these factors, natural factors and anthropogenic factors accounted for 44.23% and 55.77% of the total feature importance, in turn revealing coupled natural–anthropogenic controls. This study establishes an integrated framework linking source apportionment and driver identification, providing scientific insights for potentially toxic elements (PTEs) control in analogous mining–agricultural regions. Full article

This study presents the first comprehensive machine learning framework for predicting the performance of an air-breathing polymer electrolyte membrane fuel cell, based on high-fidelity multiphysics data and validated under realistic conditions. Using data generated from a validated multiphysics model, four machine learning models are trained: MLR, RFR, ANN, and SVR. The models aim to capture the effects of geometric, material, and operating parameters on cell performance to support the development of more efficient and sustainable clean energy systems. Evaluation with standard error metrics shows that MLR exhibits large deviations from actual values, highlighting the limitations of linear models and underscoring the need for more complex approaches. ANN and SVR provide high predictive accuracy and generalize well to unseen data, while RFR tends to overfit. Robustness analysis using white Gaussian noise and four-fold cross-validation further confirms the reliability of top-performing models. ANN and SVR models generate polarization curves 4000 and 40,000 times faster, respectively, than the multiphysics model, enabling real-time applications. Both models achieved excellent predictive performance, with R² values exceeding 0.999 under normal operating conditions and remaining above 0.98 even in the presence of noisy inputs. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

Background/Objectives: Childhood obesity is associated with chronic low-grade systemic inflammation. This exploratory cross-sectional study aimed to evaluate associations between a comprehensive panel of serum micronutrient levels and four systemic inflammatory indices—neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and monocyte-to-lymphocyte ratio (MLR)—in a pediatric cohort, with iron as the primary focus and zinc as a secondary exploratory analysis. Methods: We included 410 children (mean age 7.2 ± 3.8 years; 205 male) attending a tertiary pediatric clinic. Of these, 399 had complete BMI percentile data and were included in obesity-stratified analyses; patients were classified as having obesity (BMI ≥ 95th percentile; n = 56) or not having obesity (n = 343). The remaining 11 children lacked BMI percentile data and were included only in full-cohort analyses. Serum iron, ferritin, folate, zinc, vitamin D, vitamin B12, magnesium, and phosphorus were measured alongside complete blood count parameters. Spearman correlations and multivariable ordinary least squares regression were performed. Benjamini–Hochberg false discovery rate (FDR)-adjusted q-values were computed for all Spearman correlations; all remaining analyses are exploratory and all findings should be interpreted with caution. Results: All four inflammatory indices were significantly higher in children with obesity (SII: 487.1 vs. 332.1, p < 0.001; NLR: 1.30 vs. 1.03, p = 0.001). Low serum iron was more prevalent in the group with obesity (42.9% vs. 27.1%, p = 0.025). In multivariable regression, serum iron was significantly associated with NLR (β = −0.009, 95% CI [−0.012, −0.005], p < 0.001) and SII (β = −3.268, 95% CI [−4.404, −2.132], p < 0.001) after adjustment for age and BMI percentile. In an exploratory analysis restricted to children with obesity and complete data (n = 39), zinc was associated with SII (β = −11.912, 95% CI [−21.836, −1.988], p = 0.025); however, the overall model was non-significant (p = 0.067), zinc showed no association in the full cohort, and—given the small sample and absence of multiple comparison correction—this finding must be considered strictly hypothesis-generating. Conclusions: Systemic inflammatory burden is elevated in children with obesity. Iron shows consistent associations with inflammatory indices independent of age and BMI. Zinc shows a potentially relevant, exploratory association with inflammation, specifically in the subgroup with obesity, warranting replication in adequately powered prospective studies. These findings are consistent with a role for iron status assessment in the clinical evaluation of pediatric obesity and warrant further prospective investigation of zinc-related inflammatory associations. Full article

Cocoa accounts for 5.20% of Peru’s cultivated land and is growing at a rate of 8.80% per year; however, yields remain low due to deficiencies in crop management. Therefore, this study used a multiple linear regression (MLR) model to evaluate effects of an organic agroforestry system (OAF) and conventional monocultures (CMs) on soil and production in high-yielding T. cacao plantations in the Upper Huallaga Valley, Peru. Four plantations were evaluated: organic agroforestry (Pa) and conventional monoculture (LE, Sa, and Sh). Soil physicochemical variables and cocoa production were assessed. The MLR analysis revealed that in OAF systems with mature trees, there will be slight losses of clay and silt fractions; the latter can be offset by high planting density. The OAF system showed a significant positive effect on pH. However, the CM system showed significant decreases in pH, CEC, Ca²⁺, and Mg²⁺. The interaction between OAF and CM optimized production, increasing the weight of dry beans. Planting density is associated with improvements in pH and bases, as well as fruit index and the weight of dry beans. MLR modeling suggests that integrating OAF systems with conventional management practices in high-density plantations offers valuable alternatives for the design of local agricultural policies and producer support programs, by identifying the factors that link management systems to soil quality and sustainable cocoa productivity in this valley. Full article

This study comprehensively investigates the degradation performance of a Mn-doped Zn₂SnO₄ photocatalyst based on time-dependent UV-Vis absorption spectra. Before machine learning modelling, the effects of experimental parameters such as UV–Vis measurement wavelength, reaction time, and Mn doping ratio were statistically validated using One-Way Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) methods. To overcome the limitations of linear models in representing complex physical systems, an optimized Multi-Layer Perceptron (MLP) architecture was developed to capture the system’s nonlinear dynamics with high accuracy. To validate the model’s out-of-sample prediction capability and prevent data leakage potentially arising from spectral data correlation, the “Leave-One-Doping-Level-Out” (LODLO) cross-validation strategy was applied, during which performance metrics of R²$=0.8889$ and $MSE=0.00238$ were recorded. To make the neural network’s decision-making mechanism transparent, a dual-validation explainability framework comprising Shapley Additive Explanations (SHAP) and Permutation Feature Importance analyses was employed. By quantifying the relative contributions of the experimental parameters to the model predictions, this approach revealed that the UV–Vis measurement wavelength was the dominant predictive variable, followed by the Mn doping ratio and reaction time. This study presents a transparent methodology that offers both strong predictive capability and physically grounded data to shed light on the complex interactions in doped semiconductor photocatalysts. Full article

Background/Objectives: Robot-assisted surgery (RAS) has grown rapidly over the past few decades. To determine the effect of high stress levels on the performance of RAS, monitoring some parameters of surgeons is critical. This can be aided by the development of Artificial Intelligence (AI), which has exponentially grown in recent years. This study aims to predict the surgeon’s stress level based on ergonomic, kinematic and physiological parameters of the surgeon obtained in the immediately previous situation during RAS activities. Methods: Physiological data were recorded from surgeons during twenty-six surgical sessions involving twelve participants with different levels of experience and surgical specialties. After dataset generation, two preprocessing procedures (scaling and normalization) were applied to the recorded signals. The processed data were then partitioned into two subsets: 80% of the samples were used for model training and cross-validation, while the remaining 20% were reserved for testing. Six AI approaches were evaluated to build predictive models: multiple linear regression (MLR), a support vector machine (SVM), a multilayer perceptron (MLP), a convolutional neural network (CNN), random forest (RF), and a U-Net algorithm (UNET). These algorithms were trained using the training dataset and subsequently assessed on the independent test set. In addition, after each surgical session, surgeons completed a questionnaire reporting their perceived stress level, which was later compared with the stress estimates generated by the predictive models. Results: The results obtained showed that MLR and scaling pre-processing reached the highest R² coefficients and the lowest error for each studied parameter. The results of the surgeons’ surveys were highly correlated for microsurgery activities (R² = 0.7989) and for laparoscopy RAS (R² = 0.8381). Conclusions: The linear models proposed were correctly validated on cross-validation and the test dataset. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon’s health during RAS. Full article

The use of the Internet of Things (IoT) and wearable devices to enhance construction safety has recently attracted growing attention from the construction research community. In this paper, a system-level Structural Equation Model (SEM) is proposed to examine the relationships among perceived Safety System Value (SSV), Organizational Readiness (OR), and Adoption Barriers (AB). A survey of 567 construction professionals in Saudi Arabia was used to collect the data, which was analyzed using covariance-based SEM with Robust Maximum Likelihood (MLR) estimation. SSV was found to act as a perceptual antecedent of OR (β = 0.719). OR, in turn, was found to strongly affect AB (β = 0.712). The direct effect of SSV on AB was statistically significant (β = 0.191). Furthermore, the mediation analysis showed that approximately 73% of the total effect of SSV on AB is transmitted through OR (indirect β = 0.512, total β = 0.703). The model explained 51.6% of the variance in OR and 73.9% of the variance in AB. Data were collected through a structured questionnaire survey of 567 construction professionals in Saudi Arabia. This research contributes to the broader field of systems research by presenting a framework for the adoption of safety-related construction technologies as a systems phenomenon. The research has practical implications for building readiness-driven approaches for the effective integration of safety technologies in safety-critical construction environments. Full article

This paper presents a connected perception framework for blind-spot awareness by connecting an external camera system with a lightweight autonomous robot. The proposed system combines real-time object detection, localization, position prediction, and collision avoidance to enhance environmental perception beyond the limitations of onboard sensing. A YOLOv11-based detection model is employed for obstacle detection, achieving high accuracy with a mean average precision (mAP@0.5) of 0.991. For obstacle localization, the external camera system achieves centimeter-level accuracy, which is further improved using Multiple Linear Regression (MLR)-based correction, reducing the localization error by approximately 75.77%. In addition, position prediction models for both camera-based and autonomous vehicle systems demonstrate strong performance, with coefficients of determination ($R^2$) exceeding 0.98. The system also achieves effective collision avoidance, successfully stopping in all tested scenarios with response times ranging from 0.2 to 0.45 s. The integration of external and onboard perception enables effective blind-spot mitigation and improves situational awareness within simulated blind-spot corner scenarios representing real-world occlusion challenges. The results validate the system-level integration of these modules as a practical framework for addressing sensing limitations in autonomous robotic applications. Full article

Yield estimation in sugarcane systems remains a major challenge in tropical regions due to the reliance on destructive, labor-intensive, and spatially limited field measurements. Although remote sensing has been widely used for crop monitoring, its predictive performance is often constrained when spectral information is used in isolation. This study proposes a data fusion framework integrating multitemporal Sentinel-2 spectral bands with meteorological variables to improve sugarcane biomass prediction under tropical conditions. A commercial field was monitored throughout the 2022–2023 growing season, and machine learning models, including random forest (RF), support vector machine (SVM), and multiple linear regression (MLR), were developed to estimate stem, foliage, and total biomass. To reduce potential spatial data leakage caused by spatial autocorrelation within the field, model performance was evaluated using Spatial Block Cross-Validation. Results showed that integrating spectral and meteorological data consistently improved predictive performance compared to spectral-only and weather-only scenarios. Spectral bands exhibited stronger relationships with biomass than derived vegetation indices, while maximum temperature and solar radiation were identified as key drivers of biomass variability. RF combined with spectral–weather fusion achieved the highest predictive performance, reaching R² values up to 0.95, RMSE values as low as 5296.35, and rRMSE values close to 18% for stem biomass, consistently outperforming SVM and MLR. In contrast, spectral-only scenarios produced lower predictive accuracy and higher prediction errors across all biomass variables. This study provides one of the first field-scale implementations under humid tropical conditions in southeastern Mexico, where georeferenced yield data remain scarce. Full article

A multiscale contact model is developed for micro-textured gear interfaces incorporating Micro-Convex-Concave Asperity (MCCA) characteristics to elucidate the synergistic modulation between Interface Enriched Lubrication (IEL) performance and Anti-Scuffing Load-Bearing Capacity (ASLBC) of Micro-Textured Meshing Interfaces (MTMI) under transient Thermal Elastohydrodynamic Lubrication (TEHL) conditions. Homogenization theory is employed to quantify the effects of areal density and depth-to-diameter ratio on IEL characteristics. A time-resolved micro-elastohydrodynamic lubrication model, formulated through dimensionless discretization and adaptive mesh refinement, investigates the influences of autocorrelation length and MCCA amplitude on interfacial behavior. A correlation framework linking Micro-Element Texture (MET) geometric parameters to meshing ASLBC is established to identify optimal textures for simultaneous enhancement of IEL and ASLBC. Experimental observations demonstrate qualitative consistency with numerical predictions regarding the evolutionary trends of temperature fields and dynamic friction coefficients, providing preliminary physical validation for the proposed model. Univariate Sensitivity Analysis (USA) and Multiple Linear Regression (MLR) are further utilized to optimize microtexture parameters by elucidating the influences of MET sizes, area ratio, and configuration on meshing ASLBC and friction coefficients. Full article