Search Results (43)

In this study, we analyze the production process capability of sandpaper manufacturing, with an emphasis on material removal from the finished product and the identification of manufacturing variables that most influence grinding performance and final quality. To this end, the CRISP-DM methodology was applied along with linear regression, stepwise analysis, and principal component analysis (PCA) to a sample of 62 operational variables collected between 2024 and 2025. These variables were reduced to 12 critical dimensions that explain 80% of the process variability. This study highlights the interaction between chemical properties of the adhesive system (gel time, pH, and formaldehyde concentration) and fine mechanical adjustments (blade and roller clearance), showing how these variables jointly affect sanding performance. By integrating these factors into a multivariate framework, PCA allows for the identification of latent relationships, reduces process complexity, and establishes a statistical basis for standardization and continuous improvement, with the aim of supporting the transfer of technical knowledge in industrial manufacturing environments. The proposed framework is intended to support technical knowledge transfer in industrial manufacturing environments. Full article

(1) Background: Psoriasis is increasingly recognized as a systemic inflammatory disease associated with metabolic comorbidities. However, the hierarchical relationship between inflammatory activation and insulin resistance in driving metabolic syndrome (MetS) remains incompletely defined. This study aimed to characterize the integrated inflammatory–metabolic architecture of psoriasis using multivariate and latent domain modeling. (2) Methods: In this cross-sectional hospital-based study (2020–2022), 235 adult patients with psoriasis were evaluated. Systemic inflammatory markers (NLR, SII, CRP, ESR) and composite metabolic indices (TyG, AIP, METS-IR) were assessed. Correlation analysis, multivariable linear and logistic regression, interaction modeling, and principal component analysis (PCA) were performed to examine independent associations and underlying domain structure. (3) Results: Inflammatory and metabolic markers showed modest but significant correlations. In multivariable logistic regression, the TyG index was the strongest independent predictor of MetS (OR = 5.15, p < 0.001), whereas inflammatory markers did not retain independent significance. An interaction between adiposity and insulin resistance further improved model discrimination (AUC = 0.830). PCA identified two distinct latent domains explaining 69.9% of total variance: an immune–inflammatory domain (NLR, SII, ESR, CRP) and a metabolic–insulin resistance domain (TyG, AIP, METS-IR). Only the metabolic domain independently discriminated MetS. (4) Conclusions: Psoriasis exhibits a multidimensional systemic architecture characterized by partially independent inflammatory and metabolic domains. Although systemic inflammation and metabolic dysfunction coexist, insulin-resistance-related indices were more strongly associated with metabolic syndrome in this cohort. Full article

In this article, we study scalar-on-function regression with functional covariates observed through replicated measurements subject to measurement error. Treating replicated curves as surrogates of an underlying latent process, the proposed framework resolves the identifiability issues commonly encountered in functional measurement error models. Through functional principal component analysis, the model is represented as a finite-dimensional hierarchical linear measurement error model. Parameter estimation is carried out using an expectation-maximization algorithm, and alternative correction strategies based on adjusted regression calibration and simulation extrapolation are also considered for comparison. Simulation studies demonstrate the advantages of explicitly accounting for measurement error in terms of bias reduction and estimation stability. An application to soybean yield prediction in Illinois, using meteorological variables contaminated by measurement error, illustrates the practical value of the proposed approach. Full article

Estimation of the postmortem interval (PMI) remains a major challenge in forensic science. We used attenuated total reflection (ATR)–Fourier-transform infrared (FTIR) spectroscopy combined with chemometric modeling for PMI prediction using vitreous humor samples from 20 forensic cases with known PMI (24.8–97.6 h) and 10 with unknown PMI. The intensities of vibrational bands commonly associated with PMI were analyzed, and several peaks in the carbohydrate/phosphate region showed significant correlations with PMI. Principal component analysis revealed time-dependent spectral evolution, with PC1 (48.1%) associated mainly with carbohydrate/phosphate variations and PC2 (37.6%) with protein structural changes. Partial least squares regression with two latent variables achieved a cross-validated RMSE of 15.8 h (R² = 0.53) on all 20 known samples. Variable importance analysis identified glycoprotein degradation (1190 cm⁻¹) and phospholipid breakdown (736 cm⁻¹) as the dominant predictors, with traditional carbohydrate bands playing a secondary role. Predictions for unknown samples ranged from 27.1 to 80.1 h, with five of ten falling within the 90% prediction interval (±20 h) of the available estimates. This study presents a promising PMI estimation model that performed well on unseen samples, even if the sample size represents a methodological limitation that will be addressed in future investigations through larger, more diverse datasets. Full article

This study proposes an explainable machine learning framework for estimating the total project cost (TPC) of AI training-data construction, where cost information is difficult to structure due to heterogeneous workflows and quality requirements. Using 386 public AI training-data projects conducted between 2020 and 2022, we derive 24 numerical predictors from standardized final reports and construct three input tracks: a baseline feature set, a principal component analysis (PCA)-enhanced set, and a factor analysis (FA)–enhanced set capturing latent cost structures. Four regression models (Ridge, Random Forest, XGBoost, and LightGBM) are evaluated using nested cross-validation. XGBoost achieves the best overall performance across all three tracks (Baseline, PCA-enhanced, and FA-enhanced). Among them, PCA-enhanced XGBoost attains the highest predictive accuracy (R² = 0.868; RMSE = 1084.9; MAE = 746.9; MAPE = 0.358; pooled out-of-fold), while Baseline XGBoost yields the lowest MAE (731.4; R² = 0.863). To support transparent decision-making, Shapley Additive exPlanations (SHAP)-based attribution and scenario-based sensitivity analyses are conducted. Results show that project scale and process-level unit costs are dominant cost-drivers, while cloud usage, expert participation, and de-identification requirements exhibit secondary effects. The proposed framework provides an interpretable, data-driven approach to cost information management and decision support for data-intensive AI projects. Full article

Ischemic stroke remains a major cause of mortality and long-term disability worldwide, and improved strategies for identifying individuals at elevated vascular risk are needed. Serum autoantibodies have emerged as potential biomarkers reflecting vascular injury and immune activation; however, their integrative biological significance and incremental predictive value beyond established clinical risk factors remain unclear. We analyzed 833 participants, including patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA) and healthy controls. Serum levels of anti-PDCD11 antibody (Ab), anti-DNAJC2 antibody, and anti-PAI-1 (SERPINE1) antibody were quantified, and multivariable logistic regression and machine-learning (ML) models (logistic regression and random forest) were constructed using clinical variables with and without antibody markers. Model performance was evaluated using cross-validation, bootstrap-derived confidence intervals, calibration metrics, and reclassification indices. Model interpretability analyses, principal component analysis (PCA), unsupervised clustering, and propensity score matching were performed to explore latent biological structures. Clinical-only models demonstrated excellent discrimination (bootstrap Area Under the Curve (AUC) 0.917 for random forest and 0.919 for logistic regression). The addition of antibody markers yielded similar performance (AUC 0.913 and 0.923, respectively) without evidence of meaningful improvement in reclassification. However, SHapley Additive exPlanations (SHAP) analysis identified antibody markers as influential contributors following major clinical risk factors. PCA revealed a dominant antibody component explaining approximately 79% of the variance, which remained independently associated with stroke after age adjustment. Unsupervised clustering further identified a high-risk subgroup characterized by consistently elevated antibody levels. These findings support the presence of a latent antibody axis associated with vascular vulnerability. Although antibody markers did not substantially enhance global predictive performance, they captured integrated biological signals reflecting cumulative vascular and immunological stress. Autoantibody profiling may complement conventional risk assessment by improving biological characterization of stroke susceptibility. Prospective validation in independent cohorts is required prior to clinical implementation. Full article

Introduction: Muscle function is influenced by hydroelectrolytic mechanisms that regulate cellular volume beyond isolated plasma electrolyte concentrations. However, the role of integrated hydration and electrolyte regulation profiles in muscle function among older adults remains insufficiently understood. Objective: To identify which physiological domains of hydroelectrolytic regulation are most strongly associated with muscle strength and functional performance in community-dwelling older adults. Methods: A cross-sectional study was conducted in 96 community-dwelling individuals aged ≥ 70 years. Markers of cellular hydration and membrane integrity were assessed using bioelectrical impedance analysis, together with first-morning fasting plasma and urinary sodium and chloride concentrations. Principal component analysis (PCA) was applied as a data-driven approach to identify latent domains of coordinated hydroelectrolytic regulation. Associations between component scores and handgrip strength and Timed Up and Go (TUG) were examined using two sequential multivariable regression models: Model 1 adjusted for sex and fat-free mass index (FFMI); Model 2 additionally adjusted for age, hypertension, and diuretic use. Results: Three principal components were retained, explaining 77.5% of total variance: PC1 (renal–cellular domain), PC2 (plasma electrolyte domain), and PC3 (cellular volume domain). For handgrip strength, Model 1 showed significant associations for PC3 (β = 0.152; p = 0.025) and PC1 (β = 0.180; p = 0.050). In Model 2, only PC3 remained independently associated (β = 0.146; p = 0.036). For TUG, Model 1 showed associations for PC1 (β = −0.262; p = 0.049) and PC3 (β = −0.238; p = 0.015). In Model 2, PC1 (β = −0.308; p = 0.019) and PC2 (β = −0.190; p = 0.046) remained independently associated, whereas PC3 was not. Conclusions: Maximal force production appears primarily associated with cellular volume regulation, whereas functional performance reflects broader multi-compartmental hydroelectrolytic integration involving renal and plasma domains. These findings suggest that multidimensional hydration profiling may complement isolated biochemical markers in the functional assessment of older adults, warranting validation in longitudinal studies. Full article

The July 2024 uprising in Bangladesh highlighted the growing importance of social media in transforming widespread grievances into coordinated civic mobilisation, yet empirical understanding of how grievances, access to platforms, networked connectivity, and global consciousness jointly shape mobilisation remains limited, particularly in Global South contexts. This study addresses this gap by systematically examining the mechanisms through which these factors interact to influence digital mobilisation during the Bangladeshi uprising. Using survey data collected from 260 university students who constituted a central mobilisation cohort, the study operationalises grievances, access, connectivity, global consciousness, and digital mobilisation as composite constructs and analyses them through an integrated quantitative framework. Reliability analysis confirms internal consistency of the constructs, while principal component analysis validates their latent structure. Standardised regression modelling demonstrates that connectivity within online communities and global consciousness are the most influential predictors of mobilisation, together explaining approximately 45% of the variance in mobilisation outcomes, whereas access to platforms and grievances play smaller enabling roles. Unsupervised clustering further reveals two graded mobilisation profiles rather than a sharply polarised divide. Substantively, a one standard deviation increase in connectivity and global consciousness is associated with an average increase of approximately 0.6 on a 5-point mobilisation scale, corresponding to a marked shift from passive to active participation. By quantifying how network embeddedness and transnational framing amplify mobilisation, this study advances theories of connective action and provides empirically grounded insight into the dynamics of digitally mediated collective action in contemporary protest movements. Full article

This study investigated fresh pork and pork subjected to repeated freeze–thaw cycles. The effects of freeze–thaw treatments on water status, WHC, and quality attributes of pork were systematically analyzed, and a nondestructive prediction method for WHC based on magnetic resonance imaging (MRI) was developed. The results showed that increasing freeze–thaw cycles significantly reduced moisture content and increased drip loss, indicating a continuous deterioration of overall WHC. Texture parameters and shear force values decreased markedly, suggesting that muscle structure was progressively damaged by ice crystal formation and recrystallization. T₂-weighted MRI pseudo-color scans clearly reflected changes in internal water distribution, with high-signal regions gradually decreasing as freeze–thaw cycles increased, which was consistent with the experimentally measured trends in moisture content and WHC. Based on MRI features, principal component regression (PCR) and partial least squares regression (PLSR) models were established to predict pork WHC. The PCR model extracted 16 principal components (cumulative contribution rate of 96.394%), with calibration set results of Rc² = 0.8825 and RMSEC = 1.7959, and validation set results of Rp² = 0.8856 and RMSEP = 2.0284. The optimal number of latent variables for the PLSR model was six, yielding calibration set results of Rc² = 0.9634 and RMSEC = 1.0026, and validation set results of Rp² = 0.9656 and RMSEP = 1.1119, with all residuals less than 1. Overall, the combination of MRI and chemometric methods, particularly the PLSR model, enables rapid, nondestructive, and accurate prediction of pork WHC, providing a useful tool for quality evaluation under repeated freeze–thaw conditions and for quality control in pork processing, storage, and cold-chain management. Full article

Background and Objectives: BK virus (BKPyV) is a common latent pathogen in humans, but it becomes particularly insidious in kidney transplant recipients, where reactivation may contribute to allograft loss. The immune mechanisms controlling BKPyV latency in immunocompromised hosts remain incompletely understood. We assume the urinary immune proteome reflects local immune response in the kidney and the urinary tract. Thus, this study aimed to determine whether the presence of BKPyV alters the urinary immune-related proteomic profile of kidney transplant recipients and shifts it away to that observed in healthy individuals. Materials and Methods: 137 urine samples were collected from kidney recipients, both BKPyV-positive and BKPyV-negative, patients with stage 5 chronic kidney disease, and healthy controls. Targeted proteomic analysis was performed using the proximity extension assay, followed by heatmapping, principal component analysis, random forest, and linear regression modeling. Results: The urinary proteome of BKPyV-positive recipients remained more distinct from healthy controls than that of BKPyV-negative ones. Among the 33 proteins detected across all samples, 17 showed significant intergroup differences, with KLRD1 (CD94) uniquely upregulated in all transplant recipients, but downregulated in BKPyV-positive samples. Conclusions: We conclude that the presence of BKPyV in the urinary tract of kidney recipients notably interplays with the local immune response even in the absence of clinical disease. Full article

Accurate mapping of soil organic matter (SOM) is essential for soil management. Remote sensing (RS) provides broad spatial coverage, while visible and near-infrared (VisNIR) laboratory spectroscopy enables accurate point-scale SOM prediction. Conventional data methods for fusing RS and VisNIR data often rely on principal components (PCs) extracted from VisNIR data that have an indirect relationship to SOM and employ ordinary kriging (OK) for their spatialization, resulting in limited accuracy. This study introduces an enhanced fusion method using partial least squares regression (PLSR) to extract supervised latent variables (LVs) related to SOM and residual kriging (RK) for spatialization. Two fusion strategies (four variants)—RS + first i PCs/LVs and RS + ith PC/LV—were evaluated in the contrasting agricultural regions of Da’an City (n = 100) and Fengqiu County (n = 117), China. Laboratory-measured soil spectra (400–2400 nm) were integrated with many temporal combinations of Landsat 8 imagery. The results demonstrate that LVs exhibit stronger correlations with SOM than PCs. For example, in Da’an, LV6 (r = 0.36) substantially outperformed PC6 (r = 0.02), while in Fengqiu, LV3 (r = 0.40) outperformed PC3 (r = −0.05). RK also dramatically improved their spatialization over OK, as demonstrated in Da’an where the R² for LV2 increased from 0.21 to 0.50. More importantly, in SOM prediction performance, all four fusion variants improved accuracy over RS alone, and the LV-based fusion achieved superior results. In terms of mean performance, RS + first i LVs achieved the highest R² (0.39), lowest RMSE (5.76 g/kg), and minimal variability (SD of R² = 0.06; SD of RMSE = 0.28 g/kg) in Da’an, outperforming the PC-based fusion (R² = 0.37, SD = 0.09; RMSE = 5.85 g/kg, SD = 0.42 g/kg). In Fengqiu, two fusion strategies demonstrated comparable performance. Regarding peak performance, the PC-based fusion in Da’an achieved a maximum R² of 0.57 (RMSE = 4.82 g/kg), while the LV-based fusion delivered comparable results (R² = 0.55, RMSE = 4.94 g/kg); both surpassed the RS-only method (R² = 0.54 and RMSE = 4.98 g/kg). In Fengqiu, however, the LV-based fusion demonstrated superiority, reaching the highest R² of 0.40, compared to 0.38 for the PC-based fusion and 0.35 for RS alone. Furthermore, across different temporal scenarios, the LV-based fusion also exhibited greater stability, particularly in Da’an, where the RS + first i LVs method yielded the lowest standard deviation in R² (0.06 vs. 0.09 for PC-based fusion). In summary, integrating LV-derived variables with RS data enhances the accuracy and temporal stability of SOM predictions, making it a preferable approach for practical SOM mapping. Full article

Complex odor emissions from wastewater treatment plants consist of multiple volatile compounds that exhibit heterogeneous temporal dynamics and low linear correlations, making accurate prediction and interpretation difficult when analyzed on a single-compound basis. This study investigates whether clustering can serve not only as an exploratory tool but as an essential preprocessing step to enhance machine-learning performance in multi-odor prediction systems. A total of 22 designated odorants were continuously monitored, and their pairwise dependencies were evaluated using Pearson correlation and mutual information. Data-driven clustering was performed through K-means, hierarchical linkage, and principal-component–based latent grouping, and the resulting structures were quantitatively compared with functional-group-based chemical classifications using the consistency ratio and Jaccard similarity index. Cluster validity was further examined using the Silhouette Coefficient, Davies–Bouldin Index, and Calinski–Harabasz Index. The predictive contribution of clustering was verified by training XGBoost regression models on both raw and cluster-structured datasets. The clustered dataset yielded higher predictive accuracy, with increased R² and reduced MAE and RMSE across most odorants. SHAP analysis further confirmed that clustering improved model interpretability by stabilizing feature contributions and reducing noise-driven importance shifts. The findings demonstrate that clustering is not a supplementary diagnostic tool, but a prerequisite for building reliable, high-performance machine-learning models in complex odor systems. This integrative framework offers a methodological foundation for multi-odor forecasting, source tracking, and next-generation odor management platforms. Full article

This study establishes the Construction Embodied Energy and Emissions Index (CEEEI) to assess the comprehensive environmental impacts of construction work in 148 countries from 2000 to 2023. The index combines data on material, energy, and carbon intensity from four international open databases. The three latent components derived from Principal Component Analysis (PCA) account for 72.1% of the total variance. They are categorized into the following factors: Economic–Urban Development, Carbon Governance, Industrial Carbon and Material Intensity, and Energy Source and Decarbonization Structure. The CEEEI adjusted (CEEEI_adj) evaluates countries based on their embodied efficiency, revealing that developed nations, including the UK, Netherlands, and Sweden, have the lowest embodied emissions, whereas fast-urbanizing, fossil-dependent countries perform poorly. The regression analysis shows that GDP per capita, urbanization rates, and fossil energy consumption ratios are vital determinants of embodied intensity. This study offers a reproducible open-data system that enables construction organizations worldwide to develop decarbonization policies. Full article

Background: Dementia incidence is rising globally, yet its determinants remain debated. While diet has been linked to cognitive health, distinguishing dietary effects from socioeconomic and demographic transitions is challenging. This study examined associations between meat protein and fat supply and dementia incidence worldwide, accounting for life expectancy, GDP per capita, urbanization, and genetic predisposition (I_bs). Methods: Ecological data from 204 “countries” were analyzed. Pearson and Spearman correlations assessed bivariate relationships. Stepwise regression identified independent predictors of dementia incidence (ln-transformed). Partial correlations tested unique effects of protein and fat after adjustment for confounders. Principal component analysis (PCA) explored latent structures. Results: Meat protein and fat supply correlated moderately with dementia incidence (r ≈ 0.65, p < 0.001), but life expectancy showed the strongest association (r = 0.82, p < 0.001). Regression confirmed life expectancy as the dominant correlate (β ≈ 0.56–0.82, p < 0.001). Meat fat supply remained an independent positive association (β = 0.17–0.23, p ≤ 0.01; partial r = 0.22, p = 0.004), whereas protein effects were weaker and inconsistent, sometimes reversing to a negative association (partial r = −0.15, p = 0.043). PCA showed all variables loaded on a single “development–nutrition transition” factor explaining ~74% of variance. Conclusions: Dementia incidence is largely shaped by demographic aging, but dietary fat from meat shows a modest, independent association, whereas protein does not consistently relate to risk. Rising fat consumption linked to nutrition transitions may represent a modifiable global correlate of dementia. Full article

People are exposed to mixtures of metals, per- and polyfluoroalkyl substances (PFAS), phthalates, and polycyclic aromatic hydrocarbons (PAH) rather than single chemicals, yet mixture inference is hampered by high dimensionality, correlation, missingness, and left-censoring below limits of detection (LOD). We analyzed 2013–2014 National Health and Nutrition Examination Survey (NHANES) biomarkers (n = 4367) to (i) recover latent, interpretable co-exposure structures and (ii) quantify how these mixtures relate to liver health. To denoise and handle censoring, we applied Principal Component Pursuit with LOD adjustment (PCP-LOD), decomposing the exposure matrix into a non-negative low-rank component (population co-exposure profiles) and a sparse component (individual spikes), and then used Bayesian Kernel Machine Regression (BKMR) to estimate nonlinear and interactive associations with AST, ALT, GGT, ALP, total bilirubin, and the Fatty Liver Index (FLI), retaining analytes with ≥50% detection. PCP-LOD revealed coherent clusters (e.g., long-chain PFAS grouping; shared metal loadings), while the sparse layer highlighted episodic phthalate elevations. BKMR indicated outcome-specific mixture effects: PAHs and selected phthalates showed consistently positive associations with ALP, GGT, and FLI; PFAS (PFOS, PFNA, PFOA) exhibited modest associations with ALP and bilirubin; metals displayed mixed directions. A joint increase in the overall mixture from the 25th to 75th percentile corresponded to an upward shift in FLI and a smaller rise in ALT. This censoring-aware low-rank-plus-sparse framework coupled with flexible mixture modeling recovers actionable exposure architecture and reveals clinically relevant links to liver injury and steatosis, motivating longitudinal and mechanistic studies to strengthen causal interpretation. Full article