Search Results (1,451)

Background and Objectives: Despite critically low levels of chronic kidney disease (CKD) awareness worldwide, there is no psychometrically validated instrument to comprehensively assess CKD awareness across socioecological levels. This study aimed to develop, psychometrically evaluate and validate a multidimensional awareness scale grounded in socioecological theory for patients with stage 3–4 CKD. Materials and Methods: This methodological study enrolled 908 stage 3–4 CKD patients. Scale development proceeded through systematic stages: comprehensive literature review, qualitative interviews (n = 15), expert panel evaluation (n = 25), and pilot testing. The initial 72-item pool was refined to 41 items (Content Validity Index = 0.912). The sample was randomly split for exploratory factor analysis (EFA; n = 454) and confirmatory factor analysis (CFA; n = 454). Psychometric evaluation encompassed internal consistency (Cronbach’s α, McDonald’s ω), test–retest reliability (n = 30; 4-week interval), convergent validity (average variance extracted [AVE], composite reliability [CR]), discriminant validity (Fornell–Larcker criterion), and criterion validity (correlation with Turkish Health Literacy Scale-32 [TSOY-32]). Results: EFA revealed a seven-factor structure with an acceptable explained variance of 43.8%. Following iterative item elimination based on communalities (h² < 0.20) and factor loadings (λ < 0.30), CFA confirmed the final 34-item model with good fit (CFI = 0.972; RMSEA = 0.070 [90% CI: 0.067–0.074]). The factor structure captured awareness across core socioecological levels (Individual, Interpersonal/Institutional, Community, and Systemic), complemented by Treatment Adherence and Social Impact dimensions. Internal consistency coefficients were α = 0.884 and ω = 0.889 for the total scale. Test–retest reliability yielded an ICC of 0.954 (95% CI: 0.907–0.978). Convergent and discriminant validity were confirmed via composite reliability (CR: 0.740–0.953) and the Fornell–Larcker criterion. Criterion validity analysis revealed a significant correlation with TSOY-32 (r = 0.810, p < 0.001). Conclusions: The CKD Awareness Scale (CKD-AS-34) represents a novel, psychometrically validated, multidimensional awareness instrument for CKD. This scale enables clinicians to identify awareness deficits spanning individual to systemic levels, facilitating personalised patient education and targeted public health interventions. Full article

Background and Objectives: Sclerostin or dickkopf-1 (DKK1) inhibits the canonical Wnt/β-catenin signaling pathway, which regulates vascular calcification and may contribute to the development of arterial stiffness. The brachial–ankle pulse wave velocity (baPWV) measures peripheral arterial stiffness (PAS). This study aimed to investigate the correlation between sclerostin and DKK1 levels and PAS in patients with type 2 diabetes mellitus (T2DM). Materials and Methods: Biochemical data and sclerostin and DKK1 levels were analyzed in the fasting blood samples of 125 patients with T2DM. baPWV measurements using the VaSera VS-1000 automatic pulse wave analyzer classified patients with values > 18.0 m/s on either side into the PAS group. Results: Among patients with T2DM, 47 (37.6%) were classified as having PAS. These patients exhibited higher hypertension prevalence (p = 0.002); greater age (p < 0.001); elevated systolic (p < 0.001) and diastolic blood (p = 0.012) pressures; and increased fasting glucose (p = 0.001), glycated hemoglobin (p = 0.008), triglyceride (p = 0.001), blood urea nitrogen (p < 0.001), and creatinine (p = 0.001) levels, urine albumin-to-creatinine ratio (p = 0.039), and C-reactive protein (p = 0.024) and serum sclerostin (p < 0.001) levels, but decreased estimated glomerular filtration rate (p < 0.001). Multivariate logistic regression analysis identified serum sclerostin level (odds ratio, 1.127; 95% confidence interval, 1.058–1.200; p < 0.001) as an independent PAS predictor in patients with T2DM. Serum log-transformed sclerostin levels were positively correlated with left (p = 0.005) and right (p = 0.001) baPWV via Spearman’s rank-order correlation coefficient analysis. Conclusions: Serum sclerostin levels, but not DKK1 levels, are positively correlated with PAS in patients with T2DM. Full article

The Xinqiao Cu-S polymetallic deposit is located in the Tongling ore concentration area of the Middle-Lower Yangtze River metallogenic belt. The orebodies consist of skarn orebodies and stratiform sulfide orebodies, but the genetic link between them remains controversial. In this study, magnetite was used as a proxy to systematically constrain the hydrothermal evolution from the intrusion to the contact zone and further to the stratiform orebodies. A representative drill hole (E603) was logged, and samples were systematically collected from the Jitou pluton outward to the contact zone. Composite samples from the 8–28 m interval were crushed and prepared as resin mounts for integrated TIMA automated mineralogy, BSE textural observation, and in situ LA-ICP-MS trace element analysis. Five types of magnetite (Mt1 to Mt5) were systematically identified. Mt1 occurs as inclusions within feldspar in the quartz monzodiorite. It exhibits typical magmatic magnetite characteristics and contains grid-like ilmenite exsolution, indicating crystallization during the late magmatic stage. Mt2 is distributed in the interstices of magmatic minerals, commonly showing hematitization and replacement of ilmenite exsolution lamellae by titanite. Its trace element geochemistry displays magmatic–hydrothermal transitional features. Mt3–Mt5 in the skarn and stratiform orebodies are paragenetic with retrograde alteration minerals (e.g., epidote, chlorite, and actinolite) and sulfides, and are characterized by low Ti, Al, and V contents and high Mg, Mn, and Sn contents, indicating a hydrothermal origin. From Mt3 to Mt5, (Ti + V) and (Al + Mn) decrease, while Zn and Mn increase, accompanied by a decrease in the (Si + Al)/(Mg + Mn) ratio. This reflects a trend of decreasing fluid temperature and progressively enhanced wall-rock buffering. The Mg-in-magnetite geothermometer yields relatively consistent results for Mt1–Mt3, but anomalously high temperatures for Mt4–Mt5. This suggests that the elevated Mg activity in the fluid, caused by reaction with carbonate wall rocks, can significantly influence the calculated temperatures. Therefore, this geothermometer should be used cautiously for magnetite in the outer skarn zone and interpreted in combination with other temperature constraints. The textures, paragenetic mineral assemblages, and trace element characteristics of magnetite collectively reveal a continuous mineralization process linking the skarn and stratiform orebodies at Xinqiao, providing robust mineralogical and geochemical evidence for the contribution of Yanshanian magmatic–hydrothermal activity to the stratiform mineralization. Full article

Regression analysis of interval-censored failure time data commonly arises in biomedical studies, particularly when the available sample size is limited. Although many methods have been proposed for the semiparametric probit model with interval-censored data, there does not appear to exist an established approach that effectively borrows information from external sources to improve estimation efficiency. Such external information may arise, for example, in clinical trials where an auxiliary dataset from a related population is available but may differ from the target population in certain aspects, leading to heterogeneity between populations. To address this issue, a sieve maximum likelihood estimation procedure is developed for the semiparametric probit model with interval-censored data, and a transfer learning method is proposed to leverage auxiliary information from a source domain to improve estimation efficiency in the target domain while accounting for population heterogeneity. The proposed approach is based on a penalized likelihood formulation and uses monotone splines to approximate the unknown baseline function, providing flexibility in both modeling and computation. Simulation studies show that the proposed estimator substantially improves estimation accuracy compared with methods that rely solely on the target data, particularly when the target sample size is small. An application to an Alzheimer’s disease dataset further illustrates the practical usefulness of the proposed approach in biomedical studies. Full article

Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval ($T_s$). In parallel, the node adjusts the number of buffered samples as the battery state of charge ($SOC$) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction. Full article

As mineral exploration increasingly targets deeper and more geologically complex terrains, the need for reliable predictive models becomes critical to mitigating exploration risk and improving cost efficiency. Correspondingly, the effectiveness of deep mineral exploration strategies depends substantially on the effectiveness and precision of three-dimensional mineral prospectivity mapping (3D MPM) models. However, the inherent spatial non-stationarity—where ore grade variability changes across geological domains—and the strongly skewed distribution of high-grade samples present a dual challenge. Conventional methods, which primarily rely on mean-based regression, often struggle to adequately address this dual challenge, limiting their predictive performance in complex geological settings. To address these issues, this paper proposes a pinball-loss-guided, global–local fusion Transformer model within a unified framework for 3D MPM. It leverages a multi-head self-attention mechanism with global–local fusion to capture long-range dependencies and global geological contexts, while incorporating local feature extraction modules to adaptively model spatially varying mineralization controls, jointly optimized through a pinball loss function to address mineralization distribution skewness. The proposed framework was first rigorously evaluated using the Xiadian gold deposit as a case study. Bootstrap analysis of the ablation experiments confirmed its predictive performance in terms of quantile-specific accuracy and prediction interval (PI) calibration. Ten rounds of random data splits provided further confirmation of the model’s stability. Subsequently, the validated model was applied to prospectivity mapping in unexplored regions, leading to the delineation of several high-potential exploration targets. Finally, comparative analyses with state-of-the-art machine learning methods were conducted, which further validated the competitive fitting capability of the proposed framework. Full article

Since the initiation of two variants of the bootstrap method by Efron and Rubin in the late 1970s, a variety of advancements has emerged in the literature. The subsampling of blocks enabled the estimation of the actual variance of the sample mean. The equivalence of the data-level and the estimator-level resampling is easily established for the sample mean and estimators alike. For Rubin’s variant of the bootstrap we apply an algorithm by Diniz et al. which allows for the numerically stable computation of the sample-based cumulative distribution function of the estimator under investigation. No actual Monte-Carlo resampling is necessary in this setting and we demonstrate how we get access to the very small probabilities of the tails and moreover to confidence intervals. We do this at the example of a well-known test model that exhibits geometrically decaying spatial correlations. The analysis naturally applies to temporally correlated systems or to the correlations occurring in Markov chains, as well. Full article

In this study, the temporal and spatial distribution characteristics of ionospheric scintillation in the East Asian sector are statistically analyzed based on S4 data provided by the COSMIC-1 occultation dataset and solar–terrestrial spatial environment parameters from 2007 to 2018. The results show that scintillation activity has an obvious distribution pattern with local time: the frequency gradually increases from 17:00 in the evening, with the peak concentrated at 22:00–01:00 at night; in terms of seasonal variation, scintillation activity is highest in spring and fall, followed by summer, and lowest in winter; and, regarding annual variation, it is highly correlated with the solar activity. Further analyses show that scintillation activity is strongly correlated with geomagnetic activity. On this basis, this study constructs a two-layer LSTM deep learning model based on weighted regression to realize S4 numerical forecasting for the next 1 h in the middle- and low-latitude regions of China, using F10.7, Kp, Dst, sunspot number, solar wind vertical velocity, and historical S4 values as inputs. The model demonstrates robust predictive performance on the validation dataset containing 8760 samples, with a mean squared error of 0.00546 and an absolute error that is distributed within the interval [−0.2, 0.2] 98% of the time, indicating strong accuracy and robustness. These results suggest that the proposed model provides a high-precision tool for ionospheric scintillation warning. Full article

Background: low-quantity or degraded samples are often studied in forensic genetics. Therefore, it is important to efficiently obtain all the available DNA from the biological sample analyzed to provide the most reliable results. This is particularly challenging in bone marrow processing due to its hydrophobic molecular structure, as for other lipid-rich tissues, especially if rancid. In fact, during adipose tissue decomposition, the putrefaction of fatty acids can in some instances give a compact cerous consistency to the lipidic tissue, hardly susceptible to the nucleic acid extraction mechanisms. According to environmental circumstances, this condition is notably observable in submerged bodies or in putrefied bone marrow. Thus, this study is focused on developing an optimized nucleic acids extraction protocol for putrefied bone marrow. Methods: genetic analyses were performed on putrefied yellow bone marrow collected from 20 human femora recovered from bodies in different decomposition stages. The optimized method was developed by integrating additional steps, reagents and time intervals on a silica-based column commercial kit. This strategy was compared in DNA yield to a standard extraction protocol, represented by the same commercial kit, but following the manufacturer’s directions. Both these strategies were tested in nucleic acid isolation efficiency by performing DNA typing, including real-time PCR quantification, Short Tandem Repeats (STR) amplification and fragments analysis steps. The analytical parameters evaluated were allele count, DNA concentration (ng/µL) and Degradation Index (DI). Results: for allele count and DNA concentration parameters, the optimized protocol showed clear and significant qualitative and quantitative improvements compared with the standard protocol, supporting its potential applicability in forensic casework and laying the foundation for future studies. Conclusions: prior to appropriate laboratory internal validation, the optimized protocol can be used for tough lipid-rich tissues processing without the need to purchase a dedicated system and using a same commercial kit routinely adopted for other forensic genetics matrices. Full article

Background/Objectives: Administrative and clinical databases are increasingly used for research, but their value depends on coding accuracy. The Spanish National Hospital Discharge Database (CMBD) is a standardised registry widely applied in epidemiology. Type 1 diabetes mellitus (T1DM) is an autoimmune disease with early onset and long-term complications. This study aimed to validate the accuracy of T1DM diagnoses recorded in the CMBD. Methods: A cross-sectional validation study was conducted at Hospital Clínico San Carlos (Madrid, Spain) including discharges from 2016–2023. Two age- and sex-matched samples of 384 admissions each (with and without T1DM coding, ICD-10 E10) were randomly selected. The gold standard was the confirmation of T1DM based on the diagnostic criteria established by the 2016 American Diabetes Association (ADA) consensus, which remained valid through 2025, verified by a detailed review of electronic health records (EHRs). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals (CIs), and interobserver concordance was assessed with Cohen’s kappa. Results: Of the 245,206 discharges, 1324 (0.54%) included a T1DM diagnosis. Validation showed a sensitivity of 100% (95% CI: 98.7–100), specificity of 80.2% (95% CI: 76.4–83.5), PPV of 75.3% (95% CI: 70.7–79.3), and NPV of 100% (95% CI: 99.0–100). Interobserver agreement was excellent (κ = 0.869). Specificity declined with age, from 100% in patients < 30 years to 60% in those ≥ 80 years, mainly due to misclassification with insulin-treated type 2 diabetes. Conclusions: T1DM diagnoses in the CMBD show very high validity and reliability in younger patients, supporting their use in epidemiological and clinical research, while complementary verification is advisable in older adults. Full article

Modeling ship maneuvering dynamics presents numerous challenges, including long-term multi-step recursive error accumulation, insufficient generalization under distributed control rates, and high-frequency disturbance amplification effects. Traditional analytical models heavily rely on vessel-specific trials to characterize strongly nonlinear coupling terms and perform parameter identification, making it difficult to balance efficiency and accuracy under complex operating conditions. This paper presents a ship maneuvering-oriented integration of an invertible Koopman representation and a NeuralODE-based continuous-time predictor. The IKN reconstructs strongly coupled state spaces while enhancing representational invertibility, whereas NeuralODE directly fits the control differential equations governing ship maneuvering dynamics and supports continuous-time prediction. Experiments validate multi-rate control performance under ideal and disturbed data conditions, assessing error accumulation and extrapolation stability through long-term multi-step propagation. Evaluations utilize the KVLCC2-type L7 ship model with a 0.25 s sampling interval and a 200 s prediction horizon, validated against a multi-rate control test set. The results indicate that, compared to the baseline neural ODEs model without IKN, the normalized root mean square error (NRMSE) of state quantities decreased by 12.68% on average. In typical operational scenarios such as constant-speed emergency turns and variable-speed sine sweep maneuvers, the average state NRMSE was 7.96% lower than the LSTM model and 53.85% lower than the IKN–Koopman operator network. Noise experiments demonstrated that when introducing simulated sensor noise at 5%, 10%, and 20% into the dataset, the average state NRMSE remained at 5.98%, 8.24%, and 10.06%, respectively. This confirms the method’s stable prediction performance under varying noise intensities. Full article

This study explores classical and Bayesian estimation for the two-parameter Chen distribution with randomly censored data, where censoring times follow an independent two-parameter Chen distribution with separate shape and scale parameters. We first derive the maximum likelihood estimators of the unknown parameters, together with their asymptotic variances and credible intervals, and further adopt the method of moments, L-moments and least squares methods for classical estimation. Under the generalized entropy loss function and inverse gamma priors, Bayesian estimation is implemented via Gibbs sampling, with the highest posterior density credible intervals of parameters constructed accordingly. We also investigate the estimation of key reliability and lifetime characteristics of the distribution, and conduct Monte Carlo simulations to compare the performance of all aforementioned estimation methods. Finally, two real-world CMAPSS jet engine lifetime datasets from NASA are applied to validate the practical effectiveness of the proposed estimation approaches, demonstrating the enhanced flexibility of the Chen distribution compared to the exponential distribution in fitting aerospace-related censored data, given the marginal p-values in the K-S tests. Full article

Dairy cows commonly experience negative energy balance during the periparturient period, predisposing them to metabolic disorders such as ketosis (KET), hypophosphatemia (HP), hypocalcemia (HC), and hypomagnesemia (HM). However, comparative data on breed- and parity-related differences remain limited. Therefore, these differences were evaluated in this study during the postpartal transition period. A total of 174 cows (149 Holstein, 25 Jersey) were monitored, and blood samples were collected from calving to 21 days postpartum at 3-day intervals to measure β-hydroxybutyrate, inorganic phosphorus, calcium, and magnesium concentrations. Metabolic disorders were defined using established thresholds. Data were analyzed using Fisher’s exact test, the Mann–Whitney U test, and generalized estimating equations. HP was the most prevalent disorder in both breeds. Jerseys had 2.83 times higher odds of KET, whereas Holsteins had 4.98 times higher odds and an earlier onset of HM. Multiparous Holsteins showed higher incidences of HP, HC, and HM compared to primiparous ones, while parity effects were minimal in Jerseys. Breed and parity significantly influenced both the incidence and onset timing of postpartal metabolic disorders. These findings highlight the importance of breed- and parity-specific health management strategies in dairy cattle farms. Full article

Rapid point of care assessment of pulmonary surfactant composition by measuring the lecithin/sphingomyelin (L/S) ratio could improve management of patients with neonatal respiratory distress syndrome (nRDS). Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) offers a practical route to making such measurements, but the influence of the sample solvent prior to drying on measurement repeatability is poorly understood. We compare films dried from dichloromethane (DCM) and water (AQ) solvents (DCM-dry route vs. AQ-dry route) by ATR-FTIR and show that spectra from the AQ-dry route increased the signal-to-noise ratio (SNR) of a representative (2920 cm⁻¹) absorption peak for the mixture from 20.13 to 128.20 and for human endotracheal aspirate (ETA) from 6.33 to 8.13. A mixed nested analysis of variance (ANOVA) showed that drying route accounted for 89.52% of mixture peak height variance and reduced percent relative standard deviation (%RSD) from 23.5% to 16.2%, corroborated by multivariate analysis for ETA. We further demonstrate that partial least squares regression (PLSR) models trained on AQ-dry mixture spectra predicted L/S (R² = 0.91; root mean square error (RMSE) = 0.31) with 95% prediction interval grey-zone interpretation around L/S = 2.2, complemented by a receiver operating characteristic area under the curve (ROC-AUC) of 0.978. Full article

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article