Search Results (1,743)

We consider Riemann soliton vector fields and Ricci bi-conformal vector fields on the oscillator group. We prove that the oscillator group admits Riemann solitons. Subsequently, we provide a complete classification of all Ricci bi-conformal vector fields admitted by the oscillator group and identify those that belong to specific categories, namely gradient-type vector fields, Killing vector fields, and Ricci collineation using the partial differential equations. Full article

Background: Zirconia ceramics are generally used in monolithic restorations, and their microstructural, mechanical, and optical properties continue to improve. Several factors affect the mechanical properties of these restorations; however, the combined effects of yttria content and margin design on the fracture resistance remain unclear. Methods: Sixty monolithic zirconia crowns were fabricated and assigned to six groups (n = 10) based on three different yttria contents (strength-gradient multilayer zirconia containing 3 mol% yttria tetragonal zirconia polycrystals in the dentin region and 5 mol% yttria-partially stabilized zirconia in the occlusal region: 3Y-TZP/5Y-PSZ [ZP], 3 mol% yttria tetragonal zirconia polycrystals: 3Y-TZP [HTML], and 4 mol% yttria-partially stabilized zirconia: 4Y-PSZ [STML]), and two different margin designs (chamfer and rounded shoulder). Crowns were adhesively bonded to standardized 3-dimensional-printed resin dies and subjected to thermal and mechanical aging (10,000 thermocycles at 5–55 °C, and 1.2 million mechanical cycles at 50 N, 1.6 Hz). Fracture resistance values were recorded in Newtons, and fracture types were evaluated. Data were analyzed using a two-way analysis of variance (ANOVA), and Bonferroni adjustment was used for multiple comparisons (α = 0.05). Results: A significant interaction between yttria content and margin design was found (p = 0.005). In the chamfer margin design groups, ZP (2208.5 ± 501.9 N) and HTML (2069.6 ± 463.3 N) showed significantly higher fracture resistance than STML (1444 ± 303.2 N) (p < 0.05). In the rounded shoulder margin design groups, no significant differences were observed among ZP (1662.8 ± 293.8 N), HTML (1940.9 ± 341.6 N), and STML (1795.6 ± 529.6 N) (p > 0.05). ZP and HTML showed higher fracture resistance values with the chamfer margin design, while STML showed higher fracture resistance with the rounded shoulder margin design. Conclusions: The fracture resistance of zirconia restorations is influenced by both the margin design and the yttria content. Designing the margin geometry based on the type of zirconia to be used can enhance the mechanical properties of the restorations and support clinical decision-making. Full article

Wetland degradation in soda saline–alkali ecosystems can profoundly alter belowground microbial communities, yet its effects on bacterial phylogenetic distribution and predicted ecological characteristics remain insufficiently understood. This study investigated soil physicochemical properties, enzyme activities, and bacterial communities across a wetland degradation gradient in the Halahai Provincial Nature Reserve, China, including reed wetland (RW), meadow steppe (MS), and degraded Suaeda saline patches (DS). Soil analyses were integrated with 16S rRNA gene amplicon sequencing, phylogenetic reconstruction, and FAPROTAX and BugBase prediction. DS showed significantly higher pH and electrical conductivity, but lower soil water content, organic carbon, nutrient availability, and urease activity than RW and MS. Alpha diversity analysis indicated that DS had lower bacterial richness and diversity, but higher dominance, whereas RW and MS did not differ significantly. Beta-diversity analysis revealed clear habitat-dependent separation, with DS harboring the most distinct community structure. Taxonomic and phylogenetic analyses indicated enrichment of Gemmatimonadota and the RCP2-54 lineage in DS, whereas RW and MS were more strongly associated with Pseudomonadota, Acidobacteriota, and related groups. Predicted functional and phenotypic analyses further suggested a shift toward stress-related and degradation-associated traits in DS. These findings demonstrate that wetland degradation reshaped the taxonomic composition, phylogenetic distribution, and predicted ecological characteristics of soil bacterial communities in this fragile ecosystem. Full article

The durability and serviceability of steel-fiber-reinforced concrete (SFRC) tunnel linings in cold regions are significantly challenged by repeated freeze–thaw actions, making the accurate prediction of frost resistance a critical engineering problem. Although extensive research has been conducted on the freeze–thaw characteristics of concrete, the existing empirical and mechanism-based models remain limited in capturing the complex nonlinear interactions among mixture proportions, steel fiber characteristics, and environmental conditions. Therefore, a data-driven prediction framework based on machine learning was developed in this study. A database containing 277 groups of standardized SFRC freeze–thaw test results was established, incorporating key variables including mixture design parameters, fiber properties, and freeze–thaw cycle conditions. Four machine-learning models, namely, support vector regression, back-propagation neural network, gradient boosting, and extreme gradient boosting (XGB), were constructed and systematically compared. Model accuracy was assessed using MAE, MAPE, MSE, RMSE, and R². The results demonstrate that all models can reflect the nonlinear relationship between the input variables and mass loss rate, while the XGB model exhibits superior predictive performance with a testing R² of 0.91, representing an improvement of approximately 3–28% compared with other models. Meanwhile, the prediction errors are reduced significantly, with RMSE and MAE decreased by about 19–58% and 22–65%, respectively. The proposed approach provides an improved and reliable tool for predicting frost resistance and supports the durability design and optimization of SFRC tunnel linings in severe cold-region environments. Full article

Microplastics in freshwater environments provide persistent substrates for microbial colonization, forming the plastisphere. However, how trophic conditions shape plastisphere bacterial communities in drinking-water source reservoirs remains poorly understood. In this study, nine major drinking-water source reservoirs in Longyan City, Fujian Province, China, were investigated. Water quality measurements, trophic state assessment, and 16S rRNA gene amplicon sequencing were combined to characterize plastisphere bacterial communities across oligotrophic and mesotrophic reservoirs. The comprehensive trophic level index classified four reservoirs as mesotrophic and five as oligotrophic. Bacterial alpha diversity indices showed no significant trophic-dependent pattern, whereas PERMANOVA revealed significant compositional divergence between trophic groups (p < 0.01). Electrical conductivity, pH, and dissolved oxygen were the strongest correlates of community variation. Mesotrophic reservoirs were enriched in Bacillota and Bacteroidota, with biomarkers mainly affiliated with Comamonadaceae, while oligotrophic reservoirs harbored more diverse biomarkers dominated by Pseudomonadota and Cyanobacteriota. Functional prediction indicated that only aliphatic non-methane hydrocarbon degradation differed significantly between trophic groups, whereas nitrogen-cycling functions showed no significant divergence. These findings demonstrate that trophic status acts as a significant environmental filter shaping plastisphere community structure in drinking-water source reservoirs, even within a narrow oligotrophic-to-mesotrophic gradient, providing new insights for ecological risk assessment of microplastics in source-water ecosystems. Full article

As the core of digital infrastructure, the exceptionally rapid development of data centers (DCs) faces serious challenges due to their high electricity costs. Traditional approaches treat computational task scheduling separately from different physical control mechanisms, such as server group management, overlooking the synergistic potential between the two aspects. To address this problem, this paper proposes a computational–physical collaborative optimization model that realizes spatiotemporal task migration on the computational side and adaptive parameter regulation of IT equipment and cooling devices on the physical side. In response to the lack of global coordination in conventional distributed optimization, a two-layer partially observable Markov game (POMG) is constructed to unify global cooperative decision-making and local autonomous control. On this basis, the hierarchical multi-agent deep deterministic policy gradient (H-MADDPG) algorithm is designed by introducing task priority ranking and a variable-dimension action mask mechanism, which effectively handles the discrete–continuous hybrid action space and adapts to the dynamic variation in action dimensions caused by uncertain task arrivals. Comparative experiments with various benchmark schemes are conducted to verify the effectiveness and superiority of the proposed strategy in total cost, power usage effectiveness (PUE), resource utilization, and load balancing. Full article

Background: In 2020, the first pre-assembled bioprosthetic aortic valved conduit (AVC) was approved in the United States. This study compares its anatomic and functional outcomes to traditional hand-sewn composite conduits in patients undergoing aortic root replacement. Methods: This retrospective study compared 118 patients receiving the pre-assembled AVC (2021–2023) versus 66 patients with hand-sewn conduits (2012–2020) after elective bio-Bentall procedures. Primary outcomes were post-operative mortality and complication rates. Secondary outcomes included anatomic and hemodynamic changes. Graft dimensions were obtained from post-operative computed tomography (CT). Echocardiographic parameters were collected at early and late follow-up. Between-group differences and longitudinal changes were assessed using linear mixed-effects models. Results: Groups were comparable in age (pre-assembled 63 ± 11 vs. hand-sewn 64 ± 11 years) and predominantly male. Despite significantly higher concomitant hemiarch rates in pre-assembled conduits (91.5% vs. 28.8%, p < 0.001), 30-day mortality, stroke, and reoperation for bleeding were comparable between groups. Pre-assembled conduits demonstrated superior hemodynamics with lower baseline peak gradients (Δ 9.1 mmHg, p < 0.001), lower mean gradients (Δ 5.3 mmHg, p < 0.001), and larger indexed effective orifice area (Δ 0.27 cm²/m², p = 0.018). Annual rates of hemodynamic and dimensional change were minimal and comparable between groups. Kaplan–Meier analysis showed no survival difference at 3 years. Conclusions: The pre-assembled AVC demonstrates equivalent safety and superior early hemodynamic performance compared to hand-sewn conduits, with stable mid-term anatomic and functional outcomes. Full article

Approximately 60% of the world’s primary energy is dissipated as waste heat, representing a critical opportunity for energy recovery in sectors such as electro-mobility and fuel cells. Commercial thermoelectric generators (TEGs), predominantly based on bismuth telluride (Bi₂Te₃), face limitations due to mechanical rigidity, toxicity, and high production costs. This study proposes graphene oxide (GO) as an emerging alternative thanks to its oxygenated functional groups and layered structure as well as GO paper facilitates’ thermal and electrical transport. However, the effective integration of this nanomaterial into solid-state systems under real operating conditions remains a technical challenge. Therefore, this work presents the development, multiphysics modeling, and experimental validation of an innovative TEG cell using GO paper as an active layer. The results demonstrate that the proposed GO-ITC achieves an average of 2.75 times higher generated voltage with a lower thermal gradient as well as an improved equivalent figure of merit (ZT) compared to Bi₂Te₃-based TEGs. This work contributes to the evaluation of GO-doped materials for voltage generation under specific thermal gradients, providing a lightweight and flexible solution for waste heat harvesting in modern power systems. Full article

Background: Hospital readmission among patients with diabetes remains a major challenge for healthcare systems, contributing to increased costs and adverse patient outcomes. Early identification of high-risk patients may support targeted interventions and improved care management. Objectives: This study aimed to develop and rigorously evaluate a machine learning framework for predicting 30-day hospital readmission in patients with diabetes using a large multi-institutional clinical dataset. Methods: The study utilized the Diabetes 130-US Hospitals dataset from the UCI Machine Learning Repository, comprising 101,766 hospital encounters. Data preprocessing included missing-value handling and feature engineering. Several machine learning models were evaluated, including Logistic Regression, Random Forest, XGBoost, and LightGBM, alongside a stacking ensemble model. Model performance was assessed using nested cross-validation (5 outer folds, 3 inner folds), probability calibration via Platt scaling, and statistical robustness through 1000 bootstrap resamples. Clinical utility was evaluated using decision curve analysis and clinical impact curves, while SHAP analysis was applied for model interpretability. Results: The stacking ensemble model achieved a nested cross-validated ROC–AUC of 0.664 and a calibrated AUC of 0.688, with a Brier score of 0.094. Risk stratification demonstrated a clear gradient between low- and high-risk groups, and decision curve analysis indicated positive clinical net benefit across relevant decision thresholds. Conclusions: The proposed machine learning framework provides a robust and clinically interpretable approach for predicting 30-day hospital readmission in diabetic patients, with potential utility for supporting clinical decision-making and care management. Full article

Wild orchid populations are declining with intensified habitat fragmentation posing severe challenges to germplasm conservation. As an important ornamental Orchidaceae species, Cymbidium ensifolium has abundant germplasm resources and frequent natural and artificial hybridization. Long-term natural evolution and anthropogenic disturbance have led to complex genetic backgrounds and ambiguous phylogenetic relationships hindering accurate germplasm identification, elite resource excavation, and selective breeding. As a distinctive variety, Cymbidium ensifolium var. susin has great breeding potential. Clarifying its phenotypic and genetic characteristics is crucial for accelerating breeding progress. In this study, phenotypic determination, Hyper-seq reduced-representation genome sequencing, SNP/InDel genotyping, genetic diversity analysis, and core collection construction were used to evaluate the genetic diversity, population differentiation, and core germplasm screening of 13 Cymbidium ensifolium var. susin accessions. The results showed significant phenotypic differences and rich genetic variation among tested materials. Based on highly weighted floral traits, accessions were divided into three major phenotypic groups. At the molecular level, 963,239 SNP and 182,399 InDel loci were identified and mainly distributed in intergenic regions, followed by introns and exons. A phylogenetic tree was constructed from SNP loci combined with principal component and phenotypic clustering analyses. This study preliminarily clarified the genetic structure of pure-heart Cymbidium ensifolium var. susin, showing a distinct geographical pattern: “high consistency in Fujian and Guangdong; strong differentiation in Southwest China; and a transitional gradient in Central China”. Meanwhile, six core germplasm accessions were screened in this study, which provides a solid theoretical basis and material support for the conservation of pure-heart Cymbidium ensifolium var. susin accessions, variety improvement, hybrid parent selection, and molecular marker-assisted breeding. This is of great significance for promoting the innovation of Chinese orchid germplasm resources and the high-quality development of the industry. Full article

This article examines two-state proportional hazard rate models with unobserved heterogeneity specific to each state, a framework that is especially relevant for labor market transitions. To make estimation feasible in large longitudinal datasets, we implement hshaz2s, a Stata routine that uses analytical expressions for the gradient vector and Hessian matrix of the log-likelihood function through the dual second-order moment (d2 ml) method. The empirical application estimates a discrete-time duration model for transitions between employment and unemployment using Spanish labor market microdata for young low-skilled workers over 2000–2019. The results show that apprenticeship contracts are associated with lower exit rates from employment than other temporary contracts, but not with faster transitions from unemployment back into employment. The estimates also reveal substantial state-specific unobserved heterogeneity, with a large latent group characterized by persistent spells in both states. Analytical second-order information also markedly reduces convergence time under richer heterogeneity structures. Overall, the article makes this class of two-state hazard models operational for applied research and provides new evidence on apprenticeship and temporary contracts in Spain. Full article

Background and Objectives: Halitosis is a common condition with substantial psychosocial impact, frequently driven by intra-oral biofilm, tongue coating, and reduced salivary clearance. This study compared the short-term effectiveness of standardized counseling alone, probiotic lozenges containing Streptococcus salivarius K12, and a zinc-containing mouthrinse in adults with persistent intra-oral halitosis. Materials and Methods: In this 4-week, parallel-group, randomized pragmatic trial, 117 adults with bothersome halitosis for at least 3 months and baseline organoleptic score ≥ 2 were allocated 1:1:1 to standard care, probiotic lozenges, or zinc mouthrinse. All participants received standardized counseling and tongue cleaning instructions. The primary endpoint was change in volatile sulfur compounds (VSCs) measured by portable sulfide monitoring. Secondary outcomes included organoleptic score, Halitosis Associated Life-Quality Test (HALT), Oral Health Impact Profile-14 (OHIP-14), tongue coating, plaque, and salivary Solobacterium moorei quantified by qPCR. Results: Baseline demographic, clinical, and biochemical characteristics were comparable across groups. All interventions improved outcomes over 4 weeks, but improvements followed a consistent gradient favoring zinc mouthrinse, followed by probiotic lozenges, then standard care. Mean VSC reduction was −12.7 ± 33.9 ppb with standard care, −47.3 ± 42.2 ppb with probiotics, and −78.5 ± 36.3 ppb with zinc mouthrinse (p < 0.001). Organoleptic scores improved by −0.2 ± 0.7, −0.8 ± 0.8, and −1.2 ± 0.8, respectively (p < 0.001). HALT and OHIP-14 scores showed parallel reductions, and moderate/severe halitosis at week 4 remained most frequent in the standard care group (58.9%) and least frequent in the zinc group (20.5%; p = 0.004). Conclusions: Both active adjunctive strategies improved intra-oral halitosis beyond standardized counseling alone, but the zinc-containing mouthrinse produced the greatest short-term benefits across objective, clinician-rated, and patient-reported outcomes. These findings support zinc-based rinses as a practical short-term adjunct for managing persistent intra-oral halitosis in outpatient dental care. Durability after discontinuation and potential relapse beyond 4 weeks were not assessed in this trial. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

Background/Objectives: Ischemic stroke, hemorrhagic stroke, and transient ischemic attack (TIA) differ in terms of medical severity and prognosis; however, it remains unclear whether these differences are reflected in nursing complexity and nursing intensity when assessed using standardized nursing language. Methods: This retrospective study analyzed routinely collected nursing and administrative data from an acute care hospital. Hospitalizations were classified as ischemic stroke, hemorrhagic stroke, or TIA using ICD-9-CM codes. Nursing complexity was measured as the number of nursing diagnoses (NDs) documented within 24 h of admission, while nursing intensity was measured as the number of nursing actions (NAs) recorded during hospitalization. Group differences were tested using ANOVA and Kruskal–Wallis tests, as appropriate. Results: A total of 728 hospitalizations were included: 429 ischemic strokes, 236 hemorrhagic strokes, and 63 TIAs. Overall, 4136 NDs and 27,528 NAs were recorded. Distinct patterns emerged across stroke categories. ND counts differed significantly (F = 5.81, p = 0.003), with TIA showing lower counts than both ischemic and hemorrhagic stroke, while no significant difference was observed between ischemic and hemorrhagic stroke. NA counts also differed significantly (H = 16.73, p < 0.001), with the highest counts in hemorrhagic stroke, intermediate counts in ischemic stroke, and the lowest counts in TIA. In a sensitivity analysis standardized by length of stay, nursing intensity also differed significantly across stroke categories (H = 12.999, p = 0.002), although the pattern differed from that observed for cumulative counts. Conclusions: Nursing complexity and nursing intensity showed distinct patterns across stroke categories. While complexity was comparable between ischemic and hemorrhagic stroke and lower in TIA, intensity followed a clear gradient, highest in hemorrhagic stroke, intermediate in ischemic stroke, and lowest in TIA. Standardized nursing data may complement medical indicators by capturing additional dimensions of patient needs and care delivery in people with stroke. Full article

Background and aim: Chest CT is a major contributor to population radiation exposure. Conventional, pooled diagnostic reference levels (DRLs) do not account for inter-individual variability in body habitus and are typically used retrospectively. We evaluated dose behavior in adult chest CT, derived BMI-stratified local DRLs, and developed models to enable AI-assisted, prescan dose prediction. Methods: Consecutive adult chest CT examinations from a single center were analyzed. Dose indices (CTDIvol, DLP) and patient factors (BMI, weight, height, age, sex; scan length and planned technical parameters where available) were extracted. DRLs were defined as the 75th percentile overall and within BMI categories (underweight, normal, overweight, and obese). Group differences were assessed using non-parametric tests; associations were examined using correlation analysis. Supervised learning (e.g., Random Forest, Gradient Boosting) was trained to predict CTDIvol and DLP from routinely available variables. Results: BMI-stratified DRLs increased monotonically with habitus: underweight 444.95 mGy·cm/9.60 mGy; normal 513.00/11.55; overweight 756.08/14.65; obese 931.60/20.25 (DLP/CTDIvol). Differences across BMI groups were significant for DLP (H = 31.53, p < 0.001) and CTDIvol (H = 33.61, p < 0.001). DLP correlated moderately with weight and BMI (r ≈ 0.54–0.56, p < 0.001), with a weaker association for age; height was not a meaningful predictor. No sex-based differences in CTDIvol or DLP were observed. Predictive models estimated CTDIvol and DLP with high performance (R² up to ~0.79 and ~0.77, respectively), enabling comparison of predicted dose against BMI-matched DRLs before acquisition. Conclusions: Size-aware, BMI-stratified DRLs provide clinically interpretable investigation levels that avoid pitfalls of pooled benchmarks. Coupled with robust prediction of individualized dose from routine variables, this framework supports a shift from retrospective audit to prospective, point-of-care dose governance and protocol optimization in chest CT. Full article