Search Results (3,567)

The use of additive manufacturing (AM) has seen increased utilization over the last decade, thanks to well-documented advantages such as lower startup costs, reduced wastage, and the ability to rapidly prototype. The poor surface finish of unprocessed AM components is one of the major drawbacks of this technology, with the research literature suggesting a measurable impact on flow characteristics and burner operability. For instance, surface roughness has been shown to potentially increase resistance to boundary layer flashback—an area of high concern, particularly when utilizing fuels with high hydrogen content. A more detailed understanding of the underlying thermophysical mechanisms is, therefore, required. Computational fluid dynamics can help elucidate the impact of these roughness effects by enabling detailed data interrogation in locations not easily accessible experimentally. In this study, roughness effects on a generic gas turbine swirler were numerically modeled using a low-y+ detached eddy simulation (DES) approach. Three DES models were investigated utilizing a smooth reference case and two rough cases, the latter employing a literature-based and novel equivalent sand-grain roughness (k_s) correlation developed for this work. Existing experimental isothermal and CH₄ data were used to validate the numerical simulations. Detailed investigations into the effects of roughness on flow characteristics, such as swirl number and recirculation zone position, were subsequently performed. The results show that literature-based k_s correlations are unsuitable for the current application. The novel correlation yields more promising outcomes, though its effectiveness depends on the chosen turbulence model. Moreover, it was demonstrated that, for identical k_s values, while trends remained consistent, the extent to which they manifested differed under reacting and isothermal conditions. Full article

Background: Early detection of dysglycemia in young adults is important but underexplored. This study aimed to (1) predict long-term changes in fasting plasma glucose (δ-FPG) and (2) classify future prediabetes using complementary machine learning (ML) approaches. Methods: We analyzed 6247 Taiwanese men aged 18–35 years (mean follow-up 5.9 years). For δ-FPG (continuous outcome), random forest, stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net were compared with multiple linear regression using Symmetric mean absolute percentage error (SMAPE), Root mean squared error (RMSE), Relative absolute error(RAE), and Root relative squared error (RRSE) Sensitivity analyses excluded baseline FPG (FPG_base). Shapley additive explanations(SHAP) values provided interpretability, and stability was assessed across 10 repeated train–test cycles with confidence intervals. For prediabetes (binary outcome), an XGBoost classifier was trained on top predictors, with class imbalance corrected by SMOTE-Tomek. Calibration and decision-curve analysis (DCA) were also performed. Results: ML models consistently outperformed regression on all error metrics. FPG_base was the dominant predictor in full models (100% importance). Without FPG_base, key predictors included body fat, white blood cell count, age, thyroid-stimulating hormone, triglycerides, and low-density lipoprotein cholesterol. The prediabetes classifier achieved accuracy 0.788, precision 0.791, sensitivity 0.995, ROC-AUC 0.667, and PR-AUC 0.873. At a high-sensitivity threshold (0.2892), sensitivity reached 99.53% (specificity 47.46%); at a balanced threshold (0.5683), sensitivity was 88.69% and specificity was 90.61%. Calibration was acceptable (Brier 0.1754), and DCA indicated clinical utility. Conclusions: FPG_base is the strongest predictor of glycemic change, but adiposity, inflammation, thyroid status, and lipids remain informative. A dual interpretable ML framework offers clinically actionable tools for screening and risk stratification in young men. Full article

Wet–dry cycles are a key factor aggravating the durability degradation of foam concrete. To address this issue, this study prepared bentonite slurry–steel slag foam concrete (with steel slag and cement as main raw materials, and bentonite slurry as admixture) using the physical foaming method. Based on 7-day unconfined compressive strength tests with different mix proportions, the optimal mix proportion was determined as follows: mass ratio of bentonite to water 1:15, steel slag content 10%, and mass fraction of bentonite slurry 5%. Based on this optimal mix proportion, dry–wet cycle tests were carried out in both water and salt solution environments to systematically analyze the improvement effect of steel slag and bentonite slurry on the durability of foam concrete. The results show the following: steel slag can act as fine aggregate to play a skeleton role; after fully mixing with cement paste, it wraps the outer wall of foam, which not only reduces foam breakage but also inhibits the formation of large pores inside the specimen; bentonite slurry can densify the interface transition zone, improve the toughness of foam concrete, and inhibit the initiation and propagation of matrix cracks during the dry–wet cycle process; the composite addition of the two can significantly enhance the water erosion resistance and salt solution erosion resistance of foam concrete. The dry–wet cycle in the salt solution environment causes more severe erosion damage to foam concrete. The main reason is that, after chloride ions invade the cement matrix, they erode hydration products and generate expansive substances, thereby aggravating the matrix damage. Scanning Electron Microscopy (SEM) analysis shows that, whether in water environment or salt solution environment, the fractal dimension of foam concrete decreased slightly with an increasing number of wet–dry cycle times. Based on fractal theory, this study established a compressive strength–porosity prediction model and a dense concrete compressive strength–dry–wet cycle times prediction model, and both models were validated against experimental data from other researchers. The research results can provide technical support for the development of durable foam concrete in harsh environments and the high-value utilization of steel slag solid waste, and are applicable to civil engineering lightweight porous material application scenarios requiring resistance to dry–wet cycle erosion, such as wall bodies and subgrade filling. Full article

The increasing demand for sustainable construction materials has driven interest in utilizing waste biomass within polymer composites. Rigid polyurethane foams, widely valued for thermal insulation, exhibit a significant flammability issue. This study investigates the impact of incorporating various waste biomass materials, including brewers’ spent grain, coffee grounds, and soybean husk and their combustion ashes on the selected properties of rigid polyurethane foams. The primary objective is to assess the potential of these eco-friendly additives as replacements for traditional raw materials, aiming to enhance fire resistance and thermal stability and thereby promoting circular economy principles in the construction sector. Composite foam samples were fabricated using a mixing and casting technique, incorporating 5% wt. of fillers into the polymer matrix. Thermal stability and flammability were evaluated using cone calorimetry and thermogravimetric analysis. The findings indicated that while biomass inclusion did not significantly improve char formation, the addition of ash substantially increased char yield, a critical factor in fire suppression. Although biomass and ash may influence flammability, they do not inherently bolster the intrinsic thermal stability of the polyurethane matrix itself. Full article

Background/Objectives: Transforming growth factor-beta (TGF-β) is a multifunctional cytokine involved in immune regulation, extracellular matrix turnover, and tissue repair. Its role in periodontitis remains controversial due to conflicting human studies. This systematic review addressed the PICO-based question: in adults with periodontitis (population), how does the expression and regulation of TGF-β isoforms (intervention/exposure) compare with healthy or post-treatment states (comparator) regarding clinical outcomes (outcomes)? Methods: A systematic search of PubMed and Scopus was conducted on 1 July 2025 for human studies published in English between 2010 and 2025. Eligible studies investigated TGF-β expression, function, or genetic regulation in periodontal tissues or biological fluids. Screening and quality appraisal were performed according to PRISMA guidelines, using design-specific risk-of-bias tools. The review protocol was prospectively registered in PROSPERO (CRD420251138456). Results: Fifteen studies met inclusion criteria. TGF-β1 was the most frequently analyzed isoform and was consistently elevated in diseased gingival tissue and gingival crevicular fluid, correlating with probing depth and attachment loss. Several studies reported post-treatment reductions in TGF-β, supporting its value as a dynamic biomarker. Additional findings linked TGF-β signaling to immune modulation, fibrosis, bone turnover, and systemic comorbidities. Evidence for TGF-β2 and TGF-β3 was limited but suggested isoform-specific roles in epithelial–mesenchymal signaling and scar-free repair. Conclusions: Current evidence supports TGF-β, particularly TGF-β1, as a central mediator of periodontal inflammation and repair, with promise as both a biomarker and therapeutic target. Standardized, isoform-specific, and longitudinal studies are needed to clarify its diagnostic and translational utility. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

Background: Liver biopsy performed after less invasive workup, including imaging, for evaluation of abnormal liver function studies occasionally reveals large bile duct obstruction on histology without evidence of biliary obstruction on prior imaging. The utility of ERCP in this setting has not been studied in pediatrics. In the present study, we address this important clinical issue. Methods: A retrospective review of pediatric pathology and clinical records from 2010 to 2019 identified 123 pediatric patients with large duct obstruction on liver biopsy performed after imaging revealed no evidence of biliary obstruction. The absolute standardized difference (ASD) was used to compare baseline covariates between patients who underwent ERCP vs. all others. Covariates included age, gender, race, ethnicity, BMI, and labs (total bilirubin, GGT, alkaline phosphatase, AST, ALT, platelets, and INR). Results: Of 85 unique patients who met inclusion/exclusion criteria, 15 (17.6%) underwent ERCP. The majority of these patients who underwent ERCP (80%) had a therapeutic endoscopic intervention with a favorable impact on clinical trajectory. The mean age of patients with large duct obstruction was 7 years old. Most patients were white (47%), followed by Asian (17%). Only 25% of patients identified as Hispanic. The mean laboratory values were as follows: total bilirubin 4.61 mg/dL, GGT 353 U/L, alkaline phosphatase 403 U/L, AST 343 U/L, ALT 251 U/L, platelets 289 K/uL, and INR 1.19. Absolute standardized differences comparing baseline covariates between the ERCP and non-ERCP groups are included in Table 1. The largest absolute standardized difference between the two groups was for race (1.17), ethnicity (0.553), and GGT (0.463). Age, alkaline phosphatase, and INR were not significantly different between the two groups (ASD <0.2 for both). Conclusions: Only 17.6% of pediatric patients with large ducts undergo ERCP. Pediatric patients who underwent ERCP were more likely to be white, non-Hispanic, and have elevated GGT. Of interest, age did not differ significantly between the two groups, which may reflect enhanced uniformity of utilization of ERCP across age groups in pediatrics. Additional multi-center studies, including more patients and focused on understanding the utility of ERCP and the range of outcomes following the diagnosis of large duct obstruction in pediatrics, would be informative to guide pediatric hepatology and endoscopic practices. Full article

Biomass combustion for the production of electricity and heat remains one of the most widespread renewable energy technologies. Biomass is commonly utilized in fluidized bed combustion systems. Over the years, numerous issues related to the preparation and combustion of biomass in fluidized beds have been identified, including fouling and slagging, which involve the formation of deposits. These phenomena can be mitigated through various methods, including design modifications to boilers, the application of additives, and the careful selection and classification of fuel. Several fuel indices have been proposed to predict the behavior of fuels in terms of their tendency to cause fouling and slagging. Most of these indices were developed for fossil fuels, and the discrepancies between them suggest that although these indices are widely applied, their applicability to agricultural residues, such as straw, remains uncertain. Researchers working in this field emphasize the need for further research, particularly focusing on the comparison of developed indices with the results of biomass combustion at both laboratory and industrial scales. In this study, ten assortments of straw sourced from Poland were selected, and chemical composition analyses were conducted to determine selected fuel indices. The analyzed straw samples were then combusted in a 100 kWₜₕ laboratory-scale circulating fluidized bed unit. Using a specialized austenitic steel probe, the growth rate of the deposit was measured. The collected deposit masses for each straw type were then compared with the calculated fuel indices. The best correlation between the interpretation of the index values and the deposit mass on the probe was observed for the Rs index. However, due to the low sulfur content of straw, Rs numerical interpretation was not adequate. Overall, the indices indicating both good correlation coefficients and an appropriate numerical interpretation for fouling tendency were B/A, Fu, and Cl. Full article

The aim of this study is to present the taxation of the Polish farm sector in the years 2004–2022 in comparison with other EU countries. Three indicators were applied to describe the phenomenon: the value of taxes per farm, the share of taxes in family farm net income, and the value of taxes per hectare of agricultural utilized area. The analysis is based on data from the FADN database. Results for Poland were contrasted with those of other EU Member States. In addition, the sensitivity of the Polish farm tax burden to twelve basic production, economic, and financial categories was examined using the ordinary least squares method with a constant term. The findings were compared with EU averages, providing a descriptive overview of how tax burdens interact with farm performance and sustainability conditions. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Composites are essential materials in a wide range of industrial and medical applications due to their unique functional properties. One of the main issues of composites arises at their end-of-life stage, especially in terms of the recyclability process and its quantitative evaluation. In this study, we present a quantitative methodology for assessing the quality of composite recycling, using a paraffin-based microcomposite with the addition of tungsten particles (at one concentration 50 wt.%) as an example. Our approach combines visible-light microscopy with digital image processing techniques to obtain quantitative metrics related to recycling efficiency. The tools utilized—recognized as relatively common and uncomplicated for use in various scientific fields—have shown that the value of average particle density significantly decreased from a primary value of 43.30% to 8.30%. Consequently, the presented results confirm the usefulness of the method for the quantitative assessment of the quality of the recycling process. Full article

Unripe Pisang Awak banana is rich in resistant starch and dietary fiber, which are recognized for supporting digestive health and bowel regularity, yet its limited solubility restricts its application in instant beverages. This study aimed to improve the functional quality of Pisang Awak banana powder (PABP) through drum drying, electron beam irradiation, and inulin supplementation. PABP was produced by tray or drum drying and irradiated at 0, 2, 4, 6, and 8 kGy. Drum-dried powder treated with 8 kGy was identified as optimal and further fortified with inulin at 0–10% (w/w). Compared with tray drying, drum drying with irradiation markedly accelerated rehydration and enhanced solubility. Incorporation of 10% inulin produced the best overall performance, yielding faster reconstitution, greater solubility at 80–90 °C, and lower viscosity values across all pasting parameters. Collectively, the combination of drum drying, irradiation, and inulin addition yielded a banana powder with improved reconstitution and reduced gelation upon cooling. This optimized formulation demonstrates potential as a model for starch-based instant powders, while also contributing to the sustainable utilization of local banana resources. Full article

In the field of materials science, experimental data or simulation results on material properties are often unevenly distributed. In addition to the vast unexplored material space, properties of lesser interest have not been measured even for well-studied materials, as exemplified by the discovery of the superconductivity of the long-known MgB₂. To overcome such challenges, utilizing relationships among material properties based on scientific principles can be beneficial. We have been constructing a knowledge graph of material property relationships using natural language-processing techniques for years. Now, with the surprising development of large language models, constructing a knowledge graph has become much easier. This article explains what a knowledge graph of material property relationships is, presents several types of applications for the knowledge graph, and describes how the constructed knowledge graph can be implemented in machine learning for predicting material property values. We also demonstrate the construction of a knowledge graph of material property relationships and a search system using ChatGPT, without any programming, which will be made publicly available. Full article

A variety of factors, such as absorption, reflection, and attenuation by atmospheric elements, influence the quantity of solar energy that reaches the surface of the Earth. This, in turn, impacts photovoltaic (PV) power generation. In light of this, a digital assessment of solar energy variability through short-term measurements was conducted to enhance PV power output. The clear-sky index $\left(K_t^{*}\right)$ methodology was employed, effectively eliminating any indications of solar energy obstruction and comparing the measured radiation to the theoretical clear-sky radiation. The solar energy data were gathered in Mozambique, specifically in the southern region at Maputo–1, Massangena, Ndindiza, and Pembe, in the mid-region at Chipera, Nhamadzi, Barue–1, and Barue–2, as well as in the northern region at Nipepe-1, Nipepe-2, Nanhupo-1, Nanhupo-2, and Chomba, over the period from 2005 to 2024, with measurement intervals ranging from 1 to 10 min and 1 h during the measurement campaigns conducted by FUNAE and INAM, with additional data sourced from the PVGIS, Meteonorm, NOAA, and NASA solar databases. The analysis indicates a $K_t^{*}$ value with a density approaching 1 for clear days, while intermediate-sky days exhibit characteristics that lie between those of clear and cloudy days. It can be inferred that there exists a robust correlation among sky types, with values ranging from 0.95 to 0.89 per station, alongside correlated energies, which experience a regression with coefficients between 0.79 and 0.95. Based on the analysis of the sample, the region demonstrates significant potential for solar energy utilization, and similar sampling methodologies can be applied in other locations to optimize PV output and other solar energy projects. Full article