Search Results (207)

Among the main man-made water pollutants that pose a danger to the environment are oil products, heavy metals, and radionuclides, as well as micro- and nanoplastics. To purify such waters, it is necessary to use advanced methods, with sorption being one of them. The aim of this work is to develop a nano-functionalized composite, comprising magnetically responsive, thermally expanded graphite (TEG) and the natural clay bentonite, and to assess its ability to purify man-made contaminated waters. Throughout the course of the research, the methods of scanning electron microscopy, optical microscopy, dynamic light scattering, radiometry, and atomic absorption spectrophotometry were used. The use of the TEG–bentonite composite for the purification of the model water, simulating radioactively contaminated nuclear power plant (NPP) effluent, reduced the content of organic substances by 10–15 times, and the degree of extraction of cesium, strontium, cobalt, and manganese was between 81.4% and 98.8%. The use of the TEG–bentonite composite for the purification of real radioactively contaminated water obtained from the object “Shelter” (“Ukryttya” in Ukrainian), in the Chernobyl Exclusion Zone, Ukraine, with high activity, containing organic substances, including micro- and nanoplastics, reduced the radioactivity by three orders of magnitude. The use of cesium-selective sorbents for additional purification of the filtrate allowed for further decontamination of radioactively contaminated water with an efficiency of 99.99%. Full article

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research. Full article

Background: The combined impact of impaired kidney function and subclinical myocardial injury (SCMI) on cardiovascular (CV) mortality has not been well studied. We aimed to evaluate their individual and joint associations with cardiovascular mortality. Methods: We analyzed data from 6057 participants (mean age 57.0 ± 13.0 years) in the U.S. Third National Health and Nutrition Examination Survey. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Electrocardiographic SCMI was defined as a cardiac infarction/injury score ≥ 10. CV mortality was determined from the National Death Index. Multivariable logistic regression assessed baseline cross-sectional associations between eGFR and SCMI. Cox proportional hazards models were used to examine the individual and combined associations of eGFR and SCMI with CV mortality. Results: At baseline, 1297 participants (21.4%) had SCMI. In multivariable logistic regression analysis, eGFR < 45 mL/min/1.73 m² (vs. ≥45) was not associated with SCMI (OR [95% CI]: 1.10 [0.84–1.45]). Over a median follow-up of 18.4 years, 690 CV deaths occurred. In separate Cox models, both SCMI (vs. no SCMI) and eGFR < 45 (vs. ≥45) were associated with increased CV mortality risk (HR [95% CI]: 1.36 [1.16–1.60] and 1.56 [1.24–1.99], respectively). Compared with participants with eGFR ≥ 45 and no SCMI, those with both eGFR < 45 and SCMI had the highest CV mortality risk (HR [95% CI]: 2.36 [1.65–3.36]), followed by eGFR < 45 alone (1.47 [1.09–1.96]) and SCMI alone (1.33 [1.11–1.58]). Conclusions: Both reduced eGFR and SCMI were independently associated with CV mortality. Their coexistence showed the highest risk, but without statistical significance compared with each alone, possibly reflecting limited power and distinct mechanisms. Full article

Background: Cardiovascular disease (CVD) is the leading cause of mortality in chronic kidney disease (CKD), driven by mechanisms distinct from the general population. Fibroblast Growth Factor 23 (FGF-23), a phosphaturic hormone elevated early in CKD, has been mechanistically linked to left ventricular hypertrophy, vascular dysfunction, and disordered mineral metabolism. This study examines the associations between FGF-23 and key renal, mineral, and cardiovascular parameters and its utility in risk stratification. Methods: We conducted a cross-sectional study of 60 adults with CKD stages 1–5. Serum FGF-23 was quantified using ELISA, alongside measures of iPTH, phosphorus, calcium, and eGFR (Estimated Glomerular Filtration Rate). Cardiovascular evaluation included transthoracic echocardiography and carotid intima-media thickness (CIMT). Associations were analyzed using Spearman correlations, ROC analysis, and multivariable logistic regression. Results: FGF-23 levels were significantly associated with declining eGFR (r = –0.288; p < 0.05), elevated iPTH (Intact Parathyroid Hormone) (r = 0.361; p < 0.05), and serum phosphorus (r = 0.335; p < 0.05). Patients with structural cardiac abnormalities (left atrial enlargement or left ventricular hypertrophy) exhibited higher FGF-23 concentrations (154 vs. 128 pg/mL; p = 0.027). FGF-23 alone predicted high cardiovascular risk with moderate accuracy (AUC 0.70; sensitivity 76%; specificity 67%). A composite model including iPTH and eGFR improved discriminatory power (AUC 0.76). Conclusions: FGF-23 correlates with subclinical cardiovascular remodeling and key mineral abnormalities in CKD. Its integration with iPTH and eGFR enhances cardiovascular risk stratification, supporting its potential as a multidimensional biomarker in early CKD. However, the cross-sectional design and modest correlation strengths limit causal inference and generalizability of the findings. Full article

Filtrate from dewatering anaerobically digested biosolids is a side-stream of wastewater treatment that contains high concentrations of nitrogen and phosphorus compounds that can serve as nutrients for cultivating microalgae biomass as biofilms for bioproduct production at Water Resource Recovery Facilities (WRRFs). One system used to cultivate attached microalgae biofilms is the rotating algal biofilm reactor (RABR). A pilot RABR with 72 m² growth surface area, 11.5 m² footprint area, and a liquid volume of 11,500 L was operated in an outdoor environment at the largest WRRF in Utah, U.S.A, the Central Valley Water Reclamation Facility (CVWRF). The configuration of the RABR was altered from the previous configuration with regard to temperature and duty cycle with the goal to maximize biomass productivity. Results included an increase in dry biomass productivity on a footprint basis from 8.8 g/m²/day to 26.8 g/m²/day (205%) while power requirements changed from 28.3 W to 91 W. The increase in biomass productivity has direct benefits for bioproducts including bioplastic, biofertilizer, and the extraction of lipids for conversion to biofuels. Full article

As the demand for lightweight and high-performance conductive materials grows in power transmission systems, aluminum alloy rods have emerged as a cost-effective and scalable alternative to copper conductors. This review systematically examines the development status and technological progress in the purification and casting–rolling processes used in the production of Electrical Round Aluminum Rods (ERARs). It explores current challenges in improving electrical conductivity and mechanical strength while addressing issues such as hydrogen and oxide inclusion removal, grain refinement, and impurity segregation. Key purification techniques—including flux refining, gas treatment, filtration, and rotary injection—are compared in terms of performance, cost, and environmental impact. The paper also analyzes different casting–rolling methods, including continuous casting and rolling, twin-roll casting, and extrusion processes, with attention to process optimization and equipment design. Furthermore, emerging applications of artificial intelligence (AI) in predictive modeling, defect detection, and process parameter optimization are highlighted, offering a novel perspective on intelligent and sustainable ERAR production. This paper aims to provide insights for facilitating the industrial-scale production and performance enhancement of ERAR materials. Full article

The neutrophil-to-lymphocyte ratio (NLR) has emerged as a readily accessible, cost-effective biomarker reflecting systemic inflammation. Chronic low-grade inflammation plays a pivotal role in the pathogenesis and progression of metabolic and cardiovascular disorders including chronic kidney disease (CKD), hypertension, diabetes mellitus, and cardiovascular disease (CVD). This review critically evaluates the current evidence on NLR as a prognostic marker across these interconnected conditions. A comprehensive literature search was conducted focusing on clinical and epidemiological studies investigating the association between NLR and CKD, hypertension, diabetes, and cardiovascular outcomes. Mechanistic insights into inflammation-driven pathophysiology and the predictive utility of NLR in disease progression and adverse events were synthesized. Elevated NLR is consistently associated with increased risk and severity of CKD, correlating with glomerular filtration decline, proteinuria, and mortality. In hypertension, higher NLR levels are linked to non-dipper blood pressure patterns, arterial stiffness, and increased cardiovascular risk. Among diabetic patients, NLR correlates with poor glycemic control and vascular complications. In cardiovascular disease, elevated NLR predicts major adverse cardiovascular events (MACE) and all-cause mortality, reflecting underlying immune dysregulation and endothelial dysfunction. Despite promising findings, direct comparisons with established inflammatory biomarkers remain limited, and heterogeneity exists across populations. NLR represents a simple yet powerful inflammatory biomarker with significant prognostic value in CKD, hypertension, diabetes, and cardiovascular disease. Its integration into clinical risk stratification models could enhance personalized medicine approaches. Future research should focus on longitudinal studies, validation in diverse cohorts, and comparative analyses with other inflammatory markers to fully delineate NLR’s clinical utility. Full article

Background: The association between blood pressure (BP) dipping profiles and kidney function among chronic kidney disease (CKD) patients has been well established within the literature, but studies conducted on kidney transplant (KT) patients remain limited. Individual KT studies have small sample sizes and conflicting results. Meta-analysis overcomes these limitations by pooling data to increase statistical power and provide robust clinical guidance. This meta-analysis systematically assesses the impact of BP patterns on KT and CKD populations, aiming to highlight improved BP management strategies in these populations. Materials and methods: A comprehensive search was conducted up to September 9th, 2024, using multiple electronic databases. Results: The current study included 7 studies with a total of 788 patients. KT recipients showed a higher prevalence of non-dipper blood pressure profile than CKD patients. Also, those with a dipper profile had a significantly higher estimated glomerular filtration rate (eGFR) compared to non-dippers and reverse dippers, implying better graft function. No significant differences were observed in acute rejection risk, proteinuria, renal resistive index, cholesterol, or triglycerides across blood pressure profiles. Conclusions: These findings reveal a high prevalence of non-dipping blood pressure profiles in KT and CKD patients, linked to worse renal and cardiovascular outcomes, while also highlighting the need for ambulatory blood pressure monitoring and tailored BP management strategies in these high-risk populations to potentially improve outcomes. However, the observational nature of available studies limits causal inference, and further prospective research is required to establish definitive therapeutic recommendations. Full article

Inflammatory biomarkers, including high-sensitivity C-reactive protein (hsCRP), serum amyloid A (SAA), and interleukin-6 (IL-6), have been associated with an increased risk of future cardiovascular events. While they provide valuable prognostic information, these associations do not necessarily imply a direct causal role. The combined prognostic utility of these markers, however, remains insufficiently studied. We analysed 3300 well-characterised participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, all of whom underwent coronary angiography. Participants were stratified based on their serum concentrations of hsCRP, SAA, and IL-6. Associations between biomarker combinations and mortality were assessed using multivariate Cox regression and ROC analysis. Individuals with elevated hsCRP and SAA or IL-6 showed higher prevalence rates of coronary artery disease, heart failure, and adverse metabolic traits. These “both high” groups had lower estimated glomerular filtration rate, higher NT-proBNP, and increased HbA1c. Combined elevations of hsCRP and SAA were significantly associated with higher all-cause and cardiovascular mortality in partially adjusted models. However, these associations weakened after adjusting for IL-6. IL-6 alone demonstrated the highest predictive power (AUC: 0.638) and improved risk discrimination when included in multi-marker models. The co-elevation of hsCRP, SAA, and IL-6 identifies a high-risk phenotype characterised by greater cardiometabolic burden and increased mortality. IL-6 may reflect upstream inflammatory activity and could serve as a therapeutic target. Multi-marker inflammatory profiling holds promise for refining cardiovascular risk prediction and advancing personalised prevention strategies. Full article

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

Fuel electro-hydraulic servo valves are core components in the fuel control system of aero-engines, and their performance directly affects thrust regulation and power output precision. Due to the combustibility of the working medium in fuel systems and the lack of effective circulation filtration, the retention of micron-sized particles within the valve gap can lead to valve spool jamming, which is a critical reliability issue. This study, based on fractal theory and the liquid–solid two-phase flow model, proposes a parametric model for non-ideal surface valve gaps and analyzes the dynamics of particles subjected to drag, lift, and buoyant forces on rough surfaces. By numerically analyzing flow field models with different roughness levels and comparing them with an ideal smooth gap model, the migration characteristics of particles were studied. To verify the accuracy of the model, an upscaled experimental setup was built based on similarity theory, and PIV experiments were conducted for validation. Experimental results show that the particle release position and valve surface roughness significantly affect particle migration time. The weight of the release position on particle migration time is 63%, while the impact of valve surface roughness is 37%. In models with different roughness levels, the particle migration time increases more rapidly for roughness values greater than Ra0.4, while for values less than Ra0.4, the increase in migration time is slower. Furthermore, the study reveals that particle migration trajectories are independent of flow velocity, with velocity only affecting particle migration time. This research provides theoretical support for enhancing the reliability of fuel electro-hydraulic servo valves and offers a new perspective for the design of highly reliable hydraulic components. Full article

Fibrous biomaterials are essential in biomedical engineering, tissue engineering, and filtration due to their specific transport and mechanical properties. Fluid flow through these materials is critical for their function. However, many biological fluids exhibit non-Newtonian behavior, characterized by micro-rotational effects, which traditional models often overlook. The current study presents a machine learning (ML) framework for the prediction and understanding of hydraulic permeability in fibrous biomaterials with a micropolar fluid. A dataset of 1000 numerical simulations was generated by varying the micropolar fluid properties and the fiber volume fraction in a periodic porous structure with nine parallel cylindrical fibers in a square lattice. Six powerful ML algorithms were deployed: Decision Trees (DT), Random Forests (RF), XGBoost, LightGBM, Support Vector Regression (SVR), and k-Nearest Neighbors (kNN). The balance of predictive capacity to unseen data values (tracking $R^2$ values and error metrics) with computational efficiency for all algorithms was assessed. The best-performing ML algorithm was subsequently used to interpret the decisions made by the model using Shapley Additive exPlanations (SHAP) analysis and understand the role of feature importances. The SHAP findings highlight the potential of ML in capturing complex fluid interactions and guiding the design of advanced fibrous biomaterials with optimized hydraulic permeability. Full article

Air pollution imposes a substantial economic burden globally, with estimated annual losses exceeding $8.1 trillion due to healthcare costs, lost productivity, infrastructure degradation, and agricultural damage. This review assesses the economic effectiveness of advanced air pollution control technologies within the broader context of sustainable energy transitions. Through comparative life-cycle cost-benefit analyses, we evaluate the financial viability, energy efficiency, and policy relevance of innovations such as carbon capture and storage (CCS), AI-driven emissions monitoring, and nanotechnology-enhanced filtration. Among the technologies assessed, CCS presents the most significant capital expenditure (up to $500 million per facility) but offers long-term returns through carbon credits and enhanced oil recovery, yielding up to $30–40 in economic benefits for every $1 invested. AI-based monitoring systems demonstrate strong economic efficiency by reducing energy consumption in industrial operations by up to 15% and improving regulatory compliance at a larger scale. Nanotechnology-enabled filters provide high pollutant capture efficiency and reduce operational resistance, yet face scalability and end-of-life challenges. Additionally, emerging technologies such as bioengineered filters offer promise for low-resource settings but require further economic validation. The integration of these technologies with renewable energy systems, such as hydrogen-powered pollution control units and solar-driven filtration, further amplifies their environmental and economic benefits. By aligning air pollution mitigation with climate and energy goals, this review highlights a pathway for policymakers and industries to achieve both economic resilience and environmental sustainability. The findings underscore that, while upfront costs may be high, strategic investments in advanced pollution control deliver substantial long-term returns across sectors. Full article

Efficient solid–liquid separation is crucial in industries like mining, but traditional chamber filter presses depend heavily on manual monitoring, leading to inefficiencies, downtime, and resource wastage. This paper introduces a machine learning-powered digital twin framework to improve the operational flexibility and predictive control of a traditional chamber filter press. A key challenge addressed is the degradation of the filter medium due to repeated cycles and clogging, which reduces filtration efficiency. To solve this, a neural network-based predictive model was developed to forecast operational parameters, such as pressure and flow rates, under various conditions. This predictive capability allows for optimized filtration cycles, reduced downtime, and improved process efficiency. Additionally, the model predicts the filter medium’s lifespan, aiding in maintenance planning and resource sustainability. The digital twin framework enables seamless data exchange between filter press sensors and the predictive model, ensuring continuous updates to the training data and enhancing accuracy over time. Two neural network architectures, feedforward and recurrent, were evaluated. The recurrent neural network outperformed the feedforward model, demonstrating superior generalization. It achieved a relative $L^2$-norm error of $5\%$ for pressure and $9.3\%$ for flow rate prediction on partially known data. For completely unknown data, the relative errors were $18.4\%$ and $15.4\%$, respectively. Qualitative analysis showed strong alignment between predicted and measured data, with deviations within a confidence band of $8.2\%$ for pressure and $4.8\%$ for flow rate predictions. This work contributes an accurate predictive model, a new approach to predicting filter medium cycle impacts, and a real-time interface for model updates, ensuring adaptability to changing operational conditions. Full article

Background/Objectives: This study aimed to investigate the prognostic value of the red cell distribution width-to-estimated glomerular filtration rate (RGR) ratio in patients hospitalized with chronic heart failure (CHF) and its potential interaction with NT-proBNP levels. By integrating anemia and renal dysfunction markers, the RGR may provide enhanced predictive insights regarding extended length of hospital stay (ELOS) > 7 days, in-hospital mortality, and 6-month all-cause mortality across specific CHF phenotypes. Methods: In this retrospective, single-center pilot observational study, 627 CHF admissions (January 2022–August 2024) were analyzed. Patients were classified according to the ESC guidelines into heart failure with reduced (HFrEF), mildly reduced (HFmrEF), or preserved ejection fraction (HFpEF). The RGR was calculated as red cell distribution width standard deviation (RDW-SD) divided by estimated glomerular filtration rate (eGFR). Predictive accuracy was evaluated using logistic regression, receiver operating characteristic (ROC) analyses, and stepwise Cox proportional hazard regression. Results: RGR was significantly higher in HFrEF than in HFpEF (p = 0.042) and predicted ELOS only in HFpEF (AUC = 0.619). In contrast, for in-hospital mortality, RGR achieved excellent discrimination in HFrEF (AUC = 0.945), outperforming RDW and NT-proBNP. In HFmrEF, RDW exhibited the highest predictive power (AUC = 0.826), whereas in HFpEF, NT-proBNP was the strongest predictor (AUC = 0.958), although RGR preserved good discrimination (AUC = 0.746). Across the entire cohort and HF phenotypes, RGR consistently emerged as a significant predictor in univariable analysis. In multivariable models, it improved the significance prognosis especially alongside NT-proBNP in the entire cohort and HFrEF. For 6-month all-cause mortality, RGR surpassed RDW in prediction in all HF phenotypes. Conclusions: The RGR independently predicts prolonged hospitalization, in-hospital, and 6-month mortality in CHF—often outperforming RDW and eGFR and being comparable to NT-proBNP, especially in HFrEF. These findings suggest that RGR may serve as a valuable risk stratification tool in CHF management. Full article