Search Results (1,919)

Background and Objectives: Neuromyelitis optica spectrum disorder (NMOSD) and MOG antibody-associated disease (MOGAD) are distinct autoimmune demyelinating disorders of the central nervous system, characterized by different pathological and clinical features. Reliable biomarkers are essential for accurate diagnosis and monitoring of disease activity. Glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are promising candidates, reflecting astrocytic and axonal damage, respectively. Materials and Methods: To investigate the relationship between astroglial (GFAP) and neuronal (NfL) protein levels in the peripheral blood, 89 plasma samples were analyzed using Simoa immunoassays. The concentrations of pNfL and pGFAP were measured in three groups: AQP4-IgG-positive NMOSD patients (n = 18), MOGAD patients (n = 12), and healthy controls (HCs, n = 19). Statistical analyses assessed group differences, correlations, and the predictive value of biomarkers for disease activity. Results: Both NMOSD and MOGAD patients exhibited elevated pNfL compared with controls, indicating neuroaxonal injury. No significant differences in pNfL, pGFAP, or pGFAP/pNfL ratios were observed between patient groups. The pGFAP levels and the pGFAP/pNfL ratio were significantly higher in NMOSD patients, particularly during attacks, indicating prominent astrocyte damage. Correlations revealed associations between biomarker levels, disability, and disease duration. pNfL demonstrated high accuracy in predicting recent relapses (AUC = 0.906), whereas pGFAP showed moderate predictive capacity (AUC = 0.638). Elevated pNfL and pGFAP levels were associated with an increased likelihood of relapse within six months. Conclusions: Plasma NfL and GFAP are promising biomarkers for assessing tissue injury and disease activity in NMOSD and MOGAD. NfL predicts relapses, while GFAP primarily reflects astrocytic damage in NMOSD. Longitudinal studies are warranted to validate these biomarkers and establish clinical thresholds for disease management. Full article

Hybrid woven composite materials and structures have important application value in modern engineering because of their high specific stiffness, specific strength and excellent impact resistance. The mechanical properties of carbon/aramid fiber hybrid woven composite laminates under repeated low-velocity impacts were studied in this paper. This study aims to understand the behavior of these materials under repeated impact conditions and to evaluate their damage resistance and failure mechanisms. The materials and methods used are introduced in detail, including the preparation of samples, the experimental apparatus for impact testing, and the methods of damage assessment and data analysis. The experimental setup simulated real impact scenarios and followed procedures to collect and analyze data. The low-velocity impact tests were carried out in accordance with ASTM D7136 test standard. The experimental results show that with the increase in impact energy, the damage of laminates includes delamination, matrix cracking and fiber fracture. The damage threshold and damage propagation rate are affected by the type of fiber used and its lay-up direction in the composite. Compared with (0,90)₁₂ laminates, [(0,90)]/(±45)]_3s laminates show more obvious damage expansion, which highlights the importance of fiber orientation in the impact durability design of laminates. The results can be used to design and optimize the structure of hybrid woven composite laminates. Full article

Building energy conservation through the development of transparent thermal insulation materials that selectively block near-infrared radiation while maintaining visible light transmittance has emerged as a key strategy for global carbon neutrality. WO₃ is a semiconductor oxide with near-infrared absorption capabilities. However, the limited absorption efficiency and narrow spectral coverage of pure WO₃ significantly diminish its overall transparent thermal insulation performance, thereby restricting its practical application in energy-saving glass. Therefore, this study successfully prepared Sn-doped WO₃ materials using a one-step hydrothermal method, controlling the Sn:W molar ratio from 0.1:1 to 2.0:1. Through evaluation of transparent thermal insulation performance of a series of Sn-doped WO₃ samples, we found that Sn:W = 0.9:1 exhibited the most excellent performance, with NIR shielding efficiency reaching 93.9%, which was 1.84 times higher than pure WO₃. Moreover, this sample demonstrated a transparent thermal insulation index (THI) of 4.38, representing increases of 184% and 317%, respectively, compared to pure WO₃. These enhancements highlight the strong NIR absorption capability achieved by Sn-doped WO₃ through structural regulation. When Sn doping reaches a certain concentration, it triggers a structural transformation of WO₃ from monoclinic to tetragonal phase. After reaching the critical solubility threshold, phase separation occurs, forming a multiphase structure composed of a Sn-doped WO₃ matrix and secondary SnO₂ and WSn₀.₃₃O₃ phases, which synergistically enhance oxygen vacancy formation and W⁶⁺ to W⁵⁺ reduction, achieving excellent NIR absorption through small polaron hopping and localized surface plasmon resonance effects. This study provides important insights for developing high-performance transparent thermal insulation materials for energy-efficient buildings. Full article

Gallium oxide (Ga₂O₃) is a promising ultra-wide-bandgap (UWBG) material with exceptional transport properties, including a large breakdown voltage, making it ideal for high-voltage power device applications. Recently, Ga₂O₃ has gained significant attention as a next-generation material for electronic device fabrication aimed at advancing power electronics. In this paper, we investigate the effect of a Ga₂O₃ buffer layer on a GaN-based high electron mobility transistor (HEMT), focusing on output I–V characteristics and surface charge effects. Furthermore, we explore an advanced approach to enhance HEMT performance by utilizing polarization-induced two-dimensional electron gas (2DEG), as an alternative to conventional doping methods. A III-N/Ga₂O₃ heterostructure is proposed as a distinctive electrical property and a cost-effective UWBG solution. To evaluate the associated effects, we simulate a two-dimensional (2D) Ga₂O₃/GaN HEMT structure incorporating surface charge models. Our results confirm that 2DEG formation near the surface creates a conductive channel due to polarization-induced dipoles at the interface. The simulations also show a negative shift in the threshold voltage, a condition typically unattainable without oxidation layers or doping. Finally, we analyze the potential of AlGaN/Ga₂O₃-based HEMTs for future power electronic applications. Full article

This work is motivated by the need to enhance efficiency and radiation resistance and reduce weight in high-performance photovoltaic devices, with applications spanning both terrestrial and space environments. Metamorphic buffers are key enablers for reducing defect formation in lattice-mismatched structures, which are among the most widespread technologies for high-efficiency photovoltaic energy conversion. Although many systems have been created, absolute certainty about the effective relaxation mechanism remains unattained. In this work, MOVPE-grown step-graded buffers with variable In content were obtained on Ge substrates and investigated to identify the critical thresholds that govern strain relaxation and defect formation. The results show that the buffers are fully strained when the In top-layer content is <6.0%, while a degree of relaxation in the entire structure appears when the In top-layer content is >6.0%. In addition, the relaxation phenomenon is paralleled by the formation of a tilt angle between the layers and the substrate. We also found evidence that the appearance of relaxation is not limited to the upper layer but is presented by the structure as a whole. The effects of Te doping inside the InGaAs layers were also investigated: Te does not influence the structure of the crystal, but it introduces a Burstein–Moss blue shift in the photoluminescence energy of about 20 meV. Eventually, to reduce defect formation with the goal of achieving high-efficiency photovoltaic devices, a thick layer with a lower In content was grown onto the overshoot material (In_0.12Ga_0.88As). The results obtained confirm the high quality of the buffers and unveil the critical points, which are responsible for the most important changes in the buffer architecture and should be considered in future material engineering. The results provide valuable insights for the design of high-performance, sustainable photovoltaic devices and contribute to the advancement of III–V semiconductor integration on Ge substrates. Full article

This review investigates recent progress in the field of PLA-based conductive composites for 3D printing. First, it introduces PLA as a biodegradable thermoplastic polymer, describing its processing and recycling methods and highlighting its environmental advantages over conventional polymers. In order to evaluate its printability, PLA is briefly compared to other commonly used thermoplastics in additive manufacturing. The review then examines the incorporation of conductive fillers such as carbon black, carbon nanotubes, graphene, and metal particles into the PLA matrix, with a particular focus on the percolation threshold and its effect on conductivity. Critical challenges such as filler dispersion, agglomeration, and conductivity anisotropy are also highlighted. Recent results are summarized to identify promising formulations that combine improved electrical performance with acceptable mechanical integrity, while also emphasizing the structural and morphological characteristics that govern these properties. Finally, potential applications in the fields of electronics, biomedicine, energy, and electromagnetic shielding are discussed. From an overall perspective, the review highlights that while PLA-based conductive composites show great potential for sustainable functional materials, further progress is needed to improve reproducibility, optimize processing parameters, and ensure reliable large-scale applications. Full article

Background and Objectives: Dyslipidemia and hyperuricemia frequently co-exist in uncontrolled type 2 diabetes mellitus (T2DM), amplifying renal and cardiovascular risk. This study aimed to develop and evaluate an optimized Renal–Metabolic Risk Score (RMRS) integrating renal and lipid parameters to identify patients with both conditions. Materials and Methods: We conducted a retrospective observational study including 304 patients with uncontrolled T2DM hospitalized at the Emergency County Hospital Oradea, Romania (2022–2023). Hyperuricemia was defined as uric acid > 6 mg/dL in females and >7 mg/dL in males; dyslipidemia was diagnosed according to standard lipid thresholds. RMRS was calculated from standardized values of urea, TG/HDL ratio, and eGFR, with variable weights derived from logistic regression coefficients. The score was normalized to a 0–100 scale. Receiver operating characteristic (ROC) analysis assessed discriminative performance; quartile analysis explored stratification ability. Results: The prevalence of dyslipidemia and hyperuricemia co-occurrence was 81.6%. RMRS was significantly higher in the co-occurrence group compared to others (median 16.9 vs. 10.0; p < 0.001). ROC analysis showed an AUC of 0.78, indicating good discrimination. Quartile analysis demonstrated a monotonic gradient in co-occurrence prevalence from 64.5% in Q1 to 96.1% in Q4. Conclusions: The Renal–metabolic Risk Score (RMRS) demonstrated moderate discriminative performance in identifying patients with uncontrolled T2DM at risk for combined hyperuricemia and dyslipidemia. Because it relies on inexpensive, routine laboratory parameters, RMRS may be particularly useful in resource-limited settings to support early risk stratification, dietary counseling, and timely referral. Further validation in larger and more diverse cohorts is required before its clinical adoption. Full article

This study evaluated the effect of acidic environment and tooth brushing on the color stability and translucency of stained 3D-printed ceramic-reinforced composite (CRC) resins for indirect restorations and hybrid prostheses. Twelve specimens were prepared from each 3D-printing resin material: Ceramic Crown (CC), OnX (ONX), and Tough 2 (T2), and one CAD/CAM milling resin, Lava Ultimate (LU). After preparation, all specimens were stained, then immersed in either water or citric acid. Subsequently, the specimens underwent simulated tooth brushing for 3650 cycles. Color stability (ΔE) and translucency parameter (TP) were measured using a spectrophotometer. Data were analyzed using ANOVA, post hoc Tukey tests, and independent Student t-tests (α = 0.05). Material type, immersion medium, and their interaction did not significantly influence the mean ΔE (p > 0.05). The lowest ΔE value was for LU in acid (ΔE = 1.11 ± 0.39), and the highest for T2 in water (ΔE = 2.09 ± 1.47). Except for ONX and LU in acid, all materials had ΔE values above the perceptibility threshold (ΔE = 1.2). The mean TP was significantly affected by material type, immersion medium, and their interaction (p < 0.05). The lowest TP value was for group CC in acid (0.91 ± 0.26); the highest was for group LU in acid (6.24 ± 0.56). After immersion and subsequent tooth brushing, TP values decreased for all materials. Exposure to an acidic environment and tooth brushing did not affect color stability but significantly reduced translucency. Both the 3D-printed CRCs and milled resin material displayed comparable color stability below clinically acceptable thresholds, though the translucency of 3D-printed materials remained lower compared to milled material. Full article

Foam sound-absorbing materials develop a fine cellular structure during manufacturing, resulting in variations in porosity, cell size, and the proportion of naturally occurring thin membranes that obstruct skeletal openings. This membrane proportion significantly affects sound absorption. In this study, we utilized cross-sectional images obtained from a submicron resolution computer tomography (CT) scanner (nano-CT) that can capture membrane structures to theoretically assess the sound absorption of foam materials with membranes. We processed these cross-sectional images using techniques, including binarization, to extract the contours of the foam skeletons and the cross-sectional areas of the voids. By modeling the foam’s cross-section as the clearance between two planes, we were able to determine the propagation constant and characteristic impedance within this clearance. The effective density was adjusted based on measured tortuosity. The normal-incidence sound absorption coefficient (SAC), derived from the transfer matrix method, was then compared with experimental values obtained from a two-microphone impedance tube. Image processing techniques helped extract the skeleton cross-section and reduce residual noise, thereby minimizing the effect of variations in the binarization threshold on theoretical values. The accuracy of the theoretical model was enhanced by incorporating a correction factor for the skeleton surface area. Full article

Supercritical CO₂ (sCO₂) pipeline transport is a critical link for the large-scale implementation of Carbon Capture, Utilization, and Storage (CCUS) technology, yet its safety is severely challenged by residual impurity gases (e.g., H₂O, O₂, SO₂, H₂S, and NO₂) from the capture process. This review systematically consolidates recent research advances, with the key findings being the following. Firstly, it reveals that the nonlinear synergistic effects among impurities are the primary cause of uncontrolled corrosion, whose destructive impact far exceeds the simple sum of individual effects. Secondly, it delineates the specific roles and critical thresholds of different impurities within the corrosion chain reaction, providing a theoretical basis for targeted control. Consequently, engineering management must enforce strict impurity concentration thresholds integrated with material upgrades and dynamic operational optimization. Future research should focus on developing multi-impurity reaction kinetic models, elucidating long-term corrosion product layer evolution, and establishing standardized experimental systems. This review provides crucial theoretical support for establishing impurity control standards and optimizing anti-corrosion strategies for the safe transport of CO₂ in supercritical CCUS pipelines. Full article

This study systematically investigates the synergistic modification effects of two high-modulus additives on SBS-modified asphalt through microstructural characterization and performance evaluation. Fluorescence microscopic analysis reveals that the additive particles undergo swelling over time and form an interconnected network structure via phase separation dynamics. Rheological tests demonstrate a significant enhancement in high-temperature performance: at the optimal dosage of 10 wt%, the complex modulus increases by approximately 215%, and the rutting factor improves by about 300% compared to the control group. The results from multiple stress creep recovery (MSCR) tests confirm the material’s superior elastic recovery capability and reduced non-recoverable creep compliance. However, the incorporation of the additives adversely affects low-temperature ductility. The penetration of (two distinct high-modulus agents, designated as HMA-A and HMA-B) HMA-B decreases by approximately 36.8% more than that of HMA-A, accompanied by significantly lower low-temperature toughness. A dosage of 10% is identified as the critical threshold, which maximizes rutting resistance while minimizing low-temperature performance degradation. Based on these findings, this paper proposes an integrated design paradigm of “microstructure–performance–dosage,” recommending HMA-B for high-stress pavement channels and HMA-A for regions with substantial temperature variations. Full article

Conflicting priorities between policymakers and local communities often compromise conservation outcomes in landscapes reliant on natural resources. Understanding how diverse stakeholders value ecosystem services (ESs) across coexisting land uses is essential; however, empirical evidence from rural Southeast Asia remains limited. This study examined ES perceptions and priorities among community members (n = 500) and experts (n = 30) within a bamboo forest, rice paddy, and teak plantation in Sangthong District, Lao PDR. A two-step survey methodology was employed: initially assessing ES perceptions to filter locally relevant services using a ≥50% recognition threshold, followed by quantifying priorities for this subset through a 100-point allocation task. The results revealed a systematic divergence in priorities rooted in differing knowledge systems. Communities, grounded in traditional ecological knowledge (TEK), prioritized tangible provisioning and cultural services (e.g., food and raw materials). In contrast, experts emphasized regulating services (e.g., carbon sequestration and hazard regulation) and habitat services (e.g., biodiversity and habitat provision). Distinct “ES bundles” also emerged by land use: bamboo (raw materials and freshwater), rice (food and medicine), and teak (timber/bioenergy and regulating services). Our findings suggest a policy transition from single-objective management toward optimizing landscape-level ES portfolios, alongside institutionalizing participatory co-management that formally integrates local knowledge and enhances ES literacy. Full article

This study developed a sustainable high-strength coal gangue backfill material for underground mining applications using coal gangue, fly ash, and cement as primary raw materials, with red mud (RM) as an alternative alkali activator. The mechanical properties of the backfill material were systematically optimized by adjusting coal gangue particle size and alkali activator dosage. The optimized formulation (coal gangue/fly ash/cement = 5:4:1, 3–6 mm coal gangue particle size, 5% RM, which named BF-6-5RM) achieved superior compressive strengths of 8.23 MPa (7 days) and 10.5 MPa (28 days), significantly exceeding conventional backfill requirements and outperforming a CaO-activated reference system (coal gangue/fly ash/cement = 5:4:1, 3–6 mm coal gangue particle size, 2% CaO, which named BF-6-2CaO). Microstructural and physicochemical analyses revealed that both formulations produced calcium silicate hydrate gels (C-S-H gels) and ettringite (AFt) as key hydration products, though BF-6-5RM exhibited a denser microstructure with well-developed ettringite networks and no detectable portlandite (CH), explaining its enhanced early-age strength. Environmental assessments confirmed effective heavy metal immobilization via encapsulation, adsorption, precipitation and substitution, except for arsenic (As), which exceeded Class III groundwater thresholds (DZ/T 0290-2015) due to elevated raw material content, displaying “surface wash-off, diffusion and depletion” leaching behavior. The findings confirm that red mud-based alkali activation is a viable technology for underground backfilling, provided it is coupled with arsenic control strategies like chemical stabilization or the selection of low-arsenic raw materials. This approach not only enables the resource utilization of hazardous industrial waste but also facilitates the production of backfill materials that combine both mechanical strength and environmental compatibility, thereby delivering dual economic and ecological benefits for sustainable mining practices. Full article

Background and Objectives: The COVID-19 pandemic has caused profound disruptions in socioeconomic, and health domains, with significant implications for mental well-being. The aim of this study was to evaluate the impact of the pandemic on stress, anxiety, and depression, alongside perceived social support, coping flexibility and related factors, and to examine how these issues vary across different age groups. Materials and Methods: A cross-sectional analytical study was conducted in Türkiye between August and December 2020. Data were collected through an online questionnaire including sociodemographic characteristics, pandemic-related concerns, and validated scales: Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Coping Flexibility Scale (CFS), and Multidimensional Scale of Perceived Social Support (MSPSS). Statistical analyses included descriptive and comparative tests, correlation analysis, multiple linear regression models, and correspondence analysis. Results: Among 1699 participants, 58.0% were female; 24.5% and 42.1% reported anxiety and depressive symptoms above thresholds, respectively. Younger age correlated negatively with stress, anxiety, and depression scores (p < 0.001). Feelings of loneliness, loss of control, ostracism, and sleep or concentration problems were positively associated with anxiety, depression, and stress, but negatively associated with coping flexibility and social support (p < 0.001). The 15–20 age group had the highest anxiety and depression levels and the lowest social support; the 15–30 group showed the highest stress, while the 61–75 group exhibited the lowest coping flexibility. Regression models explained 62.7% of anxiety and 56.6% of depressive symptom variances. Major predictors of anxiety included depressive symptoms, stress, and fear of dying from COVID-19, while depressive symptoms were predicted by age, stress, coping flexibility, social support, and anxiety. Conclusions: The findings highlight the considerable psychological burden and distinct vulnerabilities among age groups. Mental health interventions should be tailored according to age, emphasising the enhancement of social support and coping flexibility to strengthen resilience in future pandemics. Full article

Background and Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, has increasingly been recognized not only as a physical health concern but also as a potential risk factor for cognitive decline. This study investigates the application of machine learning algorithms to classify cognitive status based on Mini-Mental State Examination (MMSE) scores in community-dwelling sarcopenic women. Materials and Methods: A dataset of 67 participants was analyzed, with MMSE scores categorized into severe (≤17) and mild (>17) cognitive impairment. Eight classification models—MLP, CatBoost, LightGBM, XGBoost, Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and AdaBoost—were evaluated using a repeated holdout strategy over 100 iterations. Hyperparameter optimization was performed via Bayesian optimization, and model performance was assessed using metrics including weighted F1-score (w_f1), accuracy, precision, recall, PR-AUC, and ROC-AUC. Results: Among the models, CatBoost achieved the highest w_f1 (87.05 ± 2.85%) and ROC-AUC (90 ± 5.65%), while AdaBoost and GB showed superior PR-AUC scores (92.49% and 91.88%, respectively), indicating strong performance in handling class imbalance and threshold sensitivity. SHAP (SHapley Additive exPlanations) analysis revealed that moderate physical activity (moderatePA minutes), walking days, and sitting time were among the most influential features, with higher physical activity associated with reduced risk of cognitive impairment. Anthropometric factors such as age, BMI, and weight also contributed significantly. Conclusions: The results highlight the effectiveness of boosting-based models in capturing complex patterns in clinical data and provide interpretable evidence supporting the role of modifiable lifestyle factors in cognitive health. These findings suggest that machine learning, combined with explainable AI, can enhance risk assessment and inform targeted interventions for cognitive decline in older women. Full article