Search Results (55,091)

This study aimed to evaluate the penetration and bonding performance of three restorative materials—high-viscosity glass ionomer cement (Riva Self Cure HV), resin-modified glass ionomer cement (Riva Light Cure) and a bioactive resin (Activa BioActive Restorative™)—in the healthy and carious dentine of permanent molars. Forty extracted human molars with sound or decayed dentine were restored following standardised protocols and subsequently divided into slices. So, twenty-four samples were used for each group (sound and carious dentine) for interface analysis using confocal laser scanning microscopy, field emission scanning electron microscopy and energy dispersive X-ray spectroscopy, and another eight simples were used for each group (sound and carious dentine) for Vickers microhardness testing. Results showed that both glass ionomer cements achieved consistent chemical bonding in healthy dentine and demonstrated better interfacial adaptation compared to carious dentine, where partially demineralised areas showed weaker bonding. The bioactive resin exhibited good adhesion in sound dentine due to the adhesive system but showed poorer interaction in decayed dentine with signs of interfacial separation. Elemental analysis revealed similar compositions among materials, with no significant differences in material concentrations among the ionomers, while there were significant differences with the other materials. On the other hand, some variations were observed in the sulphur, fluoride and strontium content depending on dentine condition. Microhardness values were higher in healthy dentine than in carious dentine for all materials (p < 0.001), except the high-viscosity glass ionomer, which maintained stable hardness in both substrates (36.33 ± 6.23 VHN vs. 34.56 ± 4.31 VHN; p = 0.605). These findings highlight the relevance of material selection and dentine condition in minimally invasive restorative dentistry. Full article

The production of alumina produces red mud (RM), a highly alkaline solid waste. The majority of it is disposed of in landfills, which seriously pollutes the environment. It needs to be recycled and handled with care to protect the environment. RM is a promising raw material for wastewater and waste gas treatment owing to its high alkalinity and abundant metal compounds. It can efficiently remove diverse pollutants while facilitating large-scale utilization of RM resources. Reviews of the use of RM resources to create catalysts for environmental governance are, nevertheless, scarce. Therefore, this paper analyzes and summarizes the pertinent research on RM-based catalysts to remove pollutants from the environment based on journal literature related to RM resource utilization from 2015 to 2025. This study reviews the application of RM-based catalysts for degrading pollutants in wastewater and exhaust gases via advanced oxidation processes (AOPs)—including photocatalysis, Fenton-like catalysis, ozonation catalysis, and persulfate catalysis—as well as catalytic oxidation, chemical looping combustion (CLC), and selective catalytic reduction (SCR). The paper emphasizes the analysis of modification strategies and catalytic mechanisms of RM-based catalysts in environmental remediation and examines the environmental risks and corresponding mitigation measures related to their preparation from RM resources. Finally, it outlines that future research should prioritize green, low-energy modification processes; catalytic systems for the synergistic removal of multiple pollutants; and efficient, recyclable separation and recovery technologies. These directions aim to promote the sustainable application of RM in large-scale environmental remediation and to achieve the integrated advancement of resource utilization and ecological protection. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

This paper describes a region-oriented method of pattern classification based on the Cartesian system model (CSM), a mathematical model that allows manipulating mixed feature-type symbolic data. We use the supervised hierarchical conceptual clustering to generate class regions for respective pattern class based on the evaluation of the generality of the regions and the separability of the regions against other classes in each clustering step. We can easily find the robustly informative features to describe each pattern class against other pattern classes. Some examples show the effectiveness of the proposed method. Full article

Biometric authentication using human physiological and behavioral characteristics has been widely adopted, with speaker verification attracting attention due to its convenience and noncontact nature. Conventional speaker verification systems remain vulnerable to spoofing attacks, however, often requiring integration with separate spoofed speech detection models. In this work, the authors propose an emotion-dependent speaker verification system that integrates speaker characteristics with emotional speech characteristics, enhancing robustness against spoofed speech without relying on additional classification models. By comparing acoustic characteristics of emotions between registered and verification speech using pretrained models, the proposed method reduces the equal error rate compared to conventional speaker verification systems, achieving an average equal error rate of 1.13% for speaker verification and 17.7% for the anti-spoofing task. Researchers additionally conducted a user evaluation experiment to assess the usability of emotion-dependent speaker verification. The results indicate that although emotion-dependent authentication was initially cognitively stressful, participants adapted over time, and the burden was significantly reduced after three sessions. Among the tested emotions (anger, joy, sadness, and neutral), sadness proved most effective, with stable scores, a low error rate, and minimal user strain. These findings suggest that neutral speech is not always the optimal choice for speaker verification and that well-designed emotion-dependent authentication can offer a practical and robust security solution. Full article

We present some numerical results on piston and tilt detection by using the Young experiment with Genetic Algorithms (GAs). We have simulated the cophasing of a flat surface by following the experimental setup and the mathematical model for Optical Path Difference (OPD) in the Young experiment to characterize piston and tip–tilt misalignment images in the order of a few nanometers, considering diffraction effects and random noise of 5%. Thus, the best fitness obtained by the genetic algorithm is considered as a determining factor to decide a complete error measurement because the proposed algorithm is capable of extracting the values of piston and tilt separately, regardless of which error is present or both. As a result, we have developed a study on piston detection from (0.001, 10) mm with a tilt present in the same pattern from (0, $\lambda /2$) by using GAs embedded in a computational application. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Constructing heterojunction photocatalysts with efficient interfacial charge transfer is critical for solar-driven hydrogen evolution. In this study, a highly dispersed MoS₂/g-C₃N₄ composite was successfully synthesized via a stirring-assisted hydrothermal in situ growth strategy. The introduction of stirring during synthesis significantly enhanced the uniform dispersion of MoS₂ nanosheets and exposed abundant edge sites, leading to well-integrated heterojunctions with enhanced interfacial contact. Comprehensive structural and photoelectronic characterizations (XRD, SEM, TEM, EDS mapping, UV–Vis, TRPL, EIS, EPR) confirmed that the composite exhibited improved visible-light absorption, accelerated charge separation, and suppressed recombination. Under simulated solar irradiation with triethanolamine (TEOA) as a sacrificial agent, the optimized 24% MoS₂/g-C₃N₄-S catalyst achieved a high hydrogen evolution rate of 14.33 mmol·g⁻¹·h⁻¹ at a catalyst loading of 3.2 mg, significantly outperforming the unstirred and pristine components, and demonstrating excellent cycling stability. Mechanistic studies revealed that the performance enhancement is attributed to the synergistic effects of Type-II heterojunction formation and edge-site-rich MoS₂ co-catalysis. This work provides a scalable approach for non-noble metal interface engineering and offers insight into the design of efficient and durable photocatalysts for solar hydrogen production. Full article

With the accelerating global energy transition, wind power has become a core pillar of renewable energy systems. However, its inherent intermittency and volatility pose significant challenges to the safe, stable, and economical operation of power grids—making ultra-short-term wind power prediction a critical technical link in optimizing grid scheduling and promoting large-scale wind power integration. Current forecasting techniques are plagued by problems like the inadequate representation of features, the poor separation of features, and the challenging clarity of deep learning models. This study introduces a method for the prediction of wind energy using spatiotemporal contrastive learning, employing seasonal trend decomposition to encapsulate the diverse characteristics of time series. A contrastive learning framework and a feature disentanglement loss function are employed to effectively decouple spatiotemporal features. Data on geographical positions are integrated to simulate spatial correlations, and a convolutional network of spatiotemporal graphs, integrated with a multi-head attention system, is crafted to improve the clarity. The proposed method is validated using operational data from two actual wind farms in Northwestern China. The research indicates that, compared with typical baselines (e.g., STGCN), this method reduces the RMSE by up to 38.47% and the MAE by up to 44.71% for ultra-short-term wind power prediction, markedly enhancing the prediction precision and offering a more efficient way to forecast wind power. Full article

Nanoparticle-embedded polymeric materials are an important subject in advanced structural applications due to their advantageous combination of low weight and high mechanical performance. Optical coherence tomography (OCT) is a high-resolution imaging technique that enables subsurface defect visualization, which can be used as one of the methods to reveal defects resulting from decomposition pathways or mechanisms of polymers. Nevertheless, the low contrast of polymeric materials, particularly PEEK-based polymers, does not allow for automatic geometry extraction for analytical input. To address the constraint of weak contrast, localized surface plasmon resonance (LSPR) of plasmonic nanoparticle-reinforced polymer materials has been used as an OCT contrast agent to provide the necessary contrast. The backscattering efficiency of light was also theoretically investigated, based on the Lorenz–Mie theory, with a single spherical nanoparticle embedded in a PEEK matrix as a non-absorptive, isotropic and homogeneous medium. In this study, the cases of a single homogeneous $Ti{O}_2$ nanoparticle and a hybrid $Ti{O}_2/Au$ core/shell nanoparticle configuration were considered separately. An examination of the influence of nanoparticle diameter and gold shell thickness on backscattering efficiencies of these nanostructures was performed. The results indicate that $Ti{O}_2/Au$ nanoshells demonstrate superior near-infrared (NIR) light backscattering capabilities at typical OCT operating wavelengths (830–1310 nm). Additionally, the potential of these nanoparticles for application in non-destructive testing-based light backscattering methods was investigated. The findings suggest that $Ti{O}_2/Au$ nanoshells have the ability to effectively backscatter near-infrared light in OCT operating central wavelengths, making them suitable to serve as effective NIR contrast-enhancing agents for OCT within the domain of NDT. Full article

Carbon dioxide (CO₂) released from natural or even anthropogenic sources may lead to an increase in the average global temperature and ultimately a climate shift. Thus, protecting the environment by reducing CO₂ concentration is a global concern. The recent approach of integrating CO₂ through capture, utilization, and storage seems to be an effective eradication technique. Even though a wide range of CO₂ capture strategies have been successfully adopted, there is an urgent need to compare these based on their advantages and weaknesses to define the development direction for future perspectives. Several materials have been used for capturing CO₂. Thus, we have elaborated and compared the current state of CO₂ capture strategies, including post-combustion CO₂ capture and direct air CO₂ capture. Strategies adopted under post-combustion CO₂ capture, including liquid- and solid-based sorbents, membrane-based separators, and electrochemical reduction, have been explained in detail, including their advantages, weaknesses, and potential risks. Thus, this review presents a thorough analysis of CO₂ capture in terms of material developments and manufacturing techniques, with some research gaps for future development. Full article

Exotic invasive plant species can cause biodiversity loss by outcompeting and replacing native species. Herbicides are commonly used to control invasive plants owing to their low cost and high efficiency. However, herbicide use can have unintended effects on co-occurring native plant species by altering the competitive balance. We studied how herbicide application modifies the competition between an invasive and a native species. We examined the effects of applying glyphosate on the mortality, photosynthetic capacity, and growth of Solidago canadensis, an aggressive invasive species, and Imperata cylindrica, a native species that commonly co-occurs with S. canadensis. We also studied how applying glyphosate affected the competition between these species. Various glyphosate concentrations were applied to the two species grown either together or separately. The mortality rate increased while the photosynthetic capacity and growth decreased with increasing glyphosate concentration. Increasing the glyphosate concentration more negatively affected the parameters of I. cylindrica than those of S. canadensis. Plant growth, especially that of I. cylindrica, was more restricted by intraspecific competition than by interspecific competition as the glyphosate concentration increased. Furthermore, the relative competitive potential of the native species decreased with increasing glyphosate concentration. S. canadensis is more tolerant of glyphosate, which enhances its competitive advantage and hinders the proliferation, reintroduction, and success of native plant species. Future studies should focus on developing techniques to mitigate the negative impacts of invasive plant species, for example, via optimizing methods of spraying herbicides. Full article

The magnetic properties of electrical steel sheets, crucial for efficient electrical machine performance, deteriorate through manufacturing processes. This study investigates the impact of different manufacturing steps on magnetization behavior and specific core losses in M270-50A electrical steel, and their influence on the performance of a reluctance synchronous machine (RSM). Magnetic measurements were conducted on three material states: laser-cut strips, assembled stator cores, and press-fitted stator cores. These were integrated into finite element analysis (FEA) models, including an extended two-region stator model that separates yoke and tooth regions to reflect different manufacturing effects. Simulations examined torque characteristics and flux linkage under various loading conditions and were validated using a prototype machine. The findings of magnetic measurements indicate that manufacturing-induced stresses significantly increase magnetization demand and core losses—up to 650% and 53%, respectively. These effects lead to a 4.2% reduction in maximum air gap torque and notable changes in torque characteristic curves and d-axis flux linkage maps. Including realistic magnetic data yielded torque predictions closely aligned with experimental results and reduced discrepancy in core loss simulation by more than 50%. The study’s findings indicate that accounting for manufacturing influences in material characterization enhances modeling accuracy and enables optimized electrical machine designs and control strategies. Full article

This manuscript introduces a river segmentation method and explores the impact of human interventions through a long-term study of total nitrogen, total phosphorus, chemical oxygen demand, and biochemical oxygen demand. An indicator linking parameter concentrations to the river’s flow rate was used to assess the development of the examined parameters. The analysis spanned from 2011 to 2022, considering both seasonal and yearly variations. Normal probability plots served as statistical tools to evaluate whether the data followed normal distributions and identify outliers. The proposed segmentation divided the Bahlui River into four segments, each defined by anthropogenic stressors. It was found that, due to human activity, each river segment could be viewed as an “independent” river. This supports the idea that river segments can be analyzed separately as distinct components. The proposed segmentation approach represents an alternative approach in river water quality research, moving from traditional continuous system models to fragmented system analysis, which better reflects the reality of heavily modified river systems. The study’s findings are important for understanding how anthropogenic modifications affect river ecosystem functioning in the long term. Full article

Phosphorite, or phosphate rock, has garnered increasing attention in recent years as a promising unconventional resource for rare earth elements (REEs). This paper presents a processing scheme aimed at recovering both REEs and phosphate values from amine flotation tailings generated during phosphate beneficiation in Florida. In these tailings, REEs are primarily present as monazite and xenotime, often associated with heavy minerals. The proposed flowsheet includes gravity separation to pre-concentrate REE- and phosphate-bearing minerals, followed by flotation to further upgrade both REEs and phosphate, and finally sulfuric acid leaching to extract REEs and phosphate from the flotation concentrate. Gravity separation using a shaking table increased the total REE content from approximately 202 ppm to 657 ppm, with a concentrate yield of 12.51%, REE recovery of around 41%, and P₂O₅ recovery of 33%. Fatty acid flotation of the shaking table concentrate produced a final concentrate containing 1106 ppm REEs and 14.90% P₂O₅, with recoveries of approximately 86% for REEs and 90% for P₂O₅. Subsequent pyrolysis with concentrated sulfuric acid followed by water leaching achieved recoveries of about 85% for REEs and 93% for P₂O₅. While the process demonstrated effective concentration and leaching of REE minerals and apatite, the major challenge to further improving separation and extraction efficiency lies in the fine-grained nature of the valuable minerals and their interlocking with gangue minerals. Full article