Search Results (214)

The growing application of 3D concrete printing (3DCP) in construction has raised important questions regarding its long-term durability under freeze–thaw (F–T) exposure, particularly in cold climates. This review paper presents a comprehensive examination of recent research focused on the F–T performance of 3D-printed concrete (3DPC). Key material and process parameters influencing durability, such as print orientation, admixtures, and layer bonding, are critically evaluated. Experimental findings from mechanical, microstructural, and imaging studies are discussed, highlighting anisotropic vulnerabilities and the potential of advanced additives like nanofillers and air-entraining agents. Notably, air-entraining agents (AEA) reduced the compressive strength loss by 1.4–5.3% after exposure to F–T cycles compared to control samples. Additionally, horizontally cored specimens with AEA incorporated into their mixture design showed a 15% higher dynamic modulus after up to 300 F–T cycles. Furthermore, optimized printing parameters, such as reduced nozzle standoff distance and minimized printing time gap, reduced surface scaling by over 50%. The addition of a nanofiller such as nano zinc oxide in 3DPC can result in compressive strength retention rates exceeding 95% even after aggressive F–T cycling. The lack of standard testing protocols and the geometry dependence of degradation are emphasized as key research gaps. This review provides insights into optimizing mix designs and printing strategies to improve the F–T resistance of 3DPC, aiming to support its reliable implementation in cold-region infrastructure. Full article

Streptococcus mutans (S. mutans) is a cariogenic bacterium in the oral cavity that plays a significant role in plaque formation and dental caries. In previous research by our group, we showed that the endocannabinoid anandamide (AEA) has anti-bacterial and anti-biofilm activities against S. mutans. Here, we aimed to investigate its effects on S. mutans through metabolomics analyses. S. mutans was cultivated in the absence or presence of AEA at a sub-minimum inhibitory concentration (MIC), and changes in metabolites and metabolic pathways were assessed through liquid chromatography–mass spectrometry (LC-MS). Treatment of S. mutans using AEA at 10 µg/mL significantly disturbed the glycolytic flux in the bacteria, which was indicated by a reduced glucose uptake into the cell, suppression of key glycolytic intermediates, reduced acid production into the media, imbalance of NAD⁺/NADH, and decreased adenosine triphosphate (ATP) production. The disruption of carbohydrate metabolism impacts critical cellular processes, including energy production, redox balance, and biosynthetic pathways, leading to metabolic stress and impaired cellular function. These results highlight the mode of action of AEA as an antimicrobial agent. Altogether, these findings suggest that AEA has potential as a novel antimicrobial agent in the development of therapeutics against S. mutans. Full article

Magnesium potassium phosphate cement (MKPC), a type of chemically bonded phosphate ceramic (CBPC), presents a promising alternative to ordinary Portland cement (OPC). This study focuses on developing sustainable MKPC (sust-MKPC) as a thermally passive material for building applications. A low-grade magnesium oxide (LG-MgO) industrial by-product was utilized to formulate sust-MKPC, with hydrogen peroxide employed as an air-entraining agent (AEA) to induce high porosity and enhance thermal insulation while supporting sustainability goals by reducing energy consumption in climate control systems. Seven formulations incorporating varying hydrogen peroxide contents (0, 1, 2, 3, 5, 7.5, and 10 wt.%) were prepared to evaluate the impact of AEA on the thermal and physicomechanical properties. Comprehensive characterization, including porosity and thermal conductivity measurements, revealed that increasing the AEA content significantly improved thermal inertia and lowered thermal conductivity due to porosity. However, this enhancement was accompanied by a marked reduction in mechanical strength and density, highlighting the trade-off between thermal performance and structural integrity in porous sust-MKPC formulations. Full article

The transition toward more electric aircraft (MEA) and all-electric aircraft (AEA) has driven the adoption of high-voltage DC (HVDC) electrical architectures to meet increasing power demands while reducing weight and enhancing overall efficiency. However, HVDC systems introduce new challenges, particularly concerning insulation reliability and the detection of in-flight series arc faults. This paper presents the design and evaluation of a capacitive sensor specifically developed to detect series arc faults in HVDC electrical systems for aerospace applications. A model of the sensor is proposed and validated through both simulations and experimental measurements using a step response test. The results show excellent agreement between the model and the physical setup. After validating the capacitive coupling value and its response to high-frequency signals, series arcs were generated in the laboratory to evaluate the sensor’s performance under realistic operating conditions, which involve different signal dynamics. The results are highly satisfactory and confirm the feasibility of using capacitive sensing for early arc detection, particularly aligned with the stringent requirements of more electric aircraft (MEA) and all-electric aircraft (AEA). The proposed sensor thus enables non-intrusive detection of series arc faults in compact, lightweight, and safety-critical environments. Full article

Objectives: To determine whether plasma concentrations of anandamide (AEA) and 2-arachidonoylglycerol (2-AG) are elevated in adults with prediabetes, we explored their association with tissue advanced glycation end-products (AGEs) and assessed the influence of Mediterranean diet adherence. Methods: This cross-sectional single-centre study included 92 adults with prediabetes and 86 age-/sex-matched normoglycaemic controls. Anthropometry, blood pressure, biochemical indices, and skin autofluorescence-derived AGEs were measured. Serum AEA and 2-AG were quantified by competitive ELISA, while Mediterranean diet adherence was assessed using the Mediterranean Diet Serving Score (MDSS). Results: Prediabetes was associated with higher AEA (p = 0.004) but not 2-AG (p = 0.520). Also, AEA correlated positively with AGE values (r = 0.36; p = 0.002) and increased across AGE-based cardiovascular risk categories. In multivariable models, both prediabetes status and AGE burden independently predicted AEA. Participants achieving MDSS ≥ 14 exhibited lower AEA (p = 0.038); 2-AG remained unaffected. Finally, the multivariable analysis confirmed that both prediabetes (β = 11.9; p = 0.005) and AGE values (β = 0.25; p = 0.003) are positively associated with plasma AEA levels, independent of age, sex, BMI, and fasting plasma glucose levels. Conclusions: Circulating AEA, but not 2-AG, is elevated in prediabetes and independently linked to cumulative AGE burden, suggesting early endocannabinoid activation contributes to cardiometabolic risk. High adherence to a Mediterranean diet may mitigate this dysregulation. Full article

The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from noise. Additionally, the large number of monitoring parameters (approximately 140) poses a challenge for spatiotemporal feature extraction, feature decoupling, and establishing a mapping relationship between high-dimensional monitoring parameters and leakage, rendering the precise quantitative estimation of evaporation tube leakage extremely difficult. To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. This framework processes temporal embeddings (LSTM-generated) via a dual-branch ACNN—where the 1D-CNN captures local spatial features and the attention models’ global significance—yielding decoupled representations that prevent cross-modal interference. This architecture is implemented in a simulated supercharged boiler, validated with datasets encompassing three operational conditions and 15 statuses in the supercharged boiler. The framework achieves an average diagnostic accuracy (ADA) of over 99%, an average estimation accuracy (AEA) exceeding 90%, and a maximum relative estimation error (MREE) of less than 20%. Even with a signal-to-noise ratio (SNR) of −4 dB, the ADA remains above 90%, while the AEA stays over 80%. This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage. Full article

Image segmentation is a foundation for autonomous driving frameworks that empower vehicles to explore and navigate their surrounding environment. It gives a fundamental setting to the dynamic cycles by dividing the image into significant parts like streets, vehicles, walkers, and traffic signs. Precise segmentation ensures safe navigation and the avoidance of collisions, while following the rules of traffic is very critical for seamless operation in self-driving cars. The most recent deep learning-based image segmentation models have demonstrated impressive performance in structured environments, yet they often fall short when applied to the complex and unpredictable conditions encountered in autonomous driving. This study proposes an Adaptive Ensemble Attention (AEA) mechanism within a Generative Adversarial Network architecture to deal with dynamic and complex driving conditions. The AEA integrates the features of self, spatial, and channel attention adaptively and powerfully changes the amount of each contribution as per input and context-oriented relevance. It does this by allowing the discriminator network in GAN to evaluate the segmentation mask created by the generator. This explains the difference between real and fake masks by considering a concatenated pair of an original image and its mask. The adversarial training will prompt the generator, via the discriminator, to mask out the image in such a way that the output aligns with the expected ground truth and is also very realistic. The exchange of information between the generator and discriminator improves the quality of the segmentation. In order to check the accuracy of the proposed method, the three widely used datasets BDD100K, Cityscapes, and KITTI were selected to calculate average IoU, where the value obtained was 89.46%, 89.02%, and 88.13% respectively. These outcomes emphasize the model’s effectiveness and consistency. Overall, it achieved a remarkable accuracy of 98.94% and AUC of 98.4%, indicating strong enhancements compared to the State-of-the-art (SOTA) models. Full article

Despite deep learning-based object detection techniques showing promising results, identifying items from low-quality images under unfavorable weather settings remains challenging because of balancing demands and overlooking useful latent information. On the other hand, YOLO is being developed for real-time object detection, addressing limitations of current models, which struggle with low accuracy and high resource requirements. To address these issues, we provide an Adaptive Enhancement Algorithm YOLO (AEA-YOLO) framework that allows for an enhancement in each image for improved detection capabilities. A lightweight Parameter Prediction Network (PPN) containing only six thousand parameters predicts scene-adaptive coefficients for a differentiable Image Enhancement Module (IEM), and the enhanced image is then processed by a standard YOLO detector, called the Detection Network (DN). Adaptively processing images in both favorable and unfavorable weather conditions is possible with our suggested method. Extremely encouraging experimental results compared with existing models show that our suggested approach achieves 7% and more than 12% in mean average precision (mAP) on the PASCAL VOC Foggy artificially degraded and the Real-world Task-driven Testing Set (RTTS) datasets. Moreover, our approach achieves good results compared with other state-of-the-art and adaptive domain models of object detection in normal and challenging environments. Full article

This study systematically investigates the influence of mix proportion on and the early-age properties and CO₂ uptake of CO₂-mixed cement paste, focusing on variations in the water-to-binder (w/b) ratio, slag content, and air-entraining agent (AEA) dosage. Mineralogical characteristics were analyzed using X-ray diffraction (XRD) and thermogravimetric analysis (TGA), while pore structures were assessed via nitrogen adsorption. CO₂ uptake was quantified immediately after mixing. Results indicate that a low w/b ratio limits CO₂ dissolution and transport, favors hydration over carbonation, and leads to a coarser pore structure. At moderate w/b ratios, excess free water facilitates concurrent carbonation and hydration; however, thinner water films ultimately hinder CaCO₃ precipitation and C-S-H nucleation. Slag contents up to 30% slightly suppress early carbonation and hydration, while higher dosages significantly delay both reactions and increase capillary porosity. An increasing AEA dosage stabilizes CO₂ bubbles, suppressing immediate CO₂ dissolution and reducing the early formation of carbonation and hydration products; excessive AEAs promotes bubble coalescence and results in an interconnected pore network. An optimized mix design, moderate water content, slag below 30%, and limited AEA dosage enhance the synergy between carbonation and hydration, improving early pore refinement and reaction kinetics. Full article

Background: The blood–brain barrier (BBB) plays a critical role in protecting the central nervous system (CNS) but also limits drug delivery. Insufficient knowledge of how the CNS promotes the onset and maintenance of peripheral neuropathic pain limits therapeutic methods for the treatment of persistent neuropathic pain. Thus, this study aimed to evaluate the ability of a novel combination of Palmitoylethanolamide (PEA) and Equisetum arvense L. (Equisetum A.L.) to cross the BBB and modulate nociceptive pathways. Methods: Using a humanised in vitro BBB tri-culture model, the permeability, cytotoxicity, and integrity of the barrier were assessed after exposure to two different PEA forms, PEA ultramicronized (PEA-um) and PEA80mesh, Equisetum A.L., and a combination of the last two samples. The samples exhibited no cytotoxicity, maintained tight junction integrity, and efficiently crossed the blood–brain barrier (BBB), with the combination displaying the highest permeability. The eluate from the BBB model was then used to stimulate the co-culture of CCF-STTG1 astrocytes and SH-SY5Y neurons pre-treated with H₂O₂ 200 µM. Results: Treatment with the combination significantly increased cell viability (1.8-fold, p < 0.05), reduced oxidative stress (2.5-fold, p < 0.05), and decreased pro-inflammatory cytokines (TNFα, IL-1β) compared to single agents. Mechanistic analysis revealed modulation of key targets involved in pain pathways, including decreased FAAH and NAAA activity, increased levels of endocannabinoids (AEA and 2-AG), upregulation of CB2 receptor expression, enhanced PPARα activity, and reduced phosphorylation of PKA and TRPV1. Conclusions: These findings suggest that the combination of PEA and Equisetum A.L. effectively crosses the BBB and exerts combined anti-inflammatory and analgesic effects at the CNS level, suggesting a possible role in modulating neuroinflammatory and nociception responses. Full article

Adolescent binge drinking has lasting behavioral consequences by disrupting the endocannabinoid system (ECS) and depleting brain omega-3. The natural accumulation of omega-3 fatty acids in cell membranes is crucial for maintaining the membrane structure, supporting interactions with the ECS, and restoring synaptic plasticity and cognition impaired by prenatal ethanol (EtOH) exposure. However, it remains unclear whether omega-3 supplementation can mitigate the long-term effects on the ECS, endocannabinoid-dependent synaptic plasticity, and cognition following adolescent binge drinking. Here, we demonstrated that omega-3 supplementation during EtOH withdrawal increases CB1 receptors in hippocampal presynaptic terminals of male mice, along with the recovery of receptor-stimulated [³⁵S]GTPγS binding to Gαi/o proteins. These changes are associated with long-term potentiation (LTP) at excitatory medial perforant path (MPP) synapses in the dentate gyrus (DG), which depends on anandamide (AEA), transient receptor potential vanilloid 1 (TRPV1), and N-methyl-D-aspartate (NMDA) receptors. Finally, omega-3 intake following binge drinking reduced the time and number of errors required to locate the escape box in the Barnes maze test. Collectively, these findings suggest that omega-3 supplementation restores Barnes maze performance to levels comparable to those of control mice after adolescent binge drinking. This recovery is likely mediated by modulation of the hippocampal ECS, enhancing endocannabinoid-dependent excitatory synaptic plasticity. Full article

The anandamide (AEA) and lipopolysaccharide (LPS) interaction is gaining attention, but evidence on the influence of probiotics on endocannabinoid system (ECS) biomarkers remains limited. This study (NCT05567653) investigated the effects of 12-week supplementation with Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on AEA (main outcome) and inflammatory biomarkers in female dancers. Fifteen participants (5 probiotic, 10 placebo) were included in the final analysis. Serum levels of AEA, LPS, and cytokines (tumor necrosis factor-alpha—TNF-α, interleukin-1 beta—IL-1β, and interleukin-10—IL-10) were measured using an ELISA (enzyme-linked immunosorbent assay), and the psychological stress responses were evaluated using the Mini-COPE questionnaire. At the baseline, a correlation between AEA and LPS was observed (Spearman’s r = 0.9677, p < 0.05). After 12 weeks, no statistically significant differences in the AEA, LPS, cytokine levels, or stress-coping strategies were observed between the probiotic and placebo groups (LPS–probiotic: +3.48 EU/L, p = 0.9361; placebo: +56.98 EU/L, p = 0.0694; AEA–probiotic: −1.11 ng/mL, p = 0.9538; placebo: +14.08 ng/mL, p = 0.4749). The direction of change may indicate a trend toward increased inflammation in the absence of probiotics, consistent with patterns described in previous literature. However, these results should be viewed as hypothesis generating and warrant confirmation in larger trials. Full article

The paraventricular nucleus of the hypothalamus (PVN) regulates, among others, the stress response, sexual behavior, and energy metabolism through its magnocellular and parvocellular neurosecretory cells. Within the PVN, ensemble coordination occurs through the many long-range synaptic afferents, whose activity in time relies on retrograde neuromodulation by, e.g., endocannabinoids. However, the nanoarchitecture of endocannabinoid signaling in the PVN, especially during neuronal development, remains undescribed. By using single-cell RNA sequencing, in situ hybridization, and immunohistochemistry during fetal and postnatal development in mice, we present a spatiotemporal map of both the 2-arachidonoylglycerol (2-AG) and anandamide (AEA) signaling cassettes, with a focus on receptors and metabolic enzymes, in both molecularly defined neurons and astrocytes. We find type 1 cannabinoid receptors (Cnr1), but neither Cnr2 nor Gpr55, expressed in neurons of the PVN. Dagla and Daglb, which encode the enzymes synthesizing 2-AG, were found in all neuronal subtypes of the PVN, with a developmental switch from Daglb to Dagla. Mgll, which encodes an enzyme degrading 2-AG, was only found sporadically. Napepld and Faah, encoding enzymes that synthesize and degrade AEA, respectively, were sparsely expressed in neurons throughout development. Notably, astrocytes expressed Mgll and both Dagl isoforms. In contrast, mRNA for any of the three major cannabinoid-receptor subtypes could not be detected. Immunohistochemistry validated mRNA expression and suggested that endocannabinoid signaling is configured to modulate the activity of afferent inputs, rather than local neurocircuits, in the PVN. Full article

In the context of global energy scarcity, thermal insulation castables have garnered significant attention from the steel industry to reduce energy consumption. To optimize the performance of calcium hexaaluminate (CA₆)-based insulating castables, a systematic comparative study was conducted on the influence of varying amounts of calcium aluminate cement (CAC) incorporated into the castables. The results indicated that the addition of more CAC could increase the initial flowability of the castables with an air-entraining agent (AEA). Conversely, the flowability of the castables containing alumina bubbles continuously decreased after 30 min and 60 min. The apparent porosity of castables with only added AEA and alumina bubbles after being dried at 110 °C and treated at 1300 °C presented a decreasing trend as CAC content increased. Under the joint action of AEA and alumina bubbles, the amplification in porosity of castables treated at 1300 °C was positively correlated with the amount of CAC. The increase in CAC content could enhance the strength of samples, with a particularly notable improvement observed in castables prepared with the addition of AEA. For castables prepared with AEA and CAC contents of 9 wt.%, the cold modulus of rupture and cold crushing strength after heat treatment at 1300 °C were 17.5 MPa and 80.5 MPa, respectively. The thermal conductivity of castables presented non-monotonic change with the increase in CAC content. The effect of elevated CAC content on the pore fractal dimension of castables depended on the pore-forming methods. Grey correlation analysis (GCA) demonstrated that pore sizes in the range of 500–1000 nm, pore fractal dimensions, and pore sizes less than 500 nm had the highest degrees of correlation with CMOR, CCS, and thermal conductivity, respectively. Full article

Climate-change-induced extreme environments exacerbate pavement degradation in arid regions, where traditional concrete incurs 23~40% higher life-cycle costs due to premature cracking. Particularly in the Gobi Desert, concrete pavements suffer from conflicting performance requirements—high flexural-to-compressive strength ratio (R_f/R_c), low shrinkage, and controlled porosity—with traditional design methods failing to address multi-objective trade-offs. Existing optimization methods have proven insufficient for such complex environments, with conventional approaches addressing only individual parameters or employing subjective weighting techniques that fail to capture the interrelated nature of critical performance indicators. This study develops an integrated optimization framework combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Criteria Importance Through Intercriteria Correlation (CRITIC) weighting, and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) decision-making to optimize the mix proportions water–cement ratio (W/C), sand ratio, and an air-entraining agent (AEA) for sustainable pavement concrete. Response Surface Methodology (RSM) analysis via Box–Behnken design revealed distinct parameter dominance: AEA exhibited the strongest non-linear effects on R_f/R_c and porosity, while W/C primarily governed shrinkage. NSGA-III generated 73 Pareto-optimal solutions, with CRITIC selecting an optimal mix (W/C = 0.35), sand ratio = 36%, AEA = 0.200%) validated experimentally (R_f/R_c = 0.141), shrinkage = 0.0446%, porosity = 2.82%. Microstructural characterization using scanning electron microscopy and low-field nuclear magnetic resonance (SEM/LF-NMR) demonstrated refined pore distribution and enhanced compactness. This framework effectively resolves trade-offs between performance indicators, providing a scientifically robust method for designing durable pavement concrete that reduces shrinkage by 13.0% and porosity by 13.5% compared to conventional mixes, lowering maintenance costs in arid regions. Full article