Search Results (2,942)

Background: The accuracy and consistency of complete-arch digital impressions are fundamental for long-term success of implant-supported rehabilitations. Recently, artificial intelligence (AI)-assisted tools, such as SmartX (Medit Link v3.4.2, MEDIT Corp., Seoul, South of Korea), have been introduced to enhance scan body recognition and data alignment during intraoral scanning. Objective: This in vitro study aimed to evaluate the impact of SmartX on impression accuracy, consistency, operator confidence, and technique sensitivity in complete-arch implant workflows. Methods: Seventy-two digital impressions were recorded on edentulous mandibular models with four dummy implants, using six experimental subgroups based on scan body design (double- or single-wing), scanning technique (occlusal or combined straight/zigzag), and presence/absence of SmartX tool. Each group was scanned by both an expert and a novice operator (n = 6 scans per subgroup). Root mean square (RMS) deviation and scanning time were assessed. Data were tested for normality (Shapiro–Wilk). Parametric tests (t-test, repeated measures ANOVA with Greenhouse–Geisser correction) or non-parametric equivalents (Mann–Whitney U, Friedman) were applied as appropriate. Post hoc comparisons used Tukey HSD or Dunn–Bonferroni tests (α = 0.05). Results: SmartX significantly improved consistency and operator confidence, especially among novices, although it did not yield statistically significant differences in scan accuracy (p > 0.05). The tool mitigated early scanning errors and reduced dependence on operator technique. SmartX also enabled successful library alignment with minimal data; however, scanning time was generally longer with its use, particularly for beginners. Conclusions: While SmartX did not directly enhance trueness, it substantially improved scan reliability and user experience in complete-arch workflows. Its ability to minimize technique sensitivity and improve reproducibility makes it a valuable aid in both training and clinical settings. Further clinical validation is warranted to support its integration into routine practice. Full article

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article

Saline-alkali stress seriously affects the growth and development of crops. Sorghum bicolor (L.), a C4 plant, is an important cereal crop in the world, and its growth and geographical distribution are limited by alkali conditions. In this study, sorghum genotypes with different alkaline resistance (alkaline-sensitive Z1 and alkaline-tolerant Z14) were used as experimental materials to explore the effects of alkali on sorghum seedlings. RNA-seq technology was used to examine the differentially expressed genes (DEGs) in alkali-tolerant Z14 to reveal the molecular mechanism of sorghum response to alkali stress. The results showed that plant height, root length, and biomass of both cultivars decreased with time under 80 mM NaHCO₃ treatment, but Z14 showed better water retention abilities. The photosynthetic fluorescence parameters and chlorophyll content also decreased, but the Fv/Fm, ETH, ΦPSII, and chlorophyll content of Z14 were significantly higher than those of Z1. The level of reactive oxygen species (ROS) increased in both sorghum varieties under alkali stress, while the enzyme activities of SOD, POD, CAT, and APX were also significantly increased, especially in Z14, resulting in lower ROS compared with Z1. Transcriptome analysis revealed around 6000 DEGs in Z14 sorghum seedlings under alkali stress, among which 267 DEGs were expressed in all comparison groups. KEGG pathways were enriched in the MAPK signaling pathway, plant hormone signal transduction, and RNA transport. bHLHs, ERFs, NACs, MYBs, and other transcription factor families are actively involved in the response to alkali stress. A large number of genes involved in photosynthesis and the antioxidant system were found to be significantly activated under alkali stress. In the stress signal transduction cascades, Ca²⁺ signal transduction pathway-related genes were activated, about 23 PP2Cs in ABA signaling were upregulated, and multiple MAPK and other kinase-related genes were triggered by alkali stress. These findings will help decipher the response mechanism of sorghum to alkali stress and improve its alkali tolerance. Full article

Schlieren imaging is a powerful optical sensing technique that captures flow-induced refractive index gradients, offering valuable visual data for analyzing complex fluid dynamics. However, the large volume and structural complexity of the data generated by this sensor pose significant challenges for extracting key physical insights and performing efficient reconstruction and temporal prediction. In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data. To address the limitations of traditional $\beta$-variational autoencoder ($\beta$-VAE) in capturing complex flow regions, the Progressive Frequency-enhanced Spatial Multi-scale Module (PFSM) is designed, which enhances the structures of different frequency bands through Fourier transform and multi-scale convolution; the Feature-Spatial Enhancement Module (FSEM) employs a gradient-driven spatial attention mechanism to extract key regional features. Experiments on flat plate film-cooled jet schlieren data show that SFS-VAE can effectively preserve the information of the mainstream region and more accurately capture the high-gradient features of the jet region, reducing the Root Mean Square Error (RMSE) by approximately 16.9% and increasing the Peak Signal-to-Noise Ratio (PSNR) by approximately 1.6 dB. Furthermore, when integrated with a Transformer for temporal prediction, the model exhibits significantly improved stability and accuracy in forecasting flow field evolution. Overall, the model’s physical interpretability and generalization ability make it a powerful new tool for advanced flow diagnostics through the robust analysis of Schlieren sensor data. Full article

Since the end of Apartheid in 1994, South Africa has striven to address past discrimination against members of marginalized groups such as Africans, women and LGBTQ+ individuals. Sophisticated media legislation and a vibrant civil society forged in the struggle against Apartheid ensure limited discrimination in traditional media and relatively fringe online forums. However, subtle forms of undermining signal the persistent legacy of a colonial and patriarchal past. While incidents of online racism and sexism are relatively well documented, ableism deserves more attention. Despite growing scholarship on digital discrimination, a significant research gap remains in understanding how ableist microaggressions manifest online, particularly when intersecting with race and gender. As a result of established media tropes, microaggressions against people with disabilities are somewhat naturalized and reproduced on social media, yet their intersectional dimensions—especially targeting Black women with disabilities—remain underexplored. This paper addresses this gap through a focused case study of jokes targeting Black women with disabilities in one TikTok video and the approximately 700 comments. Considering (dis)ability’s intersections with race, gender, and socio-economic status, these media texts are subjected to a critical thematic analysis. The study also problematizes the methodological challenges associated with finding, identifying, and purposively selecting such content. The analysis reveals a set of historically and contextually rooted microaggressions expressed through humor, which, as a cultural expression, is inherently covert and thus hard to detect and regulate. This research contributes to understanding how intersectional ableism operates digitally and highlights the need for nuanced approaches to identifying subtle forms of discrimination in online spaces. Full article

Cognition is often modeled in terms of abstract reasoning and neural computation, yet a growing body of theoretical and experimental work suggests that the roots of cognition lie in fundamental embodied regulatory processes. This article presents a theory of cognition grounded in sensing, feeling, and affect—capacities that precede neural systems and are observable in even the simplest living organisms. Based on the info-computational framework, this entry outlines how cognition and proto-subjectivity co-emerge in biological systems. Embodied appraisal—the system’s ability to evaluate internal and external conditions in terms of valence (positive/negative; good/bad)—and the capacity to regulate accordingly are described as mutually constitutive processes observable at the cellular level. This concept reframes cognition not as abstract symbolic reasoning but as value-sensitive, embodied information dynamics resulting from self-regulating engagement with the environment that spans scales from unicellular organisms to complex animals. In this context, information is physically instantiated, and computation is the dynamic, self-modifying process by which organisms regulate and organize themselves. Cognition thus emerges from the dynamic coupling of sensing, internal evaluation, and adaptive morphological (material shape-based) activity. Grounded in findings from developmental biology, bioelectric signaling, morphological computation, and basal cognition, this account situates intelligence as an affect-driven regulatory capacity intrinsic to biological life. While focused on biological systems, this framework also offers conceptual insights for developing more adaptive and embodied forms of artificial intelligence. Future experiments with minimal living systems or synthetic agents may help operationalize and test the proposed mechanisms of proto-subjectivity and affect regulation. Full article

This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R²) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R², demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value. Full article

This study focuses on improving the control system of vehicle suspension, which is critical for optimizing driving dynamics and enhancing passenger comfort. Traditional passive suspension systems are limited in their ability to effectively mitigate road-induced vibrations, often resulting in compromised ride quality and vehicle handling. To overcome these limitations, this work explores the application of active suspension control strategies aimed at improving both comfort and performance. Type-I and Type-II Fuzzy Logic Control (FLC) methods were designed and implemented to enhance vehicle stability and ride quality. The Harris Hawks Optimization (HHO) algorithm was employed to optimize the membership function parameters of both fuzzy control types. The system was tested under two distinct road disturbance inputs to evaluate performance. The designed control methods were evaluated in simulations where results demonstrated that the proposed active control approaches significantly outperformed the passive suspension system in terms of vibration reduction. Specifically, the Type-II FLC achieved a 54.7% reduction in vehicle body displacement and a 76.8% reduction in acceleration for the first road input, while improvements of 75.2% and 72.8% were recorded, respectively, for the second input. Performance was assessed using percentage-based metrics and Root Mean Square Error (RMSE) criteria. Numerical and graphical analyses of suspension deflection and tire deformation further confirm that the proposed control strategies substantially enhance both ride comfort and vehicle handling. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

Background/Objectives: The spinal cord is a critical component of the central nervous system that transmits neural signals between the brain and the body’s peripheral regions through its nerve roots. Despite being partially protected by the vertebral column, the spinal cord remains highly vulnerable to trauma, tumors, infections, and degenerative or inflammatory disorders. These conditions can disrupt neural conduction, resulting in severe functional impairments, such as paralysis, motor deficits, and sensory loss. Therefore, accurate and comprehensive spinal cord segmentation is essential for characterizing its structural features and evaluating neural integrity. Methods: In this study, we propose a fully automated method for segmentation of the cervical spinal cord in sagittal magnetic resonance (MR) images. This method facilitates rapid clinical evaluation and supports early diagnosis. Our approach uses a Swin-Unet architecture, which integrates vision transformer blocks into the U-Net framework. This enables the model to capture both local anatomical details and global contextual information. This design improves the delineation of the thin, curved, low-contrast cervical cord, resulting in more precise and robust segmentation. Results: In experimental studies, the proposed Swin-Unet model (SWU1), which uses transformer blocks in the encoder layer, achieved Dice Similarity Coefficient (DSC) and Hausdorff Distance 95 (HD95) scores of 0.9526 and 1.0707 mm, respectively, for cervical spinal cord segmentation. These results confirm that the model can consistently deliver precise, pixel-level delineations that are structurally accurate, which supports its reliability for clinical assessment. Conclusions: The attention-enhanced Swin-Unet architecture demonstrated high accuracy in segmenting thin and complex anatomical structures, such as the cervical spinal cord. Its ability to generalize with limited data highlights its potential for integration into clinical workflows to support diagnosis, monitoring, and treatment planning. Full article

This cross-sectional study analyzed the lower limb (LL) behavior in terms of gait asymmetry and joints’ kinematic parameters, comparing people with multiple sclerosis (pwMS) and unaffected individuals. Methods: Data from 15 patients, EDSS ≤ 4.5, and 15 healthy control volunteers were gathered. The VICON Motion Capture System (14 infrared cameras), NEXUS software, Plug-in–Gait skeleton model and reflective markers were used to collect data for each subject during five gait cycles on a plane surface. Biomechanical analysis included evaluation of LL joints’ range of motion (ROM) bilaterally, as well as movement symmetry. Results: Comparative biomechanical analysis revealed a hierarchy of vulnerability between the groups: the ankle is the most affected joint in pwMS (p = 0.008–0.014), the knee is moderately affected (p = 0.015 in swing phase), and the hip is the least affected (p > 0.05 in all phases). The swing phase showed the most significant left–right asymmetry impairment, as reflected by root mean square error (RMSE) values: swing-phase RMSE = 9.306 ± 4.635 (higher and more variable) versus stance-phase RMSE = 6.363 ± 2.306 (lower and more consistent). Conclusions: MS does not affect the joints structurally; rather, it eliminates the ability to differentiate the fine-tuning control between them. The absence of significant left–right joint asymmetry differences during complete gait cycle indicates dysfunction in the global motor control. Full article

The temporal dominance of sensations (TDS) and temporal liking (TL) methods offer complementary insights into the evolution of sensory and hedonic responses during food consumption. This study investigates the feasibility of predicting TL curves for food pairings from their TDS profiles using reservoir computing, a type of recurrent neural network. Participants evaluated eight samples—two crackers (plain, sesame), two spreads (peanut butter, strawberry jam), and their four binary combinations—performing both TDS and TL evaluations. This process yielded paired time-series data of TDS and TL curves. We trained various reservoir models under different conditions, including varying reservoir sizes (64, 128, 192, or 256 neurons) and the inclusion of auxiliary input dimensions, such as flags indicating the types of foods tasted. Our results show that models with minimal auxiliary inputs achieved the lowest root mean squared errors (RMSEs), with the best performance being an RMSE of 0.44 points on a 9-point liking scale between the observed and predicted TL curves. The ability to predict TL curves for food pairings holds some promise for reducing the need for extensive sensory evaluation, especially when a large number of food combinations are targeted. Full article

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

Continuous cropping obstacles (CCOs) lead to a decline in yield and quality under repeated cultivation in the same farmland. Notably, CCOs caused by fusarium wilt, autotoxicity, or imbalance in rhizosphere microbial communities reduce the productivity of watermelons (Citrullus lanatus). Considering the negative environmental impacts of conventional agrochemicals, it is necessary to evaluate the biocontrol efficiency of microorganisms. Therefore, this study aimed to investigate the biocontrol efficiency of Bacillus amyloliquefaciens strain PP19 against CCOs of watermelon so as to develop alternatives to agrochemicals. The inhibitory effect of PP19 on watermelon fusarium wilt was assessed through plate confrontation assays and field trials. The degradation and utilization of autotoxins by PP19 were examined via co-culture experiments. Additionally, 16S rRNA sequencing was employed to analyze the impact of PP19 on the rhizosphere soil microbial community of watermelon. Specifically, we analyzed the PP19 utilization of four phenolic autotoxins secreted by watermelon roots and assessed their effects on microbial diversity in the watermelon rhizosphere. Plant growth assays showed that PP19 improved the weight and quality of watermelon fruit. Although PP19 inhibited the growth of Fusarium oxysporum f. sp. niveum (Fon), the growth inhibitory effect was significantly enhanced by autotoxins produced by watermelon, including mixed phenolic, cinnamic, ferulic, and p-coumaric acids. Additionally, PP19 effectively degraded and utilized the autotoxins, and the autotoxins enhanced PP19’s swimming ability and biofilm formation. Moreover, PP19 treatment significantly enhanced the microbial diversity in watermelon rhizosphere, increased the number of beneficial bacterial genera, and decreased the number of pathogenic genera. Conclusively, these results suggest that B. amyloliquefaciens strain PP19 improves the resistance of watermelon to CCOs by effectively utilizing and degrading autotoxin, altering soil microbial community structure, and inhibiting Fon17 growth, resulting in improved fruit quality. Overall, PP19 possesses potential application as a biological control agent against CCOs in commercial watermelon cultivation. Full article

Background: The pathophysiology of Alzheimer’s disease (AD) is strongly linked to damage to the cholinergic systems of the central nervous system (CNS), mainly due to the formation of β-amyloid peptide plaques, which trigger intense inflammatory responses and are currently the main cause of the symptoms of the disease. Among the therapeutic strategies under investigation, classes of natural products with immunomodulatory properties, action on the CNS, and potent antioxidant activity, which contribute to neuroprotection, stand out. Methods: We aimed to evaluate the flavonoid quercetin using in silico, in vitro, and in vivo methods for the treatment of AD. Initially, the compounds were selected, and molecular dynamics simulations were performed. The in vitro assays included tests of antioxidant activity (DPPH), enzymatic inhibition of acetylcholinesterase (AChE), and prediction of oral toxicity. The in vivo studies investigated the effects on scopolamine-induced learning deficits and conducted histopathological analysis of the brain. Results: Quercetin showed structural stability in the complex with (AChE), with no significant alterations in the Root Mean Square Deviation (RMSD), SASA and radius of gyration (Rg) parameters. Through the same method it was possible to predict stability between the quercetin and inducible nitric oxide synthase (iNOS) complex, a possible mechanism for quercetin immunomodulation in the CNS. In the AChE inhibition test, the IC₅₀ obtained for quercetin was 59.15 μg mL⁻¹, while in the antioxidant test with DPPH, the concentration of 33.1 µM exhibited 50% of the scavenging of reactive oxygen species. This corroborates the perspective of quercetin having neuroprotective activity. This activity was also corroborated in vivo, in a zebrafish model, in which quercetin reduced the cognitive deficit induced by scopolamine. Histopathological analysis revealed its ability to prevent atrophy, caused by scopolamine, in the nervous tissue of animals, reinforcing the potential of quercetin as a neuroprotective agent. Conclusions: The results of the tests carried out with quercetin suggest that this molecule has antioxidant, AChE inhibitory, and neuroprotective activities, making it a good candidate for use in future clinical trials to ensure its efficacy and safety. Full article