Search Results (42,249)

Humanoid soccer robots operate in dynamic, unpredictable, and often partially observable settings. Effective teamwork, sound decision-making, and real-time collaboration are critical to competitive performance. In this paper, a biologically inspired swarm-intelligence framework for humanoid soccer is proposed, comprising (1) a low-overhead communication User Datagram Protocol (UDP) optimized for minimal bandwidth and graceful degradation under packet loss; (2) an Ant Colony Optimization (ACO)-based decentralized role allocation mechanism that dynamically assigns attackers, midfielders, and defenders based on real-time pheromone trails and local fitness metrics; (3) a Reynolds’ flocking-based formation control scheme, modulated by role-specific weighting to ensure fluid transitions between offensive and defensive formations; and (4) an adaptive behavior layer integrating lightweight reinforcement signals and proactive failure-recovery strategies to maintain cohesion under robot dropouts. Simulations demonstrate a 25–40% increase in goals scored and an 8–10% boost in average ball possession compared to centralized baselines. Full article

Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices. Full article

Photoacoustic (PA) imaging (PAI) holds great promise for non-invasive biomedical diagnostics. However, the efficacy of current contrast agents is often limited by photobleaching, toxicity, and complex synthesis processes. In this study, we introduce a novel, biocompatible PAI contrast agent: a recombinant water-soluble chlorophyll-binding protein (WSCP) from Lepidium virginicum (LvP) reconstituted with chlorophyll a (LvP-chla). LvP-chla exhibits a strong and narrow absorption peak at 665 nm, with a molar extinction coefficient substantially higher than oxyhemoglobin and deoxyhemoglobin, enabling robust signal generation orthogonal to endogenous chromophores. Phantom studies confirmed a linear relationship between PA signal amplitude and LvP-chla concentration, demonstrating its stability and reliability. In vitro cytotoxicity testing using 4T1 cells showed high cell viability at 5 mg/mL, justifying its use for in vivo studies. In vivo experiments with a 4T1 tumor-bearing mouse model demonstrated successful tumor localization following intratumoral injection of LvP-chla, with clear visualization via spectroscopic differentiation from endogenous absorbers at 665 nm and 685 nm. Toxicity assessments, both in vitro and in vivo, revealed no adverse effects, and clearance studies confirmed minimal retention after 96 h. These findings show that LvP-chla is a promising contrast agent that enhances PAI capabilities through its straightforward synthesis, stability, and biocompatibility. Full article

Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. Full article

(1) Background: Osteonecrosis of the femoral head (ONFH), caused by insufficient blood supply, leads to bone tissue death. Current treatments lack effective bone regeneration materials to reverse disease progression. This study introduces an injectable and self-setting 3D porous bioceramic scaffold (Mg@Ca), combining MgO + SiO₂ mixtures with α-hemihydrate calcium sulfate, designed to promote bone repair through in situ pore formation and osteoinduction. (2) Methods: In vitro experiments evaluated human bone marrow mesenchymal stem cell (h-BMSC) proliferation, differentiation, and osteogenic marker expression in Mg@Ca medium. Transcriptome sequencing identified bone development-related pathways. In vivo efficacy was assessed in a rabbit model of ONFH to evaluate bone repair. (3) Results: The Mg@Ca scaffold demonstrated excellent biocompatibility and supported h-BMSC proliferation and differentiation, with significant up-regulation of COL1A1 and BGLAP. Transcriptome analysis revealed activation of the PI3K-Akt signaling pathway, critical for osteogenesis. In vivo results confirmed enhanced trabecular density and bone volume compared to controls, indicating effective bone repair and regeneration. (4) Conclusions: The Mg@Ca scaffold offers a promising therapeutic approach for ONFH, providing a minimally invasive solution for bone defect repair while stimulating natural bone regeneration. Its injectable and self-setting properties ensure precise filling of bone defects, making it suitable for clinical applications. Full article

The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mechanical model, developed in COMSOL 6.2 Multiphysics, simulates the interaction between a robotic glove and a deformable latex sphere. The latex material is described using a two-parameter Mooney–Rivlin hyperelastic formulation to capture large nonlinear deformations under realistic contact conditions. The high-fidelity simulation data are used to validate the signal acquisition chain and to train a supervised AI algorithm capable of classifying rehabilitation progress—whether improving or worsening—based on biomechanical features. An integrated electronic prototype enables seamless data flow to a cloud-based monitoring platform, supporting real-time feedback and adaptability. The classification algorithm demonstrates robust performance across different test conditions, while the electronic system confirms its applicability in rehabilitation settings. The novelty of this paper lies in the closed-loop integration of FEM-based simulation, AI-driven analysis, and embedded electronics into a unified monitoring architecture. This intelligent and non-invasive approach provides a scalable tool for tracking motor recovery and enhancing therapy effectiveness through adaptive, feedback-driven interventions. Full article

The genus Trichoderma comprises a group of fungi known for their beneficial effects on plant growth and stress tolerance. Light is a key environmental factor affecting many plant physiological processes. However, a significant research gap remains regarding the interaction between light quality and Trichoderma harzianum inoculation, particularly their combined effects on tomato plant growth and photosynthetic efficiency. Here, we showed that T. harzianum inoculation effectively alleviated the growth inhibition caused by monochromatic red light or blue light in tomato plants. Combined red and blue light treatment with T. harzianum inoculation (RBT) promoted root development by regulating the rational distribution of carbon assimilation products. Specifically, the RBT treatment upregulated the expression of photosynthesis-related genes, including key Calvin cycle enzyme genes such as FBPase, FBPA, TPI, and SBPase, as well as the light signal transduction factor HY5. In addition, T. harzianum inoculation increased the maximal photochemical efficiency of PSII (Fv/Fm), and the net photosynthetic rate (Pn). The activity of sucrose synthetase (SS) and sucrose phosphate synthetase (SPS) was also enhanced, promoting photosynthetic product accumulation in leaves and roots. Among all treatment groups, RBT performed the best in the above indexes. Full article

Background/Objectives: CD47 is a cell surface glycoprotein moderately expressed in healthy cells and upregulated in cancer and viral infected cells. CD47’s interaction with signal regulatory protein alpha (SIRPα) inhibits phagocytic cells and its interaction with thrombospondin-1 inhibits T cell response. Experimental evidence has revealed that the blockade of CD47 resulted in the increased activation and function of both innate and adaptive immune cells, therefore exerting antitumoral and antiviral effects. Recent studies have shown that the combination of vaccines and immune checkpoint inhibitors could be a promising approach to increasing vaccine immunogenicity. Here, we investigated the vaccinal effect of anti-CD47 antibodies and discussed the possibilities of combining anti-CD47 treatments with vaccines. Methods: Using vesicular stomatitis virus (VSV), a widely used replication-competent vaccine vector, we evaluated the impact of the immunotherapeutic blockade of CD47 on cellular, humoral, and protective immunity. We infected C57BL/6 mice with VSV, treated them with anti-CD47 antibodies or an isotype, and evaluated the total immunoglobulin (Ig), IgG neutralizing antibodies, B cell activation, CD8+ T cell effector function, and survival of the mice. Results: We found that the treatments of anti-CD47 antibodies led to significantly increased Ig and IgG neutralizing antibody levels compared to the isotype treatment. Flow cytometric analysis of B cells revealed no difference in the number of circulating B cells; however, we observed an increased surface expression of CD80 and CD86 in B cells among anti-CD47-treated mice. Further analysis of the impact of CD47 blockade on T immunity revealed a significantly higher percentage of IFN-γ⁺ CD4 and IFN-γ⁺ CD8 T cells in anti-CD47-treated mice. Upon infecting mice with a lethal VSV dose, we observed a significantly higher survival rate among the anti-CD47-treated mice compared to control mice. Conclusions: Our results indicate that anti-CD47 treatment induces a stronger cellular and humoral immune response, leading to better protection. As such, immunotherapy by CD47 blockade in combination with vaccines could be a promising approach to improve vaccine efficacy. Full article

Background: Understanding the impact of different learning materials in terms of comprehension and engagement is essential for optimizing educational strategies. While digital learning tools are increasingly used, offering and multiplying different educational solutions, their effects on learners’ mental workload, attention, and engagement remain underexplored. This study aims to investigate how different types of learning content—educational videos, academic videos, and text reading—affect cognitive processing and engagement. Methods: Neurophysiological signals, including electroencephalography (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were recorded from experimental participants while they were engaged with each learning content. Subjective assessments of cognitive effort and engagement, together with a quiz to assess the knowledge acquisition, were collected through questionnaires for each tested content. Key neurophysiological metrics, such as engagement and Human Distraction Index (HDI), were computed and compared across conditions. Results: Our findings indicate that video-based learning materials, particularly educational videos with visual enhancements, elicited higher engagement and lower cognitive load compared to text-based learning. The text reading condition was associated with increased mental workload and a higher distraction index, suggesting greater cognitive demands. Correlation analyses confirmed strong associations between neurophysiological indicators and subjective evaluations. Conclusions: The results highlight the potential of neurophysiological measures to objectively assess learning experiences, paving the way for designing more effective and engaging learning platforms. Full article

Heat stress (HS), an important environmental stressor for healthy poultry farming, has been shown to have a detrimental effect on production performance and induce serious diseases through immune system damage. As the avian peripheral immune system’s primary organ, spleen is subject to complex biological processes in response to HS injury. Histopathological characterization demonstrated that HS resulted in the destruction of the splenic red and white medulla, a decrease in cell density and organ atrophy. These changes directly impaired pathogen clearance and immune surveillance. At the physiological level, the impact of HS is characterized by disrupted metabolic homeostasis through interrupting neuroendocrine function. This, in turn, results in a significant suppression of humoral immune response. The oxidative-inflammatory cascade constitutes the core pathology of this disease. Energy metabolism disorder triggered by mitochondrial dysfunction and redox imbalance form a vicious circle, which promotes apoptosis signaling cascade. Meanwhile, over-activation of intrinsic immune system triggers a series of inflammatory factors, which further amplifies effects of tissue damage. The present prevention and control strategies are centered on synergistic anti-inflammatory and antioxidant interventions with nutrient modulators and plant actives. Nevertheless, it is imperative for future studies to incorporate multi-omics technologies in order to analyze the metabolic mechanisms and patterns of stress and establish a precise intervention strategy based on immune homeostatic regulation. This review systematically investigated the multilevel regulatory mechanisms of HS-induced spleen injury, which provides a theoretical basis for the mechanistic analysis and technological innovation of the prevention and control of HS syndrome in poultry. Full article

Endocannabinoids, acting primarily through CB1 receptors, are critical modulators of neuronal activity, influencing cognitive functions and emotional processing. CB1 receptors are highly expressed in the hippocampus, primarily on GABAergic interneurons, modulating the excitation/inhibition balance. Previous evidence suggests the functional heterogeneity of CB1 receptors along the dorsoventral axis of the hippocampus. However, it is not known whether CB1 receptors differentially modulate basic aspects of the local neuronal network along the hippocampus. This study investigated how CB1 receptor activation modulates excitability, paired-pulse inhibition (PPI), and short-term neuronal dynamics (STND) in the dorsal and ventral CA1 hippocampus under physiologically relevant conditions. Using extracellular recordings from hippocampal slices of male Wistar rats, we compared the effects of two CB1 receptor agonists, ACEA and WIN55,212-2, on network activity in the dorsal and ventral hippocampus. We found that both agonists significantly increased excitability and reduced PPI in the dorsal, but not the ventral, hippocampus. Similarly, CB1 receptor activation modulated STND more prominently in the dorsal hippocampus, reducing facilitation at low frequencies and reversing depression at high frequencies, whereas effects on the ventral region were minimal. These dorsoventral differences in the actions of cannabinoid receptor agonists occurred despite similar CB1 receptor expression levels in both regions, suggesting that functional differences arise from downstream mechanisms rather than receptor density. Pre-application of the GIRK channel blocker Tertiapin-Q occluded the effects of WIN55,212-2 on STND, indicating a significant role of GIRK channel-mediated signaling in CB1 receptor actions. These findings demonstrate that CB1 receptors modulate hippocampal circuitry in a region-specific manner, with the dorsal hippocampus being more sensitive to cannabinoid signaling, likely through differential engagement of intracellular signaling pathways such as GIRK channel activation. These results provide novel insights into how endocannabinoid signaling differentially regulates neuronal dynamics along the dorsoventral axis of the hippocampus. They also have important implications for understanding the role of cannabinoids in hippocampus-dependent behaviors. Full article

In industrial scenarios, bearing fault diagnosis often suffers from data scarcity and class imbalance, which significantly hinders the generalization performance of data-driven models. While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). Specifically, raw vibration signals are transformed into 2D RGB representations via joint time-domain, frequency-domain, and time–frequency-domain mappings, effectively encoding multi-perspective fault signatures. A Transformer-based feature extractor, integrated with Efficient Channel Attention (ECA), is embedded into both the generator and discriminator to capture global dependencies and channel-wise interactions, thereby enhancing the representation quality of synthetic samples. Furthermore, a gradient penalty (GP) term is introduced to stabilize adversarial training and suppress mode collapse. To improve classification performance, an Enhanced Hybrid Visual Transformer (EH-ViT) is constructed by coupling a lightweight convolutional stem with a ViT encoder, enabling robust and discriminative fault identification. Beyond performance metrics, this work also incorporates a Grad-CAM-based interpretability scheme to visualize hierarchical feature activation patterns within the discriminator, providing transparent insight into the model’s decision-making rationale across different fault types. Extensive experiments on the CWRU and Jiangnan University (JNU) bearing datasets validate that the proposed method achieves superior diagnostic accuracy, robustness under limited and imbalanced conditions, and enhanced interpretability compared to existing state-of-the-art approaches. Full article

Physiological oxidative stress plays a pivotal role in supporting proper growth and development. While moderate oxidative stress is essential for activating key metabolic pathways and maintaining normal cellular signaling, excessive production of reactive oxygen species (ROSs) can overwhelm the immature antioxidant systems of newborns, potentially leading to cellular damage and impaired physiological function. This vulnerability is particularly pronounced in the central nervous system, where limited detoxification capacity exacerbates the risk of oxidative damage, following hypoxic–ischemic events. Antioxidants agents—such as melatonin, erythropoietin, allopurinol, N-acetylcisteine, selenium, iminobiotin, taurine, and acetyl-L-carnitine—have demonstrated significant neuroprotective effects in preclinical experimental studies, reducing markers of oxidative injury and improving neurological outcomes. These neuroprotective agents have also been evaluated in clinical trials, demonstrating antioxidant effects. A major issue lies in the complexity of neurological damage, which is not associated with a single pathological pathway. Additionally, the inability of these agents to reach effective concentrations within the central nervous system, along with inconsistencies across clinical trials in terms of dosage and administration methods, hinders the ability to obtain robust results. Future efforts should therefore focus on the development of delivery systems capable of crossing the blood–brain barrier and on establishing standardized clinical trial protocols and study designs. This educational review aims to provide a comprehensive overview of emerging protective strategies, including antioxidant bioactive agents and nutritional interventions. It also explores the underlying mechanisms of oxidative stress and its impact on neonatal brain injury. Full article

The bilateral interaction between the brain and the gut has recently been on the spectrum of researchers’ interests, including complex neural, endocrinological, and immunological signaling pathways. The first case reports and clinical studies have already reported that delivering microbes through fecal microbial transplantation (FMT) may alleviate symptoms of psychiatric disorders. Therefore, modifying the gut microbiota through FMT holds promise as a potential treatment for psychiatric diseases. This scoping review assessed studies from PubMed related to FMT in autism spectrum disorder and attention deficit hyperactivity disorder. The evaluation included nine clinical studies and case reports. The beneficial and persistent effect on the autism spectrum disorder (ASD) symptoms has been reported. Also, an increased microflora diversity and altered levels of neurometabolites in serum were identified, albeit with a tendency to return to baseline over time. The microbiome–gut–brain axis could provide new targets for preventing and treating psychiatric disorders. However, a recent large randomized clinical trial has shed light on the previously collected data and suggested a possible contribution of the placebo effect. This highlights the necessity of large randomized double-blind studies to reliably assess the effect of FMT in ASD. Full article

Monitoring the blast furnace shaft static pressure is crucial for maintaining a stable ironmaking process. Traditional rule-based methods and manual inspections suffer from high labor costs and inconsistent standards. This article proposes a new unsupervised anomaly detection framework that combines adversarial autoencoder with variational mode decomposition (VMD). Firstly, using VMD combined with sample entropy calculation and clustering algorithm, the trend, period, and other components of multidimensional signals are extracted, and then these components are integrated into an improved adversarial training autoencoder to detect global and local anomalies. The proposed method has an accuracy of 0.95, a recall rate of 0.91, and an F1 score of 0.93. Which demonstrates the method effectively captures multi-scale anomalies including value bias, morphological changes, and sudden fluctuations, while providing analysts with interpretable anomaly detail diagnosis. Full article