Search Results (24,236)

Schinus terebinthifolia Raddi. leaf lectin (SteLL) has been investigated for its neuromodulatory effects. Given the etiological diversity of depression, this study evaluated the effects of SteLL in a pharmacological model induced by reserpine. Mice were administered reserpine intraperitoneally for 10 days to induce anxiety- and depression-like symptoms. Before reserpine administration, animals also received SteLL (2 or 4 mg/kg, i.p.) or fluoxetine (10 mg/kg, i.p.) for 10 days. Behavioral assessments included the open field test, elevated plus maze, and tail suspension test. Body weight variation and brain levels of cytokines, noradrenaline, dopamine, and serotonin were also analyzed. In reserpine-treated mice, SteLL administration (2 and 4 mg/kg) produced anxiolytic-like effects in the open field (reduced number of rearings) and elevated plus maze (increased time spent in open arms) and significantly reduced immobility time in the tail suspension test. Additionally, SteLL prevented the body weight loss typically induced by reserpine. SteLL treatment modulated neuroinflammation by reducing IL-2 and increasing IL-10 levels in the brain. SteLL treatment restored dopaminergic and noradrenergic levels, with no effect on serotonin. In conclusion, SteLL was effective in reserpine-induced monoaminergic depletion, reversing behavioral and biochemical alterations characteristic of depression, likely through dopaminergic, noradrenergic, and anti-inflammatory mechanisms. Full article

Electric vehicle (EV) battery thermal management systems have gradually improved owing to the increasing power demand of EVs. This study aims to optimize the channel geometry of sandwich double-sided cold plates for EV battery cooling under 100% state of charge and 2C-rate charging conditions. For precise and accurate optimization, the conventional one-dimensional analysis model of the sandwich double-sided cold plate was converted into a three-dimensional computational fluid dynamics (CFD) model. Non-dimensional parameters were selected as the main variables of the channel geometry, and nine additional channel shapes were derived based on them. Battery modules with the derived channel shapes were subjected to CFD analysis in the Reynolds number range of 500 to 20,000. The goodness factor was calculated from these correlations, and optimization was performed using the Taguchi method. The results revealed that the wetted area of the channel had a greater impact on battery cooling than the number of channels. This study proposed more generalized design guidelines by employing non-dimensionalized parameters across a wide range of Reynolds numbers. The rectangular channel-based correlations developed in this study showed improved prediction accuracy compared to conventional annular pipe-based correlations and are expected to be applicable to various battery thermal management system designs in the future. Full article

Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address these problems. We propose a compact spatial representation (CSR) to robustly generate local spatial multihypothesis features from part of the 2D pose sequence. Specifically, CSR models spatial constraints based on body parts and incorporates 2D Gaussian filters and nonparametric reduction to improve spatial features against low-quality 2D poses and reduce the computational cost of subsequent temporal encoding. We design a residual-based Hybrid Adaptive Fusion module that combines multihypothesis features with global frequency domain features to accurately estimate the 3D human pose with minimal computational cost. We realize CGFusionFormer with a PoseFormer-like transformer backbone. Extensive experiments on the challenging Human3.6M and MPI-INF-3DHP benchmarks show that our method outperforms prior transformer-based variants in short receptive fields and achieves a superior accuracy–efficiency trade-off. On Human3.6M (sequence length 27, 3 input frames), it achieves 47.6 mm Mean Per Joint Position Error (MPJPE) at only 71.3 MFLOPs, representing about a 40 percent reduction in computation compared with PoseFormerV2 while attaining better accuracy. On MPI-INF-3DHP (81-frame sequences), it reaches 97.9 Percentage of Correct Keypoints (PCK), 78.5 Area Under the Curve (AUC), and 27.2 mm MPJPE, matching the best PCK and achieving the lowest MPJPE among the compared methods under the same setting. Full article

Battery cell balancing is crucial in series-connected lithium-ion packs to maximize usable capacity, ensure safe operation, and prolong cycle life. This paper presents a comprehensive study and a novel adaptive duty-cycled hybrid balancing system that combines passive bleed resistors and an active switched-capacitor (SC) balancer, specifically designed for a 4-cell series-connected battery pack. This work also explored open circuit voltage (OCV)-driven adaptive pulse-frequency modulation (PFM) active balancing to achieve higher efficiency and better balancing speed based on different system requirements. Finally, this paper compares passive, active (SC-based), and adaptive duty-cycled hybrid balancing strategies in detail, including theoretical modeling of energy transfer and efficiency for each method. Simulation showed that the adaptive hybrid balancer speeds state-of-charge (SoC) equalization by 16.24% compared to active-only balancing while maintaining an efficiency of 97.71% with minimal thermal stress. The simulation result also showed that adaptive active balancing was able to achieve a high efficiency of 99.86% and provided an additional design degree of freedom for different applications. The results indicate that the adaptive hybrid balancer offered an excellent trade-off between balancing speed, efficiency, and implementation simplicity for 4-cell Li-ion packs, making it highly suitable for applications such as high-voltage portable chargers. Full article

Chicoric and chlorogenic acids (CRA and CGA), two caffeic acid derivatives found in a large variety of plants, particularly in Asteraceae, are known to modulate glucose-6-phosphatase (G6Pase) in hepatic and muscle cells. The aim of the present study is to use CRA/CGA to explore the modulation role and molecular mechanism of endocrine pancreatic beta-cells’ insulin secretion. The G6Pase enzyme activity influenced by caffeic and derivatives alone or in combination was assessed on microsomal fractions of INS1-beta-cells and hepatocytes. Overall, our results show inverse effects of CGA/CRA, allowing us to investigate the G6Pase activity modulation under low and high glucose concentrations. Our data strongly suggests the existence of two putative forms of the G6Pase enzyme. Based on these observations, we formulate the hypothesis of an adaptative bi-conformational model of G6Pase enzyme activity modulation depending on the level of the beta-cell glucose exposure. Full article

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment. Full article

Elucidating amyloid formation inside biomolecular condensates requires models that resolve (i) local, chemistry specific contacts controlling β registry and (ii) mesoscale phase behavior and cluster coalescence on microsecond timescales—capabilities beyond single resolution models. We present a hybrid united atom/coarse grained (UA–CG) force field coupling a PACE UA peptide model with the MARTINI CG framework. Cross resolution nonbonded parameters are first optimized against all atom side chain potentials of mean force to balance the relative strength between different types of interactions and then refined through universal parameter scaling by matching radius of gyration distributions for specific systems using. We applied this approach to simulate a recently reported model system comprising the LVFFAR₉ peptide that can co-assemble into amyloid fibrils via liquid–liquid phase separation. Our ten-microsecond simulations reveal rapid droplet formation populated by micelle like nanostructures with its inner core composed of LVFF clusters. The nanostructures can further fuse but the fusion is reaction-limited due to an electrostatic coalescence barrier. β structures emerge once clusters exceed ~10 peptides, and the LVFFAR₉ fraction modulates amyloid polymorphism, reversing parallel versus antiparallel registry at lower LVFFAR₉. These detailed insights generated from long simulations highlight the promise of our hybrid UA–CG strategy in investigating the molecular mechanism of condensate aging. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article

We present a (Light)weight (S)atellite–(A)erial feature (M)atching framework (LiteSAM) for robust UAV absolute visual localization (AVL) in GPS-denied environments. Existing satellite–aerial matching methods struggle with large appearance variations, texture-scarce regions, and limited efficiency for real-time UAV applications. LiteSAM integrates three key components to address these issues. First, efficient multi-scale feature extraction optimizes representation, reducing inference latency for edge devices. Second, a Token Aggregation–Interaction Transformer (TAIFormer) with a convolutional token mixer (CTM) models inter- and intra-image correlations, enabling robust global–local feature fusion. Third, a MinGRU-based dynamic subpixel refinement module adaptively learns spatial offsets, enhancing subpixel-level matching accuracy and cross-scenario generalization. The experiments show that LiteSAM achieves competitive performance across multiple datasets. On UAV-VisLoc, LiteSAM attains an RMSE@30 of 17.86 m, outperforming state-of-the-art semi-dense methods such as EfficientLoFTR. Its optimized variant, LiteSAM (opt., without dual softmax), delivers inference times of 61.98 ms on standard GPUs and 497.49 ms on NVIDIA Jetson AGX Orin, which are 22.9% and 19.8% faster than EfficientLoFTR (opt.), respectively. With 6.31M parameters, which is 2.4× fewer than EfficientLoFTR’s 15.05M, LiteSAM proves to be suitable for edge deployment. Extensive evaluations on natural image matching and downstream vision tasks confirm its superior accuracy and efficiency for general feature matching. Full article

In field exploration, disaster rescue, and complex terrain operations, the accuracy of ground type recognition directly affects the walking stability and task execution efficiency of legged robots. To address the problem of terrain recognition in complex ground environments, this paper proposes a high-precision classification method based on single-leg triaxial force signals. The method first employs a one-dimensional convolutional neural network (1D-CNN) module to extract local temporal features, then introduces a long short-term memory (LSTM) network to model long-term and short-term dependencies during ground contact, and incorporates a convolutional block attention module (CBAM) to adaptively enhance the feature responses of critical channels and time steps, thereby improving discriminative capability. In addition, an improved whale optimization algorithm (iBWOA) is adopted to automatically perform global search and optimization of key hyperparameters, including the number of convolution kernels, the number of LSTM units, and the dropout rate, to achieve the optimal training configuration. Experimental results demonstrate that the proposed method achieves excellent classification performance on five typical ground types—grass, cement, gravel, soil, and sand—under varying slope and force conditions, with an overall classification accuracy of 96.94%. Notably, it maintains high recognition accuracy even between ground types with similar contact mechanical properties, such as soil vs. grass and gravel vs. sand. This study provides a reliable perception foundation and technical support for terrain-adaptive control and motion strategy optimization of legged robots in real-world environments. Full article

In this study, a novel compliant ankle structure with three passive degrees of freedom for quadruped robots is presented. First, this paper introduced the bionic principle and structural implementation method of the passively compliant ankle, with a particular focus on the configuration and working principle of the elastic adjustment element. Then, the kinematic model of the ankle and mathematic model of the elastic element, comprising mechanical and pneumatic model, was established by using appropriate theory. Finally, a test rig of the ankle was carried out to verify its actual function. The research results show that: (1) The ankle structure demonstrates excellent stability, maintaining its upright posture even under unreliable foot–ground interactions. (2) Compared to traditional structure, the single-leg module incorporating the proposed design exhibits smoother forward stepping under an appropriate pre-inflation pressure, with its actual motion trajectory showing closer agreement with the planned one; (3) The parallel topology enables a notable reduction in the driving torque of each joint in the leg during motion, thereby improving the energy efficiency of robots. Full article

Fingerprint authentication systems encounter growing threats from presentation attacks, making strong liveness detection crucial. This work presents a deep learning-based framework integrating EfficientNetB0 with a Squeeze-and-Excitation (SE) attention approach, using transfer learning to enhance feature extraction. The LivDet 2015 dataset, composed of both real and fake fingerprints taken using four optical sensors and spoofs made using PlayDoh, Ecoflex, and Gelatine, is used to train and test the model architecture. Stratified splitting is performed once the images being input have been scaled and normalized to conform to EfficientNetB0’s format. The SE module adaptively improves appropriate features to competently differentiate live from fake inputs. The classification head comprises fully connected layers, dropout, batch normalization, and a sigmoid output. Empirical results exhibit accuracy between 98.50% and 99.50%, with an AUC varying from 0.978 to 0.9995, providing high precision and recall for genuine users, and robust generalization across unseen spoof types. Compared to existing methods like Slim-ResCNN and HyiPAD, the novelty of our model lies in the Squeeze-and-Excitation mechanism, which enhances feature discrimination by adaptively recalibrating the channels of the feature maps, thereby improving the model’s ability to differentiate between live and spoofed fingerprints. This model has practical implications for deployment in real-time biometric systems, including mobile authentication and secure access control, presenting an efficient solution for protecting against sophisticated spoofing methods. Future research will focus on sensor-invariant learning and adaptive thresholds to further enhance resilience against varying spoofing attacks. Full article

Breast cancer remains a leading cause of morbidity and mortality among women globally, with increasing incidence projected in the coming years. Despite advances in standard oncologic therapies, there is a growing interest in supportive interventions that enhance treatment efficacy and reduce adverse effects. This review critically evaluates preclinical and clinical data on the medicinal mushroom Coriolus versicolor and its bioactive compounds—primarily polysaccharide-K, polysaccharopeptide, and laccase—as potential adjuvants in breast cancer therapy. A systematic PubMed search identified 11 original studies from 2010 to 2025 examining the impact of C. versicolor on breast cancer cell lines, animal models, and human subjects. Findings consistently demonstrate antiproliferative, pro-apoptotic, necroptotic, anti-invasive, and immunomodulatory effects across various breast cancer subtypes, including triple-negative breast cancer. One phase I clinical trial also reported good tolerability and immunological benefits in patients post-chemotherapy. The review highlights molecular mechanisms involving apoptosis, necroptosis, and modulation of the tumor microenvironment. While promising, these results underscore the need for standardized preparations, pharmacokinetic data, and larger placebo-controlled trials. Overall, C. versicolor shows potential as a safe, natural adjunct to conventional therapy, offering prospects for integrative strategies in breast cancer management. Full article

This paper introduces the GOLEM ontology, a novel framework designed to provide a structured and computationally tractable representation of narrative and fictional elements. Addressing limitations in existing ontologies regarding the integration of fictional entities and diverse narrative theories, our model extends CIDOC CRM and LRMoo and leverages DOLCE’s cognitive foundations to provide a flexible and interoperable framework. The ontology captures complexities of narrative structure, character dynamics, and fictional worlds while supporting provenance tracking and pluralistic interpretations. The modular structure facilitates alignment with various literary and narrative theories and integration of external resources. Future work will focus on expanding domain-specific extensions, validating the model through larger-scale case studies, and developing a reader response module to systematically model the reception of narratives. By fostering interoperability between literary theory, fan cultures, and computational analysis, this ontology lays a foundation for interoperable comparative research on narrative and fiction. Full article