Search Results (536)

Long-range rotating wrap-around-fin rockets may exhibit non-convergent conical motion at high Mach numbers, causing increased drag, reduced range, and potential flight instability. This study employs the implicit dual time-stepping method to solve the unsteady Reynolds-averaged Navier–Stokes (URANS) equations for simulating the flow field around a high aspect ratio wrap-around-fin rotating rocket at supersonic speeds. Validation of the numerical method in predicting aerodynamic characteristics at small angles of attack is achieved by comparing numerically obtained side force and yawing moment coefficients with experimental data. Analyzing the rocket’s angular motion process, along with angular motion equations, reveals the necessary conditions for the yawing moment to ensure stability during angular motion. Shape optimization is performed based on aerodynamic coefficient features and flow field structures at various angles of attack and Mach numbers, using the yawing moment stability condition as a guideline. Adjustments to parameters such as tail fin curvature radius, tail fin aspect ratio, and body aspect ratio diminish the impact of asymmetric flow induced by the wrap-around fin on the lateral moment, effectively resolving issues associated with near misses and off-target impacts resulting from dynamic instability at high Mach numbers. Full article

Many parametric models can be enriched by introducing additional parameters through transmutation, mixing, or compounding techniques. In this paper, we develop the framework of doubly generalized transmutation models (DGTMs), obtained by the repeated application of rank transmutation maps and their generalizations. We show that several flexible families already available in the literature can be reinterpreted as instances of double or multiple transmutation, thus unifying apparently disparate constructions under a common perspective. A key feature of DGTMs is their ability to flexibly control symmetry through parameterization, enabling more accurate modeling of asymmetric or heavy-tailed phenomena. We also discuss the potential extension of these models to the bivariate case. In addition, we introduce the gentransmuted R package, Version 1.0, which provides routines for data generation, parameter estimation, and model comparison for generalized transmutation models. Two real data applications illustrate the practical advantages of this approach, highlighting improved model fit relative to classical alternatives. Our results underscore the value of transmutation-based methods as a systematic tool for generating flexible probability distributions and advancing their computational implementation. Full article

Named entity recognition (NER) is one of the fundamental tasks in building knowledge graphs. For some domain-specific corpora, the text descriptions exhibit limited standardization, and some entity structures have entity nesting. The existing entity recognition methods have problems such as word matching noise interference and difficulty in distinguishing different entity labels for the same character in sequence label prediction. This paper proposes a span-based feature reuse stacked bidirectional long short term memory network (BiLSTM) nested named entity recognition (SFRSN) model, which transforms the entity recognition of sequence prediction into the problem of entity span suffix category classification. Firstly, character feature embedding is generated through bidirectional encoder representation of transformers (BERT). Secondly, a feature reuse stacked BiLSTM is proposed to obtain deep context features while alleviating the problem of deep network degradation. Thirdly, the span feature is obtained through the dilated convolution neural network (DCNN), and at the same time, a single-tail selection function is introduced to obtain the classification feature of the entity span suffix, with the aim of reducing the training parameters. Fourthly, a global feature gated attention mechanism is proposed, integrating span features and span suffix classification features to achieve span suffix classification. The experimental results on four Chinese-specific domain datasets demonstrate the effectiveness of our approach: SFRSN achieves micro-F1 scores of 83.34% on ontonotes, 73.27% on weibo, 96.90% on resume, and 86.77% on the supply chain management dataset. This represents a maximum improvement of 1.55%, 4.94%, 2.48%, and 3.47% over state-of-the-art baselines, respectively. The experimental results demonstrate the effectiveness of the model in addressing nested entities and entity label ambiguity issues. Full article

In real-world visual recognition tasks, long-tailed distributions pose a widespread challenge, with extreme class imbalance severely limiting the representational learning capability of deep models. In practice, due to this imbalance, deep models often exhibit poor generalization performance on tail classes. To address this issue, data augmentation through the synthesis of new tail-class samples has become an effective method. One popular approach is CutMix, which explicitly mixes images from tail and other classes, constructing labels based on the ratio of the regions cropped from both images. However, region-based labels completely ignore the inherent semantic information of the augmented samples. To overcome this problem, we propose a saliency-guided local semantic mixing (LSM) method, which uses differentiable block decoupling and semantic-aware local mixing techniques. This method integrates head-class backgrounds while preserving the key discriminative features of tail classes and dynamically assigns labels to effectively augment tail-class samples. This results in efficient balancing of long-tailed data distributions and significant improvements in classification performance. The experimental validation shows that this method demonstrates significant advantages across three long-tailed benchmark datasets, improving classification accuracy by 5.0%, 7.3%, and 6.1%, respectively. Notably, the LSM framework is highly compatible, seamlessly integrating with existing classification models and providing significant performance gains, validating its broad applicability. Full article

The Body Condition Score (BCS) is a key indicator of dairy cattle’s health, production efficiency, and environmental impact. Manual BCS assessment is subjective and time-consuming, limiting its scalability in precision agriculture. This study utilizes computer vision to automatically assess cattle body condition by analyzing tail features, categorizing BCS into five levels (3.25, 3.50, 3.75, 4.0, 4.25). SE attention improves feature selection by adjusting channel importance, while spatial attention enhances spatial information processing by focusing on key image regions. EfficientNet-B0, enhanced by SE and spatial attention mechanisms, improves feature extraction and localization. To facilitate edge device deployment, model distillation reduces the size from 23.8 MB to 8.7 MB, improving inference speed and storage efficiency. After distillation, the model achieved 91.10% accuracy, 91.14% precision, 91.10% recall, and 91.10% F1 score. The accuracy increased to 97.57% for ±0.25 BCS error and 99.72% for ±0.5 error. This model saves space and meets real-time monitoring requirements, making it suitable for edge devices with limited resources. This research provides an efficient, scalable method for automated livestock health monitoring, supporting intelligent animal husbandry development. Full article

Bacteriophages, or phages, are sophisticated nanomachines that efficiently infect bacteria. Their infection of lactic acid bacteria (LAB) used in fermentation can lead to significant industrial losses. Among phages that infect monoderm bacteria, those with siphovirion morphology characterized by a long, non-contractile tail are predominant. The initial stage of phage infection involves precise host recognition and binding. To achieve this, phages feature host adhesion devices (HADs) located at the distal end of their tails, which have evolved to recognize specific proteinaceous or saccharidic receptors on the host cell wall. Ceduovirus represents a group of unique lytic siphophages that specifically infect the LAB Lactococcus lactis by targeting proteinaceous receptors. Despite having compact genomes, most of their structural genes are poorly annotated and the architecture and function of their HADs remain unknown. Here we used AlphaFold3 to explore the Ceduovirus HADs and their interaction with the host. We show that Ceduovirus HADs exhibit unprecedented features among bacteriophages infecting Gram⁺, share structural similarities with bacterial secretion system VI, and combine both saccharide and protein-binding modules. Moreover, we could annotate the majority of Ceduovirus genes encoding structural proteins by leveraging their predicted structures, highlighting AlphaFold’s significant contribution to phage genome annotation. Full article

The GXXXG motif, also called the glycine zipper, is a common sequence pattern that facilitates tight packing of secondary structures, especially through helix–helix interactions in both membrane and soluble proteins. However, its overall distribution, sequence variation, and structural preferences depending on context are not fully understood. Here, we offer a detailed, large-scale analysis of GXXXG motifs, examining over 25,000 unique UniProt sequences with structural data. We classified the motifs as transmembrane (TM), non-transmembrane (non-TM), or shared, based on their TM coverage, and analyzed them via statistical models, diversity measures, and compositional profiling. Our findings show that ≥60% TM coverage is a reliable cutoff to distinguish TM-specific motifs, which tend to have less sequence diversity, lower entropy, more hydrophobic residues (notably leucine, isoleucine, and valine), and rank–frequency distributions that follow a heavy-tailed pattern, indicating strong selective pressure. Conversely, non-TM motifs are more varied, with higher entropy and a preference for polar or flexible residues. Shared motifs have intermediate features, reflecting their functional versatility. Power-law and Zipfian analyses support the distinct statistical signatures of TM and non-TM motifs at the 60% coverage threshold. These results enhance our understanding of the structural and evolutionary roles of the GXXXG motif, setting clear standards for identifying TM-specific motifs and offering insights into membrane protein biology, synthetic design, and functional annotation. Full article

The joint detection of multiple features significantly enhances radar’s ability to detect weak targets on the sea surface. However, issues such as large data requirements and the lack of robustness in high-dimensional decision spaces severely constrain the detection performance and applicability of such methods. In response to this, this paper proposes a radar target detection method based on linear fusion of two features from the perspective of feature dimension reduction. Firstly, a two-feature linear dimensionality reduction method based on distribution compactness is designed to form a fused feature. Then, the generalized extreme value (GEV) distribution is used to model the tail of the probability density function (PDF) of the fused feature, thereby designing an asymptotic constant false alarm rate (CFAR) detector. Finally, the detection performance of this detector is comparatively analyzed using measured data. Full article

Radio frequency (RF) fingerprinting, as an emerging physical layer security technology, demonstrates significant potential in the field of Internet of Things (IoT) security. However, most existing methods operate under a ‘closed-set’ assumption, failing to effectively address the continuous emergence of unknown devices in real-world scenarios. To tackle this challenge, this paper proposes an open-set radio frequency fingerprint identification (RFFI) method based on Multi-Task Prototype Learning (MTPL). The core of this method is a multi-task learning framework that simultaneously performs discriminative classification, generative reconstruction, and prototype clustering tasks through a deep network that integrates an encoder, a decoder, and a classifier. Specifically, the classification task aims to learn discriminative features with class separability, the generative reconstruction task aims to preserve intrinsic signal characteristics and enhance detection capability for out-of-distribution samples, and the prototype clustering task aims to promote compact intra-class distributions for known classes by minimizing the distance between samples and their class prototypes. This synergistic multi-task optimization mechanism effectively shapes a feature space highly conducive to open-set recognition. After training, instead of relying on direct classifier outputs, we propose to adopt extreme value theory (EVT) to statistically model the tail distribution of the minimum distances between known class samples and their prototypes, thereby adaptively determining a robust open-set discrimination threshold. Comprehensive experiments on a real-world dataset with 16 Wi-Fi devices show that the proposed method outperforms five mainstream open-set recognition methods, including SoftMax thresholding, OpenMax, and MLOSR, achieving a mean AUROC of 0.9918. This result is approximately 1.7 percentage points higher than the second-best method, demonstrating the effectiveness and superiority of the proposed approach for building secure and robust wireless authentication systems. This validates the effectiveness and superiority of our approach in building secure and robust wireless authentication systems. Full article

Background/Objectives: Resistance of pathogenic microorganisms to antibiotics poses a serious threat to public health and often leads to devastating consequences. In this context, one of the pressing challenges in pharmacochemistry is the search for new, effective antibiotics to combat severe human diseases. Cyclic lipopeptides have emerged as some of the most promising candidates and have been widely studied. These compounds are a class of microbial secondary metabolites produced by various microorganisms, and they possess significant medical and biotechnological importance. The defining structural feature of these compounds is the presence of both a hydrophobic fragment, primarily a hydrocarbon tail of varying length, and a hydrophilic cyclic peptide moiety. This hydrocarbon tail confers amphiphilic properties to the lipopeptides, which are essential for their broad spectrum of biological activities. Their mechanism of action involves disruption of the cell membrane, and in many cases, the formation of ion-permeable defects has also been shown. Results: This review summarizes the data on cyclic lipopeptides produced by Pseudomonas spp., Streptomyces spp., and Bacillus spp. that modify membrane permeability through the formation of ion channels. The main emphasis is on understanding how the structure of the CLP can be related to the probability and mode of pore formation. Conclusions: The findings can contribute to expanding the arsenal of effective antimicrobial agents with a mechanism of action that reduces the risk of developing resistance. Full article

The neuropeptide’s multifaceted involvement in various components of neural homeostasis impacts pain and behavioral regulation. One of the highly potent neuropeptides is dermorphin, extracted from the skin of the Amazon frog (Phyllomedusa sauvagei). The unique feature of dermorphin is the D-Ala residue in its sequence, which has inspired researchers to search for dermorphin analogs for use as pharmaceuticals. The primary objective of this study is to synthesize several new linear and cyclic dermorphin analogs and evaluate them as potential non-invasive analgesics. By exploring our method for converting linear peptides into 2,5-diketopiperazine(2,5-DKP) derivatives, which stabilize peptide structures, we synthesize several new dermorphin linear peptides and chimeric cyclopeptidomimetics. These compounds were tested in vitro and in vivo to determine their biological activities and potential applicability as pharmaceuticals. For the evaluation of in vitro opioid activity, the “Guinea Pig Ileum” (GPI) test was used. D2 showed the highest activity, and cyclopeptides D3 and D4 showed high activity. We can assume that dermorphin analogues D2, D3, and D4 are potent agonists of µ-type opioid receptors and have high opioid activity. However, this needs to be verified using molecular modeling methods in further research. The analgesic effects of dermorphins have been evaluated in the “Hot-Plate” and “Tail-Flick” tests. In rats, D2 dermorphin analogues demonstrated dose-dependent analgesic effect in the “Water Tail-Flick” test after intranasal administration. A smaller dose of 0.5 µg/kg resulted in 40% analgesia and a short-term state of stupor. The maximum long-lasting analgesia was observed at a dose of 1.0 µg/kg, which induced complete stupor. The analgesic effect of peptide D2 after intraperitoneal administration at a 5.0 mg/kg dose was over 50%. The “Open-Field” test demonstrated a dose-dependent (15, 50, 150 μg/kg) peptide D2 suppression effect on behavioural reactions in rats following intranasal administration. A new modification of linear peptides, combined with a 2,5-DKP scaffold (D3 and D4), proved promising for oral use based on the results of analgesic effect evaluation in mice following intragastric administration. Full article

Panoptic scene graph generation (PSG) aims to simultaneously segment both foreground objects and background regions while predicting object relations for fine-grained scene modeling. Despite significant progress in panoptic scene understanding, current PSG methods face challenging problems: relation prediction often only relies on visual representations and is hindered by imbalanced relation category distributions. Accordingly, we propose IntegraPSG, a single-stage framework that integrates large language model (LLM) guidance with multimodal feature fusion. IntegraPSG introduces a multimodal sparse relation prediction network that efficiently integrates visual, linguistic, and depth cues to identify subject–object pairs most likely to form relations, enhancing the screening of subject–object pairs and filtering dense candidates into sparse, effective pairs. To alleviate the long-tail distribution problem of relations, we design a language-guided multimodal relation decoder where LLM is utilized to generate language descriptions for relation triplets, which are cross-modally attended with vision pair features. This design enables more accurate relation predictions for sparse subject–object pairs and effectively improves discriminative capability for rare relations. Experimental results show that IntegraPSG achieves steady and strong performance on the PSG dataset, especially with the R@100, mR@100, and mean reaching 38.7%, 28.6%, and 30.0%, respectively, indicating strong overall results and supporting the validity of the proposed method. Full article

This research presents a novel methodology to classify copper tailings according to their potential as alkali-activated materials (AAMs) for construction applications. The methodology includes geochemical and mineralogical characterization via QEMSCAN and X-ray fluorescence, with mechanical performance evaluation through compressive strength test (UCS). A three-phase diagram based on Al₂O₃, Fe₂O₃, and CaO-MgO-K₂O is proposed for a fast screening of copper tailing potential to be used as a construction material. In this paper, three copper tailings were chosen to test the methodology, and a set of five samples for each tailing have been geopolymerized for testing. Copper tailing samples were mixed with 0, 2.5, 5, 7.5 and 10% by mass of Ordinary Portland Cement (OPC) to evaluate the effect on performance when a chemical co-activator is used to improve material reactivity. Compressive strength testing was applied on 2 cm³ cubes after 28 days of curing at 60 °C, yielding values from 6 to 26.1 MPa. The best performing sample featured a Si/Al ≅ 3 ratio and a mineralogy with significant presence of reactive species such as plagioclase and K-feldspar (≅42%). In contrast, high levels of Fe₂O₃ (≥12%), clay (≥7%), and pyrite (≥4%) were associated with reduced mechanical performance. Full article

Lipophenols, combining phenolic and lipid moieties in a single molecule, are valuable candidates for providing enhanced bioactive properties with therapeutic potential, including anti-inflammatory functions associated with immune-mediated diseases such as intestinal bowel disease (IBD). Thus, palmitoyl–epigallocatechin gallate (PEGCG), a lipophilic derivative of epigallocatechin gallate (EGCG), has been highlighted for its enhanced stability in lipid-rich environments and bioavailability due to improved cellular uptake. However, the contribution of lipophilic esterification to PEGCG’s capacity to inhibit inflammation and the development of harmful autoimmune responses remains underexplored. This work uncovered the differential efficiency of EGCG and its palmitoyl derivative in modulating, in vitro, the interleukin profile generated by intestinal epithelium under inflammatory conditions. Therefore, both could attenuate the immune response by lowering macrophage migration and polarisation towards pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes. While the fatty acid moiety gave PEGCG a functional advantage over EGCG in adjusting the interleukin-based response of intestinal epithelium to inflammation—since both of them decreased, to a similar extent, the expression of pro-inflammatory interleukins, namely IL-6, IL-17, IL-18, IL-23, and TNF-α (which lowered by 11.2%, on average)—the former was significantly more efficient in cushioning the increase in IL-1β and IL-12p70 (by 9.2% and 10.4%, respectively). This immune modulation capacity did not significantly impact the migration and expression of costimulatory molecules featuring M1 (CD86⁺) or M2 (CD206⁺) phenotypes by THP-1-derived macrophages, for which both bioactive compounds exhibited equivalent efficiency. Nonetheless, the analysis of the pro- and anti-inflammatory interleukins secreted by differentiated macrophages allowed the identification of an advantage for PEGCG, which decreased the expression of the pro-inflammatory immune mediators IL-1β and IL-12p70, IL-23, and TNF-α more efficiently. These results suggest that lipophilisation of phenolic compounds presents exciting potential for extending their application as functional molecules by combining the effects of their polar head with their ability to interfere with membranes, conveyed by their lipophilic tail. In addition, the enhanced reactivity would confer a higher capacity to interact with cellular signalling molecules and thus inhibit or attenuate the immune response, which is of special interest for preventing the onset and severity of immune-mediated pathologies such as IBD. Full article

This study investigated the flesh quality, shelf life, and sensory freshness of sea bream (Sparus aurata) reared in the REMEDIA Life IMTA system, which incorporates bioremediator organisms—sponges, polychaetes, bivalves, and macroalgae—supported by artificial vertical collectors to enhance the settlement of sessile macroinvertebrates and improve environmental quality. A total of 96 fish (18 months old) were analysed, 48 farmed within the IMTA system and 48 in the conventional offshore system. Both groups received the same commercial feed. For each group, 16 fish were analysed after 1, 7, and 14 days of storage at 2 ± 1 °C to evaluate physical features, chemical and fatty acid composition, and sensory freshness. The total weight was markedly greater for fish in the IMTA group (p < 0.05), which showed a significantly (p < 0.05) longer tail. For all the storage times, the content of total saturated fatty acids was markedly higher in the control group, along with a lower concentration of polyunsaturated fatty acids (p < 0.05). The quality index method showed better results for the IMTA group (p < 0.05), particularly after 2 weeks of storage in ice. In conclusion, sea bream reared in the IMTA system showed better flesh quality, extended shelf life, and prolonged sensory freshness. Full article