Search Results (64)

The Internet of Vehicles (IoV) provides an effective solution for alleviating traffic congestion and enhancing road safety. However, shared traffic messages in IoV may deviate from on-road conditions due to self-interest protection or insufficient sensor performance. Therefore, evaluating the trustworthiness of shared messages is essential for vehicles to make informed decisions. To this end, a truth-oriented trust model for shared traffic message is proposed, which is inspired by human trust establishment mechanisms (HS-TEMs). Firstly, we quantify the integrated trust value (I-VT) of the message sender by fusing self-experience-based vehicle trust (SEB-VT) and peer-recommendation-based vehicle trust (PRB-VT). In SEB-VT, a sample-size-dependent smoothing factor dynamically trades off prior information and empirical evidence, reducing instability under small-sample conditions. In PRB-VT, we employ link analysis to compute the reference degree of recommendation information, which mitigates biases arising from subjective cognitive limitations. Secondly, with the I-VT of vehicles, we calculate event trust (ET) by differentiating message attitudes and quantifying their relative influence, which effectively reduces the impact of individual bias on the final judgment. The simulation results show that HS-TEM can accurately and fairly evaluate the credibility of messages, which helps vehicles make informed decisions. Full article

The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain fusion reasoning framework to realize accurate link prediction. First, a dual retrieval mechanism based on semantic similarity metrics and embedded feature matching is designed to construct a high-confidence candidate entity set; second, entity-attribute chains, entity-relationship chains, and historical context chains are established by integrating context information from external knowledge bases to generate a candidate entity set. Finally, a self-consistency scoring method fusing type constraints and semantic space alignment is proposed to realize the joint validation of structural rationality and semantic relevance of candidate entities. Experiments on two public datasets show that the method in this paper fully utilizes the ability of multi-chain reasoning and significantly improves the accuracy of knowledge graph link prediction. Full article

To address the lack of effective risk-identification methods during the commissioning of new power grid equipment, we propose a knowledge graph construction approach for both scheme generation and risk identification. First, a gated attention mechanism fuses textual semantics with knowledge embeddings to enhance feature representation. Then, by introducing a global memory matrix with a decay-factor update mechanism, long-range dependencies across paragraphs are captured, yielding a domain-knowledge-augmentation universal information-extraction framework (DKA-UIE). Using the DKA-UIE, we learn high-dimensional mappings of commissioning-scheme entities and their labels, linking them according to equipment topology and risk-identification logic to build a commissioning knowledge graph for new equipment. Finally, we present an application that utilizes this knowledge graph for the automated generation of commissioning plans and risk identification. Experimental results show that our model achieves an average precision of 99.19%, recall of 99.47%, and an F₁-score of 99.33%, outperforming existing methods. The resulting knowledge graph effectively supports both commissioning-plan generation and risk identification for new grid equipment. Full article

Bispecific peptides represent an emerging therapeutic platform in immunotherapy, offering simultaneous engagement of two distinct molecular targets to enhance specificity, functional synergy, and immune modulation. Their compact structure and modular design enable precise interaction with protein–protein interfaces and shallow binding sites that are otherwise difficult to target. This review summarizes current design strategies of bispecific peptides, including fused, linked, and self-assembled architectures, and elucidates their mechanisms in bridging tumor cells with immune effector cells and blocking immune checkpoint pathways. Recent developments highlight their potential applications not only in oncology but also in autoimmune and infectious diseases. Key translational challenges, including proteolytic stability, immunogenicity, delivery barriers, and manufacturing scalability, are discussed, along with emerging peptide engineering and computational design strategies to address these limitations. Bispecific peptides offer a versatile and adaptable platform poised to advance precision immunotherapy and expand therapeutic options across immune-mediated diseases. Full article

This study investigates the structure–property–performance (SPP) relationships of two thermoplastic polyurethanes (TPUs), FILAFLEX FOAMY 70A and SMARTFIL^® FLEX 98A, manufactured by fused filament fabrication (FFF). Disc specimens were produced with varying gyroid infill densities (10–100%) and Archimedean surface textures, and their tribological and surface characteristics were analyzed through Ball-on-Disc tests, profilometry, and optical microscopy. SMARTFIL^® FLEX 98A exhibited a sharp reduction in the coefficient of friction (μ) with increasing infill, from 1.174 at 10% to 0.371 at 100%, linked to improved structural stability at higher densities. In contrast, FILAFLEX FOAMY 70A maintained a stable but generally higher coefficient of friction (0.585–0.729) across densities, reflecting its foamed microstructure and bulk yielding behavior. Surface analysis revealed significantly higher roughness in SMARTFIL^® FLEX 98A, while FILAFLEX FOAMY 70A showed consistent roughness across infill levels. Both TPUs resisted inducing abrasive wear on the steel counterpart, but their stress-accommodation mechanisms diverged. These findings highlight distinct application profiles: SMARTFIL^® FLEX 98A for energy-absorbing, deformable components, and FILAFLEX FOAMY 70A for applications requiring stable surface finish and low adhesive wear. The results advance the design of functionally graded TPU materials through the controlled tuning of infill and surface features. Full article

To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and external commonsense knowledge enhancement. The model employs a backbone architecture comprising CLIP’s ViT visual encoder, Faster R-CNN, BERT text encoder, and GPT-2 text decoder. It incorporates two core mechanisms: a multi-step cross-attention mechanism that iteratively aligns image and text features across multiple rounds, progressively enhancing inter-modal semantic consistency for more accurate cross-modal representation fusion. Moreover, the model employs Faster R-CNN to extract region-based object features. These features are mapped to corresponding entities within the dataset through entity probability calculation and entity linking. External commonsense knowledge associated with these entities is then retrieved from the ConceptNet knowledge graph, followed by knowledge embedding via TransE and multi-hop reasoning. Finally, the fused multimodal features are fed into the GPT-2 decoder to steer caption generation, enhancing the lexical richness, factual accuracy, and cognitive plausibility of the generated descriptions. In the experiments, the model achieves CIDEr scores of 142.6 on MSCOCO and 78.4 on Flickr30k. Ablations confirm both modules enhance caption quality. Full article

Mycoplasma pneumoniae, a cell wall-deficient pathogen, primarily affects children and adolescents, causing Mycoplasma pneumoniae pneumonia (MPP). Following the relaxation of non-pharmaceutical interventions (NPIs) post COVID-19, there has been a global increase in MPP cases and macrolide-resistant strains. Vaccination against M. pneumoniae is being explored as a promising approach to reduce infections, limit antibiotic misuse, and prevent the emergence of drug-resistant variants. We developed an mRNA vaccine, mRNA-SP+P1, incorporating a eukaryotic signal peptide (tissue-type plasminogen activator signal peptide) fused to the C-terminal region of the P1 protein. Targeting amino acids 1288 to 1518 of the P1 protein, the vaccine was administered intramuscularly to BALB/c mice in a three-dose regimen. To evaluate immunogenicity, we quantified anti-P1 IgG antibody titers using enzyme-linked immunosorbent assays (ELISAs) and assessed cellular immune responses by analyzing effector memory T cell populations using flow cytometry. We also tested the functional activity of vaccine-induced sera for their ability to inhibit adhesion of the ATCC M129 strain to KMB17 cells. The vaccine’s protective efficacy was assessed against the ATCC M129 strain and its cross-protection against the ST3-resistant strain. Transcriptomic analysis was conducted to investigate gene expression changes in peripheral blood, aiming to uncover mechanisms of immune modulation. The mRNA-SP+P1 vaccine induces P1 protein-specific IgG antibodies and an effector memory T-cell response in BALB/c mice. Adhesion inhibition assays demonstrated that serum from vaccinated mice attenuatesthe adhesion ability of ATCC M129 to KMB17 cells. Furthermore, three doses of the vaccine confer significant and long-lasting, though partial, protection against the ATCC M129 strain and partial cross-protection against the ST3 drug-resistant strain. Transcriptome analysis revealed significant gene expression changes in peripheral blood, confirming the vaccine’s capacity to elicit an immune response from the molecular level. Our results indicate that the mRNA-SP+P1 vaccine appears to be an effective vaccine candidate against the prevalence of Mycoplasma pneumoniae. Full article

The mechanism of low- to middle-rank coal seam gas accumulation in the Baode block on the eastern edge of the Ordos Basin is well understood. However, exploration efforts in the Shizuishan area on the western edge started later, and the current understanding of enrichment and accumulation rules is unclear. It is important to systematically study enrichment and accumulation, which guide the precise exploration and development of coal seam gas resources in the western wing of the basin. The coal seam collected from the Shizuishan area of Ningxia was taken as the target. Based on drilling, logging, seismic, and CBM (coalbed methane) test data, geological conditions were studied, and factors and reservoir formation modes of CBM enrichment were summarized. The results are as follows. The principal coal-bearing seams in the study area are coal seams No. 2 and No. 3 of the Shanxi Formation and No. 5 and No. 6 of the Taiyuan Formation, with thicknesses exceeding 10 m in the southwest and generally stable thickness across the region, providing favorable conditions for CBM enrichment. Spatial variations in burial depth show stability in the east and south, but notable fluctuations are observed near fault F1 in the west and north. These burial depth patterns are closely linked to coal rank, which increases with depth. Although the southeastern region exhibits a lower coal rank than the northwest, its variation is minimal, reflecting a more uniform thermal evolution. Lithologically, the roof of coal seam No. 6 is mainly composed of dense sandstone in the central and southern areas, indicating a strong sealing capacity conducive to gas preservation. This study employs a system that fuses multi-source geological data for analysis, integrating multi-dimensional data such as drilling, logging, seismic, and CBM testing data. It systematically reveals the gas control mechanism of “tectonic–sedimentary–fluid” trinity coupling in low-gentle slope structural belts, providing a new research paradigm for coalbed methane exploration in complex structural areas. It creatively proposes a three-type CBM accumulation model that includes the following: ① a steep flank tectonic fault escape type (tectonics-dominated); ② an axial tectonic hydrodynamic sealing type (water–tectonics composite); and ③ a gentle flank lithology–hydrodynamic sealing type (lithology–water synergy). This classification system breaks through the traditional binary framework, systematically explaining the spatiotemporal matching relationships of the accumulated elements in different structural positions and establishing quantitative criteria for target area selection. It systematically reveals the key controlling roles of low-gentle slope structural belts and slope belts in coalbed methane enrichment, innovatively proposing a new gentle slope accumulation model defined as “slope control storage, low-structure gas reservoir”. These integrated results highlight the mutual control of structural, thermal, and lithological factors on CBM enrichment and provide critical guidance for future exploration in the Ningxia Autonomous Region. Full article

Fused Filament Fabrication (FFF) is a widely adopted additive manufacturing technique, yet its mechanical performance is highly dependent on process parameters, particularly nozzle diameter and printing speed. This study evaluates the influence of these parameters on the tensile behavior of Acrylonitrile Butadiene Styrene (ABS) and Polylactic Acid (PLA), aiming to determine optimal conditions for enhanced strength. ASTM D638-Type IV specimens were printed using nozzle diameters ranging from 0.05 to 0.25 mm and speeds from 15 to 80 mm/s. For ABS, tensile strength increased from 56.46 MPa to 60.74 MPa, representing a 7.6% enhancement, as nozzle diameter increased, with the best performance observed at 0.25 mm and 45 mm/s, attributed to improved melt flow and interlayer fusion. PLA exhibited a non-linear response, reaching a maximum strength of 89.59 MPa under the same conditions, marking a 22.3% enhancement over the minimum value. The superior performance of PLA was linked to optimal thermal management that enhanced crystallinity and interlayer bonding. Fractographic analysis revealed reduced porosity and smoother fracture surfaces under optimized conditions. Overall, PLA consistently outperformed ABS across all settings, with an average tensile strength advantage of 47.5%. The results underscore the need for material-specific parameter tuning in FFF and offer practical insights for optimizing mechanical performance in applications demanding high structural integrity, including biomedical, aerospace, and functional prototyping. Full article

In this study, a C-C bond-linked triazole-fused oxadiazole energetic compound, 4-amino-5-(4-amino-1,2,5-oxadiazol-3-yl)-2,4-dihydro-3H-1,2,4-triazol-3-one (1), was successfully designed and efficiently synthesized. Following nitration, a functional group-modified nitramine energetic compound (2) was obtained, and its energetic ionic salt (3) was further prepared. A comprehensive characterization of the structures of these three compounds was conducted, resulting in the successful elucidation of the single-crystal structures of compound 2·Ca²⁺·6H₂O and compound 3·MeOH. Compound 2 exhibited a positive formation enthalpy (56.2 kJ·mol⁻¹) and moderate mechanical sensitivity (FS = 120 N, IS = 12 J). Due to the presence of the nitramine group, compound 2 exhibited a relatively low thermal decomposition temperature (T_dec = 94 °C). However, the thermal stability of compound 3 was significantly improved (T_dec = 233 °C), which is attributed to salt formation. Compound 3 exhibits a positive formation enthalpy (121.0 kJ·mol⁻¹), along with excellent detonation performance (D = 8120 m·s⁻¹, P = 32.1 GPa) and reduced mechanical sensitivity (FS = 224 N, IS = 24 J). Therefore, the multi-heterocyclic compound, joined via C-C bond linkage, demonstrates outstanding performance, offering a new avenue for the design and synthesis of energetic materials. Full article

To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces. Full article

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are two neurodegenerative disorders that share common genes and pathomechanisms and are referred to as the ALS-FTD spectrum. A hallmark of ALS-FTD pathology is the abnormal aggregation of proteins, including Cu/Zn superoxide dismutase (SOD1), transactive response DNA-binding protein 43 (TDP-43), fused in sarcoma/translocated in liposarcoma (FUS/TLS), and dipeptide repeat proteins resulting from C9orf72 hexanucleotide expansions. Genetic mutations linked to ALS-FTD disrupt protein stability, phase separation, and interaction networks, promoting misfolding and insolubility. This review explores the molecular mechanisms underlying protein aggregation in ALS-FTD, with a particular focus on TDP-43, as it represents the main aggregated species inside pathological inclusions and can also aggregate in its wild-type form. Moreover, this review describes the protective mechanisms activated by the cells to prevent protein aggregation, including molecular chaperones and post-translational modifications (PTMs). Understanding these regulatory pathways could offer new insights into targeted interventions aimed at mitigating cell toxicity and restoring cellular function. Full article

Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury prevention, which has led many researchers to work on developing effective detection systems. However, existing unimodal systems that rely solely on skeleton or sensor data face challenges such as poor robustness, computational inefficiency, and sensitivity to environmental conditions. While some multimodal approaches have been proposed, they often struggle to capture long-range dependencies effectively. In order to address these challenges, we propose a multimodal fall detection framework that integrates skeleton and sensor data. The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. In parallel, the Bi-LSTM with CA processes inertial signals, with Bi-LSTM capturing long-range temporal dependencies and CA refining feature representations. The features from both branches are fused and passed through a fully connected layer for classification, providing a comprehensive understanding of human motion. The proposed system was evaluated on the Fall Up and UR Fall datasets, achieving a classification accuracy of 99.09% and 99.32%, respectively, surpassing existing methods. This robust and efficient system demonstrates strong potential for accurate fall detection and continuous healthcare monitoring. Full article

The multimodal knowledge graph link prediction model integrates entity features from multiple modalities, such as text and images, and uses these fused features to infer potential entity links in the knowledge graph. This process is highly dependent on the fitting and generalization capabilities of deep learning models, enabling the models to accurately capture complex semantic and relational patterns. However, it is this deep reliance on the fitting and generalization capabilities of deep learning models that leads to the black-box nature of the decision-making mechanisms and prediction bases within the multimodal knowledge graph link prediction models, which are difficult to understand intuitively. This black-box nature not only restricts the promotion and popularization of multimodal knowledge graph link prediction technology in practical applications but also hinders our understanding and exploration of the internal working mechanism of the model. Therefore, the purpose of this paper is to deeply explore the explainability problem of multimodal knowledge graph link prediction models and propose a multimodal post hoc model-independent multi-granularity explanation method (MMExplainer) for multimodal link prediction tasks. We learn the importance of each modality through modal separation, use textual semantics to guide a heuristic search to filter candidate explanation triples, and use textual masks to obtain explanation phrases that play an important role in prediction. Experimental results show that MMExplainer can provide coarse-grained explanations at the modal level and fine-grained explanations in structural and textual modalities, and the relevance index of the explanations in model decision-making is better than that of the baseline model. Full article

Orthopedic soft tissue injuries, such as those to the fibrocartilaginous meniscus in the knee, present a significant clinical challenge, impacting millions globally and often requiring surgical interventions that fail to fully restore mechanical function. Current bioengineered meniscal replacement options that incorporate synthetic and/or natural scaffolds have limitations in biomechanical performance and biological integration. This study introduces a novel scaffold fabrication approach, termed Hybrid Hydrogels Augmented via Additive Network Integration (HANI) with great potential for meniscal tissue engineering applications. HANI scaffolds combine cross-linked gelatin-based hydrogels with polycaprolactone (PCL) additive networks, created via Fused Deposition Modeling (FDM), to enhance mechanical strength and replicate the anisotropic properties of the meniscus. Custom Stereolithography (SLA)-printed molds ensure precise dimensional control and seamless incorporation of PCL networks within the hydrogel matrix. The mechanical evaluation of HANI scaffolds showed improvements in compressive stiffness, stress relaxation behavior, and load-bearing capacity, especially with circumferential and 3D PCL reinforcements, when compared to hydrogel scaffolds without additive networks. These findings highlight HANI’s potential as a cost-effective, scalable, and tunable scaffold fabrication approach for meniscal tissue engineering applications. Full article