Search Results (12,437)

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

DP-128 is a multitarget benzonaphthyridine-6-chlorotacrine hybrid molecule with potent in vitro anticholinesterase and Aβ42 and tau anti-aggregating activity. While often used as a reference protein aggregation inhibitor, its further development as an anti-Alzheimer agent is limited by significant cytotoxicity, suboptimal aqueous solubility and microsomal stability. Since these drawbacks might arise from its rather high lipophilicity, in this work we have developed a series of more polar analogues, designed by structural modifications at the benzonaphthyridine or 6-chlorotacrine moieties or within the eight-atom linker. Half of the new analogues are indeed slightly more soluble and clearly less cytotoxic than DP-128, display single-digit acetylcholinesterase inhibitory activity, and retain the Aβ42 and tau anti-aggregating potency of the lead, as well as favourable brain permeation and high plasma stability. While further optimization of microsomal stability is necessary for a potential therapeutic use of this class of compounds, hybrids 16 and 17, with similar or even higher Aβ42 and tau anti-aggregating activity and lower cytotoxicity than DP-128, might represent novel pharmacological tools for protein aggregation studies. Full article

In this study, a hybrid multi-layer scheme for reversible color image encryption is proposed, ensuring lossless reconstruction and strong cryptographic security concurrently. This method consists of three main stages. First, session-specific keys are generated using HKDF-SHA256 along with a timestamp-based mechanism to prevent replay attacks and support dynamic key management. Second, a four-layer confusion–diffusion structure is applied. It uses Gram–Schmidt orthogonal matrices, integer-based PWLCM chaotic mapping, the Hill cipher, and dynamically created S-Boxes. These operations rely on integer modular arithmetic ${\mathbb{Z}}_{256}$ and Q16.16 fixed-point precision. Finally, ChaCha20 stream encryption with HMAC-SHA256 authentication is used to secure data transmission in distributed environments. Experimental tests conducted on standard images show strong cryptographic performance, including near-ideal entropy (7.9993 bits), a significant avalanche effect ($NPCR99.6\mathrm{\%}$, $UACI33.4\mathrm{\%}$), and very low pixel correlation. The method achieves perfect lossless reconstruction and provides an effective key space $\ge 2¹²⁸$. These results confirm the suitability of the proposed scheme for secure image protection in applications requiring bit-exact recovery, such as medical imaging, digital forensics, and satellite communications. Full article

The growing environmental impact of textile consumption has intensified the need for efficient post-consumer waste collection systems capable of supporting circular economy transitions. Designing effective logistics systems for textile waste is therefore crucial, and their proper dimensioning requires accurate forecasts of collected volumes. However, textile waste flows are highly heterogeneous and strongly influenced by behavioural factors, making reliable forecasting particularly challenging. This study investigates whether urban textile waste collection can be effectively predicted by combining stable bin-level heterogeneity with time-varying socio-spatial and behavioural indicators. Using panel data generated by a hybrid simulation model for the municipality of Parma, we implemented a fixed-effects econometric framework and compared its performance with traditional benchmarks, including seasonal means and Holt–Winters exponential smoothing. The results demonstrate that incorporating structural heterogeneity across collection points, together with behaviour-related dynamics, enhances prediction accuracy and significantly outperforms traditional univariate time-series approaches, both at the aggregate level (R² ≈ 0.81) and at the bin level (MAE ≈ 25). These findings also support the robustness and generalizability of the proposed panel-data econometric framework, which shows strong potential for application in other urban settings characterized by similar structural and behavioural features. Full article

The precipitation of nanoscale HfO₂ plays a critical role in the high-temperature creep properties of Fe-Cr-Al electrical heating alloys. However, the atomic-scale initial nucleation and growth mechanisms remain unclear, hindering the precise design of precipitates based on Hf microalloying. In this study, classical molecular dynamics simulations implemented in LAMMPS were employed to investigate the formation and evolution of Hf-O clusters at 1773 K, 1873 K, and 2000 K. The Fe-Cr-Al-Hf-O system was described by hybrid potential functions, whose reliability was verified by lattice-parameter calculations in good agreement with literature values. The simulation results demonstrate that Hf atoms and O atoms attract each other, forming stable Hf-O clusters. At higher temperatures, the diffusion capabilities of Hf and O atoms are enhanced, the number of Hf-O bonds grows, and the size of the largest cluster expands, indicating that elevated temperatures promote cluster growth. The calculated diffusion activation energy of Hf and O atoms indicates that increasing temperature promotes O atom diffusion more significantly. Analysis of the cluster radius of pair gyration and average atomic energy reveals that Hf-O clusters formed at 1873 K exhibit more compact and stable structural characteristics. Radial distribution function analysis further revealed that the atomic arrangement of neighboring atoms in Hf-O clusters closely resembles the relaxed HfO₂ crystal structure at the same temperature, indicating that Hf-O clusters serve as critical nucleation cores promoting the precipitation of HfO₂ crystals. This study elucidates the dynamic formation mechanism and structural evolution of Hf-O clusters in Fe-Cr-Al alloys at the atomic scale, providing valuable guidance for the optimized design of precise control over HfO₂ nanoprecipitates. Full article

The growing need for lightweight, multifunctional, and high-performance structures in the automotive, aerospace, electronics, and medical industries has driven the development of advanced joining technologies for polymers and metal-polymer combinations. Among these, ultrasonic welding (USW) and friction stir spot welding (FSSW) have emerged as promising solid-state techniques capable of producing reliable joints with minimal thermal degradation and enhanced interfacial bonding. This review focuses on recent developments in USW and FSSW of thermoplastics, fiber-reinforced composites, and hybrid metal–polymer systems, with a particular emphasis on process mechanics, microstructural evolution, and joint performance. The mechanisms of heat generation, material flow behavior, and consolidation are discussed in relation to key process parameters, including applied pressure, rotational speed, vibration amplitude, plunge depth, and dwell time. Microstructural transformations such as polymer chain orientation, recrystallization, interfacial diffusion, and defect formation are analyzed to establish process–structure–property relationships. Mechanical performance metrics, including lap shear strength, fatigue resistance, impact behavior, and environmental durability, are critically compared across different materials and welding methods. Furthermore, recent advances in numerical and thermo-mechanical modeling, in situ process monitoring, and data-driven optimization are discussed to highlight pathways toward predictive and scalable manufacturing. Current industrial applications and existing limitations such as challenges in automation, thickness constraints, and hybrid material compatibility are also evaluated. Finally, key research gaps and future directions are identified to improve joint reliability, sustainability, and broader industrial adoption of advanced solid-state welding technologies. Full article

In this work, a novel approach for implementing a resonant micro-accelerometer is demonstrated that may extend the operating frequency of such devices to several tens of MHz, which may enable direct wireless signal transfer. The proposed resonant accelerometer consists of a hybrid structure: a piezoelectric micro-resonator and a capacitive mass-spring (CMS) system (that are mechanically separated but electrically interconnected). The sensor utilizes the piezoelectric stiffening mechanism, which translates the acceleration-induced displacement of the capacitive mass-spring (CMS) structure into a shift in the resonance frequency of the interconnected resonator. The operating principle is elaborated upon in detail, supported by simulation and experimental results. Additionally, a novel fabrication technique is presented to realize a suspended fixed bi-layer electrode for the CMS in which a hardened layer of photoresist is utilized as a sacrificial layer. The experimental sensitivity of a fully functional device is reported to be ~6 Hz/g at 25 MHz (~0.23 ppm/g), which closely matches the simulated sensitivity of ~7 Hz/g (~0.278 ppm/g) for the fabricated capacitive gap of ~7 µm. Full article

Pitch canker caused by the fungus Fusarium circinatum is a destructive disease that affects pines in Europe, South Africa, and North America, particularly along the southeastern and western coasts of the United States. This study systematically elucidated the function of the Leucine-rich repeat (LRR) protein FcLRR1 in the pine pitch canker pathogen Fusarium circinatum. A total of 13 LRR proteins were identified via bioinformatic analysis. Using a gene knockout system, we demonstrated that deletion of FcLRR1 significantly impaired vegetative growth, conidiation, and conidium germination; led to a complete loss of macroconidia production; and drastically reduced abiotic stress tolerance and virulence. Transcriptome profiling revealed 612 downregulated genes, which were significantly enriched in pathways such as starch and sucrose metabolism, indicating that FcLRR1 modulated energy supply and pathogenicity through carbon source utilization. Through genome-wide protein structure modeling and yeast two-hybrid assays, we identified and validated the interaction between FcLRR1 and ALG-11, among other candidate proteins, further supporting its involvement in carbon metabolism, cell wall integrity, and pathogenesis. This study represents the first functional characterization of an LRR-containing protein in a forest pathogenic fungus and provides a foundational basis for developing targeted disease control strategies. Full article

Hybrid multi-backbone architectures and the utilization of edge cues for auxiliary training have become two major research trends in salient object detection (SOD). It is widely acknowledged that CNNs can effectively model local spatial structures, while Transformers can capture long-range global dependencies. However, the representation discrepancy between CNN and Transformer features, together with boundary-detail degradation during multi-scale fusion, remains a major challenge. In addition, how to effectively leverage edge cues as reliable structural guidance without introducing texture-induced false boundaries or boundary leakages remains an open issue. In this paper, we present SECA-Net, a unified framework that establishes a profound synergy between CNN and Transformer representations. It explicitly bridges their inherent discrepancies through level-dependent interaction strategies, while resolving structural degradation via a sequential “purify-and-guide” mechanism. This approach enables the network to extract and utilize edge cues effectively, thereby alleviating boundary degradation and texture-induced false contours. Specifically, we design a dual-encoder structure to extract features. A level-wise feature interaction (LFI) module is introduced to perform discrepancy-aware fusion across feature levels, stabilizing CNN–Transformer aggregation. Meanwhile, the features extracted from the CNN branch are projected into a semantic-aware edge refinement (SAER) module to produce clean multi-scale edge priors under high-level semantic guidance, suppressing texture-induced spurious edges. Finally, we design an edge-guided cross-attention feature aggregation (ECFA) module, which progressively injects refined edge priors as structural constraints into multi-scale saliency decoding via cascaded cross-attention, enabling effective structural refinement. Overall, LFI reduces cross-branch discrepancy, SAER purifies boundary priors, and ECFA integrates semantics and structure in a progressive decoding manner, forming a unified SECA-Net framework. Extensive experimental results on five benchmark SOD datasets show that SECA-Net outperforms 19 state-of-the-art methods, demonstrating its effectiveness. Specifically, our proposed method ranks first in $F_{\beta }$ and $BDE$ across all datasets, notably improving $F_{\beta }$ by $1.54\%$ on the challenging DUTS-TE dataset. Full article

Traffic flow prediction is a key component of Intelligent Transportation Systems (ITS), crucial for alleviating urban congestion, optimizing traffic management, and improving the overall efficiency of road networks. With the rapid growth in vehicle numbers and the increasing complexity of urban traffic patterns, accurate short-term traffic flow prediction has become increasingly important. This paper comprehensively reviews the latest advancements in traffic flow prediction methods, focusing on graph neural network (GNN)-based approaches and hybrid deep learning frameworks. First, we introduce the fundamental theoretical foundations, including graph neural networks, deep learning algorithms, heuristic optimization methods, and attention mechanisms. Subsequently, we summarize GNN-based prediction methods into four paradigms: (1) federated learning and privacy-preserving methods, enabling cross-regional collaboration while protecting sensitive data; (2) dynamically adaptive graph structure methods, capturing time-varying spatial dependencies; (3) multi-graph fusion and attention mechanism methods, enhancing feature representations from multiple perspectives; and (4) cross-domain technology integration methods, fusing novel architectures and interdisciplinary technologies. Furthermore, we investigate hybrid methods combining signal decomposition, heuristic optimization, and attention mechanisms with LSTM networks to address challenges related to non-stationarity and model optimization. For each category, we analyzed representative works and summarized their core innovations, strengths, and limitations using a systematic comparative table. Finally, we discussed current challenges, including computational complexity, model interpretability, and generalization ability, and outlined future research directions such as lightweight model design, uncertainty quantification, multimodal data fusion, and integration with traffic control systems. This review provides researchers and practitioners with a systematic understanding of the latest advances in traffic flow prediction and offers guidance for methodological selection and future research. Full article

Background/Objectives: Wearable activity monitors and sensor-based devices are increasingly used to quantify mobility, load, and recovery in orthopaedic patients, yet clinicians lack practical guidance on selection, implementation, and interpretation. This qualitative expert consensus study synthesized real-world experiences from leaders in orthopaedics, rehabilitation, biomechanics, and digital health who implemented wearables at scale. Methods: Semi-structured interviews were conducted with 16 experts (64% response rate) recruited via hybrid purposive and snowball sampling. Participants included orthopaedic surgeons and research scientists with 124 cumulative years of wearable experience across over 9000 monitored patients. Interviews addressed device selection, clinical workflow, data management, and adoption barriers. Data were charted into a structured extraction matrix and analyzed using Inductive Thematic Analysis and a Framework Approach, reported per COREQ guidelines. Results: Experts utilized diverse sensor platforms across arthroplasty, trauma, spine, and sports medicine. Four key themes emerged: (1) device selection prioritized usability and patient compliance over technical sophistication; (2) workflow required defined team roles to manage data volume and avoid clinical burden; (3) patient engagement favored simplified, actionable feedback amid divergent views on data transparency; (4) future outlook anticipated AI-driven proactive risk prediction. Conclusions: No single wearable suits all orthopaedic practices; success hinges on aligning sensor placement with clinical questions, rigorous data quality checks, and integration into care plans. This study offers a practical checklist and roadmap for point-of-care adoption. Full article

Semantic segmentation of overhead remote sensing imagery supports critical applications in mapping, urban planning, and disaster response, yet state-of-the-art segmentation networks are predominantly designed for ground-perspective imagery and do not directly address remote sensing challenges such as extreme scale variation, foreground–background imbalance, and large image sizes. Rather than proposing new architectures, we take an architecture-agnostic approach by incorporating the differential morphological profile (DMP), a multi-scale shape extraction method based on grayscale morphology, as supplementary input to modern segmentation networks. We evaluate two integration strategies: a Direct-In approach, which adapts the input stem to accept DMP channels in place of or alongside RGB data, and a Hybrid DMP dual-stream architecture in which separate RGB and DMP encoders process each modality independently. Experiments on the iSAID, ISPRS Potsdam, and LoveDA benchmark datasets assess multiple DMP differentials and structuring element shapes. Results show that use of the DMP as direct input into models generally under-perform RGB-only baselines, while the Hybrid DMP approach substantially closes this gap and in some cases surpasses baseline performance, with gains varying across object categories. In the strongest case, a Hybrid DMP SegNeXt-S model achieves a gain of $+3.19$ mIoU over the RGB-only baseline on the ISPRS Potsdam dataset, and Hybrid DMP models outperform the RGB-only baseline on two of the three benchmark datasets evaluated. These findings suggest that DMP features provide complementary shape information that, when properly integrated, can enhance semantic segmentation performance for overhead remote sensing imagery. Full article

Background and Objectives: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that requires effective conservative management. Physiotherapeutic scoliosis-specific exercises (PSSE) have been widely used; however, evidence regarding their effectiveness according to therapist supervision intensity remains limited. Therefore, this study aimed to evaluate the effects of PSSE in patients with AIS and to examine differences according to supervision intensity. Design: Systematic review and meta-analysis of randomized controlled trials (RCTs) conducted in accordance with the PRISMA 2020 guidelines. Materials and Methods: RCTs involving patients with AIS (aged 10–18 years, Cobb angle 10–45°) were included if PSSE was applied alone or in combination with other conservative treatments. The primary outcomes were Cobb angle, angle of trunk rotation (ATR), and Scoliosis Research Society-22 (SRS-22). Effect sizes were calculated as standardized mean differences (SMDs) using a random-effects model. Subgroup analyses were performed according to supervision intensity. Results: A total of 10 RCTs (n = 600) were included. The pooled analysis demonstrated that PSSE significantly reduced Cobb angle (SMD = −0.52, 95% CI −0.79 to −0.25, p < 0.001) and ATR (SMD = −1.01, 95% CI −1.53 to −0.48, p < 0.001) compared with control interventions. In subgroup analyses, fully supervised interventions showed larger and more consistent effects, with statistically significant improvements in both Cobb angle (SMD = −0.70) and ATR (SMD = −1.33), whereas hybrid approaches did not demonstrate statistically significant effects. However, statistical support for subgroup differences was stronger for ATR than for Cobb angle. SRS-22 scores showed a trend toward improvement but did not reach statistical significance. Moderate to high heterogeneity was observed in some analyses, and risk-of-bias concerns were identified in several studies. Conclusions: PSSE may be an effective conservative intervention for improving structural curvature and trunk rotation in patients with AIS. Subgroup findings suggest that closer therapist supervision may be associated with more favorable effects, particularly for ATR; however, these findings should be interpreted cautiously because of heterogeneity and potential risk of bias. Large-scale, high-quality trials are warranted to confirm the magnitude and long-term sustainability of clinical effects. Full article

Accurate identification of disease-associated microRNAs (miRNAs) is crucial for elucidating pathogenic mechanisms and advancing therapeutic discovery. Although computational methods, particularly those based on biological networks, have become essential tools for predicting miRNA-disease associations, existing approaches often struggle to comprehensively learn from heterogeneous data and optimize feature representations. To overcome these limitations, we propose the Multi-view Hybrid Attention Graph Convolutional Network (MV-HAGCN). This framework constructs a comprehensive heterogeneous network by integrating multi-source biological information, simultaneously capturing miRNA similarity and disease similarity. We design a hierarchical attention mechanism to enable refined feature learning: first, the Efficient Channel Attention (ECA) module prioritizes information-rich input features, ensuring the model focuses on high-value biological characteristics. Subsequently, the Multi-Head Self-Attention Graph Convolutional Network operates on these refined features. Through iterative message passing and multi-head self-attention, it captures not only direct first-order relationships between nodes but also explicitly models and infers complex, indirect higher-order relationships within the network. This hierarchical design progressively refines feature representations, from channel-level recalibration to global structural dependency modeling, enabling the model to capture both local and high-order relational patterns. Furthermore, a dynamic weight learning strategy adaptively integrates multi-perspective similarity matrices, achieving superior feature complementarity and synergy. Finally, the high-order node representations learned through multi-layer graph convolutions are fed into a multi-layer perceptron for integration and nonlinear transformation, enabling precise prediction of potential miRNA-disease associations. Comprehensive evaluation through five-fold cross-validation on HMDD v2.0 and v3.2 benchmark datasets demonstrates that MV-HAGCN consistently outperforms existing state-of-the-art methods in predictive performance. Case studies targeting key diseases such as breast cancer, lung tumors, and pancreatic disorders revealed that the top 50 miRNAs associated with each of these three conditions were all validated in databases, confirming the practical value of this model in screening candidate miRNAs with high biological relevance. Full article

IQ67-domain (IQD) proteins are plant-specific calmodulin (CaM)/calmodulin-like (CML) targets implicated in the spatial organization of Ca²⁺ signaling, yet their roles in tartary buckwheat (Fagopyrum tataricum) remain largely unexplored. Here, we identified 24 FtIQD genes and classified them into six phylogenetic subfamilies. FtIQDs show uneven chromosomal distribution and mainly arise from segmental duplication under purifying selection. Promoter analysis revealed the enrichment of MYB-, light-, and ABA-related cis-elements. To link FtIQDs with rutin variation, we performed an FtIQD-focused association analysis using whole-genome resequencing data from altitude-stratified panels of up to 220 accessions. Under additive, dominant, and recessive models, multiple significant SNPs (p < 1 × 10⁻⁵) were detected near a subset of FtIQD loci, showing clear model- and environment-dependent patterns. Recurrent loci included FtIQD22, FtIQD02, FtIQD16, and FtIQD19. RNA-seq under PEG-induced drought stress, tissue expression patterns, pathway co-expression, and qRT–PCR further prioritized FtIQD19. FtIQD19–GFP showed predominant nuclear localization with additional filamentous/peripheral signals, and yeast two-hybrid assays identified FtCaM7.2 as the strongest interactor among representative CaMs. Structural modeling of the FtIQD19–FtCaM7.2 complex suggested testable residue-level interaction features. Collectively, this work provides a foundational FtIQD resource and highlights candidate Ca²⁺/CaM–IQD modules potentially associated with altitude-dependent rutin variation in tartary buckwheat. Full article