Search Results (4,418)

This study introduces an intelligent output feedback hybrid $H_2/H_{\infty }$ robust controller for a pumped storage unit regulation system (PSURS), utilizing an enhanced salp swarm algorithm (ISSA). A linearized PSURS model is developed through transfer function analysis. Utilizing this model, a robust controller design is executed using linear matrix inequalities (LMIs) to craft an output feedback hybrid $H_2/H_{\infty }$ controller that aims for both optimal and robust performance. The $H_2/H_{\infty }$ controller designed in this paper boasts a straightforward structure that eliminates the need for multiple-state feedback, simplifying its integration into practical PSURS applications. In addition, the ISSA plays a critical role in the design phase by optimally tuning the weight parameters of the controller to ensure its effectiveness. Simulation tests have demonstrated that this newly developed intelligent output feedback hybrid $H_2/H_{\infty }$ robust controller markedly enhances the stability of the PSURS. It shows superior control quality and robustness compared to traditional controllers. Furthermore, when applied to a multi-machine power system within PSURS simulations, this controller effectively improves system damping and helps mitigate frequency fluctuations. Full article

In flood disaster emergency decision-making, the dynamic changes in expert trust relationships directly affects the efficiency of reaching a decision consensus. This paper constructs a dynamic evolution model of expert trust relationships in flood disaster emergency decision-making from the perspective of preference distance: the initial trust matrix and weights of experts based on four dimensions including cooperation intensity, social relations, background similarity, and subjective initial trust; the cognitive trust is quantified by using the intuitionistic fuzzy Hamming distance, and the trust relationship is dynamically update through the exponential fusion method; the Louvain community discovery algorithm is introduce to achieve dynamic clustering of experts and weight updates of experts in combination with the dynamic changes in trust relationships; and a consensus feedback adjustment mechanism is designed to optimize the preferences of experts with lower consensus. At the same time, the dynamic trust model is verified by combining a flood disaster case. Case validation shows that the model completes consensus iteration in just four rounds, with the maximum increase in cognitive trust due to opinion convergence reaching 0.18 during the evolution process. The model effectively captures changes in trust among experts during decision-making, improving consensus convergence speed while ensuring that the final solution aligns with the comprehensive considerations required in emergency scenarios. This study provides a quantitative tool for large-group decision-making in flood emergencies under high-pressure, information-poor environments; one that balances dynamic trust evolution with efficient consensus building. Full article

The continuous demand for advanced composite materials with superior mechanical and electrical properties has driven the exploration of copper matrix composites for high-performance applications. The Cu–2Zr–0.6B (wt.%) powder mixtures were mechanically alloyed (MA) using two different ball-to-powder weight ratios (BPR: 10:1 and 15:1) to investigate the influence of milling conditions on the final composite material’s properties. MA powders milled with BPR 15:1 exhibited the highest values of dislocation densities, which induce higher hardness of Cu–ZrB₂ bulk materials. The MA powders were consolidated using three different methods: conventional cold pressing followed by sintering (CPS), hot pressing (HP), and spark plasma sintering (SPS). The in situ forming of ZrB₂ (3.5 vol.%) reinforcements during consolidation processes in Cu matrix proved to have a major impact on enhancing the hardness and structural stability, while the use of SPS and HP offered superior control over grain growth and porosity reduction compared to CPS. Main findings related to electrical and mechanical properties showed similar values for SPS (~38% IACS, ~173 HV1) and HP compacts (~39% IACS, ~155 HV1) but proved to be much higher compared to values of CPS compacts (~21% IACS, ~80 HV1). Full article

Dental tissue regeneration, particularly alveolar bone and gingival repair, remains a major challenge in regenerative medicine. 3D bioprinting offers patient-specific and anatomically precise constructs, representing an advanced alternative to conventional grafting. In this study, nanohydroxyapatite (nHA), chitosan (CS), and collagen (CoL) were combined to fabricate and characterize 3D bioprinted dental grafts. SEM revealed a highly porous, interconnected architecture favorable for cell infiltration and nutrient exchange. EDS confirmed Ca/P ratios of 2.06 for nHA/CoL and 1.83 for nHA/CS/CoL, both of which are above the stoichiometric 1.67, indicating the presence of additional mineral phases and ion substitutions. FTIR and XRD verified characteristic functional groups and crystalline phases, including B-type HA with carbonate substitution. Mechanical testing showed that pure nHA exhibited the lowest compressive strength, whereas CoL incorporation improved stiffness. The nHA/CS/CoL composite achieved the highest compressive strength, elastic modulus, and toughness, demonstrating superior mechanical resilience. DSC analysis indicated endothermic peaks at 106.49 °C and 351.91 °C, with enthalpy values (264.91 J/g and 15.09 J/g) surpassing those of nHA alone. TGA revealed ~28.8% weight loss across three degradation stages, confirming enhanced thermal stability. In vitro cytocompatibility testing using L929 fibroblasts validated the biocompatibility of the composites. Collectively, the synergy between bioceramics and biopolymers markedly improved both mechanical and thermal performance. These findings position the nHA/CS/CoL scaffold as a promising candidate for clinical applications in dental tissue regeneration. Unlike conventional grafting materials, this study introduces a synergistically optimized nHA/CS/CoL bio-ink formulation specifically designed for extrusion-based 3D bioprinting of patient-specific dental constructs. The core innovation lies in the precise integration of nHA within a dual-polymer matrix (CS/CoL), which bridges the gap between mechanical resilience and biological signaling, achieving a compressive strength that mimics native alveolar bone while maintaining high cytocompatibility. Full article

In a network invaded by a mutant according to the birth–death updating rule and uniform initialization, in order to minimize the amplifying effect of any directed network, the adjacency matrix is modified at each time step up to a given time horizon, subject to resource constraints. The fixation probability of an invasive mutant is deduced from the first eigenvector of the resulting modified Markov transition matrix. Large-scale minimization is solved numerically for a representative sample of directed graphs of dimensions 6 to 8. The effects of the determinants of the optimal reduction of the fixation probability are estimated using a Heckman selection model. The number of neighbors, the heterogeneity of the incoming edge weights, and the homogeneity of the outgoing edge weights of the initial network increase the likelihood that the graphs are amendable. Among the amended networks, the reduction in the fixation probability is greater when the outgoing edge weights of the initial network are heterogeneous, those of its incoming edges are homogeneous, and the sequence of modifications increases the variance of the outgoing edge weights and decreases that of the incoming edge weights, thereby creating a trade-off, which is estimated numerically. Full article

Cutaneous fibrosis is characterized by aberrant wound healing with excessive extracellular matrix deposition, sustained inflammation, and oxidative stress, while currently available therapies show limited efficacy and safety. A Dual Hyaluronic Acid Compound (DHC), consisting of high-molecular-weight, low-molecular-weight, and minimally cross-linked hyaluronic acid, has demonstrated regenerative and antioxidant properties, but its anti-fibrotic effects have not been fully explored. This study investigated the anti-fibrotic potential of DHC using a bleomycin-induced murine dermal fibrosis model and a UVB-irradiated ex vivo human skin model. In C57BL/6 mice, dermal fibrosis was induced by daily bleomycin injections for three weeks, followed by intradermal DHC administration. Histological and biomechanical analyses showed that DHC significantly reduced dermal thickness, collagen deposition, and skin hardness compared with untreated fibrotic controls. DHC decreased α-SMA expression and increased MMP1 levels, indicating attenuation of myofibroblast activation and enhanced matrix remodeling. It also reduced macrophage markers (CD68, CD163) and pro-inflammatory cytokines (IL-1β, TNF-α). Furthermore, DHC restored superoxide dismutase (SOD) and catalase (CAT) activity and upregulated NRF2, HO-1, and NQO1 expression in the in vivo model. Similarly, DHC upregulated SOD and CAT activity and reduced pro-inflammatory cytokines (IL-6, TNF-α) in the ex vivo human skin model. These findings suggest that DHC exerts multimodal anti-fibrotic effects through coordinated regulation of fibroblast activation, inflammation, and oxidative stress, supporting its potential as a therapeutic approach for cutaneous fibrosis. Full article

Continuous unidirectional (UD) thermoplastic composite tapes are increasingly used in aerospace, automotive, and energy applications because of their high specific strength, low weight, recyclability, and compatibility with automated manufacturing. Since final component performance strongly depends on tape quality, reliable defect characterization and detection are essential. In this study, manufacturing-induced defects in polypropylene-based UD tapes reinforced with carbon and glass fibers were investigated using real images acquired directly from laboratory-scale production without synthetic data. Defects related to interfacial integrity, matrix distribution, fiber architecture, and surface irregularities were systematically analyzed, and a practical four-class defect taxonomy was established. To enable automated inspection under limited-data conditions, lightweight YOLOv8, YOLOv11, and the new YOLO26 models were comparatively evaluated using a UD tape-specific augmentation strategy combining physically constrained Albumentations and on-the-fly augmentation. Among the tested models, YOLO26-s achieved the best overall performance, reaching a mean mAP@0.5 of 0.87 ± 0.03, outperforming YOLOv11 (0.83) and YOLOv8 (0.78), with 0.90 precision and 0.85 recall. Interfacial (0.92 mAP) and matrix-related (0.90 mAP) defects were detected most reliably, whereas fiber-related (0.89 mAP) and surface defects (0.79 mAP) remained more challenging, particularly in glass-fiber-reinforced tapes due to transparency-masking effects. The results demonstrate the potential of compact deep learning models for computationally efficient and manufacturing-relevant in-line quality monitoring of UD tape production. Full article

Sustainable materials are increasingly being incorporated into high-strength concrete (HSC) to reduce environmental impact while maintaining structural performance. This study experimentally investigates the combined use of steel scale waste (SSW) as a replacement for natural fine aggregates and graphite nano/micro platelets (GNMPs) as a nano-modifying additive in HSC. Natural sand was replaced with SSW at levels of 0%, 50%, and 100%, while GNMPs were incorporated at dosages of 0%, 0.1%, 0.3%, and 0.5% by weight of cement. The results indicate that partial replacement of sand with SSW significantly improves concrete density and mechanical performance due to enhanced particle packing and the high specific gravity of steel scale particles. At the nanoscale, GNMPs contribute to pore refinement, improved nucleation of hydration products, and crack-bridging within the cement matrix, thereby strengthening the interfacial transition zone and delaying crack propagation. The combined effect of these mechanisms produces a synergistic enhancement in concrete performance. The optimum mixture containing 50% SSW and 0.3% GNMPs achieved a compressive strength of 68.2 MPa and splitting tensile strength of 7.6 MPa, representing improvements of approximately 54% and 52%, respectively, compared with the control mix. Durability-related properties such as water absorption and sorptivity were also significantly improved due to matrix densification and pore structure refinement. Although the incorporation of SSW and GNMPs reduced workability, all mixtures remained within a practical range for casting. The developed concrete is particularly suitable for structural applications requiring high strength and durability, such as high-rise building components, bridge elements, and precast structural members. The findings demonstrate that the combined use of industrial steel waste and nano-reinforcement offers a promising pathway toward sustainable and high-performance concrete. Full article

This study developed an active packaging material by incorporating tea tree oil (TTO)-loaded lotus stalk biochar (BC@TTO) into a chitosan (CS) matrix. Biochar was prepared from lotus stalks via pyrolysis at 600 °C and characterized, revealing a mesoporous structure with a specific surface area of 35.9 m²/g. Adsorption studies demonstrated that BC exhibited high affinity for TTO, following pseudo-first-order kinetics and the Langmuir isotherm model, with a maximum adsorption capacity of 295.6 mg/g. Chitosan-based composite membranes with varying BC@TTO contents (1–7 wt%) were fabricated by solution casting. The incorporation of BC@TTO significantly enhanced the tensile strength, elongation at break, barrier properties (water vapor and oxygen), and antioxidant/antibacterial activities of the membranes, with optimal performance observed at 3 wt% loading. However, higher loadings led to filler aggregation, reduced transparency, and compromised mechanical properties. In vitro release studies indicated that TTO release followed the Avrami model, suggesting a diffusion-controlled mechanism. Preservation tests on blueberries showed that the CS-3BC@TTO membrane effectively reduced weight loss and maintained fruit quality during storage. This work presents a promising strategy for designing bioactive packaging materials with sustained release functionality for food preservation applications. Full article

In social network-based group decision-making, achieving consensus often entails costs, leading to an inherent trade-off between cost and consensus. To address this issue, we propose a dual-semantic, multi-objective consensus optimization model that simultaneously minimizes cost and maximizes consensus. The resulting Pareto set offers decision makers (DMs) multiple trade-off solutions between cost and consensus. Specifically, we first develop a 2-tuple trust propagation model that incorporates path knowledge and path length to improve the completeness and accuracy of indirect trust inference. Building on this foundation, we adaptively adjust DM weights by combining trust relationships with dynamic incentive weights. This design balances individual influence and adjustment willingness throughout the consensus-reaching process. Finally, we formulate a multi-objective decision optimization model. This model integrates minimum cost and maximum consensus to generate a modified decision matrix for efficiently aggregating group opinions. A multi-physician collaboration case in a medical diagnostic decision-support system validates the effectiveness of the proposed method. Full article

Natural fibre hybrid composites have gained attention as cleaner alternatives to synthetic glass fibre systems due to their renewable feedstocks and inherent density advantage. However, moisture ingress degrades fibre–matrix integrity and mechanical performance, making durability a critical design constraint. This study systematically investigates the water absorption kinetics and post-immersion mechanical property retention in ramie–flax/epoxy hybrid composites across four fibre loadings (10–40 wt.%), with the ramie-to-flax weight ratio fixed at 1:1 in all formulations. Tensile, flexural, and impact properties were evaluated under dry and saturated conditions; Fickian diffusion kinetics were analysed to quantify moisture transport parameters; and fracture surfaces were examined by SEM. A density-based material efficiency analysis quantified the lightweighting benefit relative to equivalent synthetic glass/epoxy composites. Water absorption increased monotonically with fibre content; all formulations reached equilibrium after approximately 120 h. The 30 wt.% composite achieved dry-state tensile, flexural, and impact strengths of ca.49 MPa, ca.58 MPa, and 2.82 kJ/m² respectively, retaining ca.78%, ca.69%, and ca.82% after full saturation, superior to all other loadings. These results establish 30 wt.% as the optimal fibre loading for moisture-exposed semi-structural applications, supporting the adoption of ramie–flax composites within a cleaner manufacturing framework. Full article

Ochratoxin A (OTA), a prevalent food contaminant, is closely linked to the development of various cancers, including clear cell renal cell carcinoma (ccRCC). However, the potential mechanisms remain to be explored. In this study, we employed network toxicology, machine learning, and molecular docking techniques to systematically investigate the potential molecular mechanisms underlying OTA-associated ccRCC. We normalized transcriptional data from two Gene Expression Omnibus (GEO) datasets and analyzed it using differential expression analysis and weighted gene co-expression network analysis (WGCNA), identifying 3224 ccRCC-associated target genes. These were intersected with 232 predicted OTA target genes, yielding a total of 56 overlapping targets. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these targets were primarily enriched in critical biological processes, including extracellular matrix remodeling, immune microenvironment regulation, signaling pathway transduction, cellular metabolism, and protein homeostasis. Machine learning analysis identified “glmBoost + RF” (a sequential combination of feature selection and classifier) as the optimal model, from which nine key genes were extracted. SHapley Additive exPlanations (SHAP) analysis revealed five core genes (IGFBP3, ITGA5, PYGL, SLC22A8, LTB4R), with IGFBP3 and ITGA5 serving as the principal driver genes of the model. Validation of the model’s diagnostic efficacy and single-cell transcriptome analysis indicated that the core genes exhibited significant differential expression patterns, cell-type-specific expression characteristics, and high independent diagnostic efficacy. Molecular docking analyses predicted stable interactions between OTA and the core target proteins. These findings suggest potential molecular links between OTA exposure and ccRCC, providing a foundation for hypothesis generation and future experimental validation. Full article

The algebraic analysis of linear code parameters reveals deep connections with cryptographic constructions, including the Information Dispersal Algorithms (IDAs) and secret-sharing schemes. In this work, we propose an algebraic method for constructing bases of binary linear codes from subsets of codewords selected according to their generalized Hamming weights (GHWs). The approach employs a degree-compatible monomial ordering on the polynomial ring ${\mathbb{F}}_2[x_1,\dots ,x_n]$ and imposes the conditions $d_1\left(C\right)=1$ and $d_k\left(C\right)=n$. Under these assumptions, we prove the existence of a generator matrix containing an invertible $k\times k$ submatrix, which guarantees correct information reconstruction. This structural property enables the direct application of binary linear codes to information dispersal and recovery mechanisms without the need for larger finite fields. We validate the proposed framework through algebraic proofs and an explicit example illustrating both the dispersal and recovery procedures. These results provide a theoretical foundation for the design of information dispersal schemes relying exclusively on binary linear codes. Full article

The development of sustainable plant protection strategies requires stable and environmentally compatible delivery systems for beneficial microorganisms. In this study, Bacillus subtilis was encapsulated within chitosan-based cast films to evaluate bacterial viability, sustained biological activity, and antifungal efficacy. Films prepared from chitooligosaccharide (COS) and chitosans of low, medium, and high molecular weight (CS-LMW, CS-MMW, CS-HMW) were characterized in terms of morphology, mechanical performance, and pH-dependent swelling behavior. The viscosity of the chitosan solutions increased markedly with molecular weight from 73 cP (COS) to 614 cP (CS-HMW), while film thickness ranged from 34 ± 1.5 to 57 ± 2.3 µm. Mechanical performance improved significantly with increasing molecular weight, with maximum tensile stress exceeding 200 MPa for CS-HMW films, while swelling studies confirmed pronounced pH-dependent behavior consistent with the polyelectrolyte nature of chitosan. Encapsulation effectively preserved bacterial viability and metabolic activity over time. The intrinsic antifungal activity of chitosan synergized with the biocontrol activity of B. subtilis against Fusarium avenaceum and Alternaria solani. The highest antifungal performance was observed for CS-HMW films, which produced inhibition zones up to 84.6 ± 5.0 mm against A. solani. These findings demonstrate that chitosan-based cast films serve as effective carriers for beneficial microorganisms, providing environmental protection and regulated biological activity. The combination of a bioactive polymer matrix with a potent biocontrol agent represents a promising eco-friendly approach to sustainable plant protection. Full article

Fiber dyeability is a core indicator of textile quality and added value. Pre-experiment accurate prediction of fiber dyeability reduces the waste and inefficiency of trial-and-error methods. However, due to the limited data volume and complex mechanisms of fiber dyeability, there are no relevant studies to date. Thus, this paper proposes a novel prediction model integrating domain knowledge and process data called multi-head attention–physics-informed neural network (MHA-PINN). Within the MHA-PINN framework, limited experimental data is first augmented by using variational autoencoders, and subjected to ensemble feature selection on the augmented samples. Subsequently, a multi-head attention layer is introduced to capture the interdependencies among sample variables, thereby outputting a new feature matrix that represents the weighted fusion of these variables. Finally, a physics-informed neural network module embeds the dyeing kinetic equations into the loss function, guiding the model to converge towards accurate solutions for sample predictions. The effectiveness and superiority of the proposed MHA-PINN have been validated on a fiber dyeability experimental dataset. Full article