Search Results (21,692)

This paper formulates the “Minimum Vertex Cut with Reachable Set” (MVCRS) problem as an optimization framework to suppress botnet propagation in networked systems, and clarifies its computational complexity and algorithmic solutions. Building a firewall to minimize damage is essential for addressing botnet propagation in Internet of Things (IoT) networks. We define the basic MVCRS problem as minimizing the sum of the weight of the deployed resources and the resulting propagation scope. While we demonstrate that the constrained version of the problem is NP-complete, we show that the fundamental trade-off optimization model can be solved in polynomial time by reducing it to the maximum flow–minimum cut problem. This provides a theoretical baseline for optimal resource allocation in cybersecurity. Experimental evaluations reveal the limitations of conventional heuristics. In community-structured networks, the degree-based greedy algorithm overlooks critical bridge nodes, yielding an optimality gap of up to 72.6% above the theoretical minimum cost. Conversely, our exact algorithm consistently guarantees the optimal minimum cost (a 0% gap) with high statistical stability across diverse topologies. Furthermore, it scales efficiently to solve 100,000-node IoT networks within practical time limits, proving to be a reliable and efficient foundation for botnet suppression in complex real-world systems. Full article

Gamma-tocotrienol (GT3) is one of the constituents of vitamin E that demonstrated significant radioprotective efficacy in murine and nonhuman primate (NHP) models. Considering the antioxidant activity of GT3 and its role in terminating lipid peroxidation, we hypothesize that mechanism of radioprotective effect of GT3 may involve the inhibition of irradiation-induced ferroptosis—a form of regulated cell death characterized by excessive, iron-dependent, peroxidation of lipids in cellular membranes. To test this hypothesis, the metabolomic and proteomic data from serum samples of GT3- or vehicle-treated NHPs exposed to 12 Gy (partial- or total-body) radiation was analyzed with focus on lipid peroxidation markers and proteins involved in iron metabolism. Four secondary lipid peroxidation products were identified including 4-oxo-2-nonenal (4-ONE), 4-hydroperoxy-2-nonenal (4-HPNE), 3,4-epoxynonanal (3,4-ENA), and trans-4,5-epoxy-(2E)-decenal (4,5-EDE). In vehicle-treated animals, their concentrations increased significantly as soon as 4 h after irradiation and then gradually declined. GT3 treatment mitigated this radiation-induced increase. In addition to lipid peroxidation products, similar patterns of change were observed for several polyunsaturated, monounsaturated, and saturated fatty acids as well as amino acids such as lysine and its derivatives. Taken together, these metabolomic changes suggest that irradiation induces cellular membrane damage through enhanced lipid peroxidation, while GT3 exerts a protective effect against this process. In addition, GT3 increased serum levels of haptoglobin and hemopexin—two plasma scavenger proteins that play complementary protective roles in iron and heme homeostasis. Although the present study does not conclusively demonstrate that GT3 mediates radioprotection via inhibition of ferroptosis, the data suggest that GT3 limits membrane damage and reduces susceptibility to ferroptosis by enhancing iron and heme scavenging. Further investigation into the interaction between GT3 and key components of ferroptosis following exposure to ionizing radiation is therefore warranted. Full article

Ultraviolet (UV) irradiation induces complex skin damage, including hyperpigmentation, oxidative stress, and alterations in proteins related to keratinocyte differentiation and epidermal barrier-associated status. This study investigated the multifunctional protective effects of Padina arborescens ethanolic extract (PAEE) against skin damage in melanocytes, keratinocytes, and zebrafish. In alpha-melanocyte-stimulating hormone (α-MSH)-stimulated B16F10 cells, PAEE effectively suppressed the protein kinase A (PKA)/cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling pathway, which was associated with reduced expression of microphthalmia-associated transcription factor (MITF) and tyrosinase, leading to decreased melanin synthesis. PAEE also exhibited photoprotective properties by reducing reactive oxygen species (ROS), inhibiting interleukin-1 beta (IL-1β), and attenuating matrix metalloproteinase-1 (MMP-1) upregulation associated with UVB (ultraviolet B)-induced photodamage in HaCaT keratinocytes. Notably, PAEE restored the UVB-reduced expression of filaggrin and involucrin, representative markers of keratinocyte differentiation and epidermal barrier-associated status, in HaCaT keratinocytes. In zebrafish embryos, PAEE suppressed α-MSH-induced melanin accumulation and UVB-induced ROS generation at non-toxic concentrations. Taken together, these results suggest that PAEE exerts anti-melanogenic and photoprotective effects in cellular and zebrasfish models and may serve as a promising marine-derived ingredient for cosmeceutical applications targeting UVB-related skin damage. Full article

Background: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, and mitochondrial dysfunction. Emerging evidence indicates that the gut microbiota plays a critical role in modulating neuroinflammatory, and metabolic pathways involved in AD pathogenesis through the microbiota-gut-brain axis. Objective: This systematic review aims to comprehensively evaluate the role of the microbiota-gut-brain axis in Alzheimer’s disease, with a particular focus on its mechanistic links to oxidative stress, mitochondrial dysfunction, and amyloid pathology, as well as its therapeutic potential. Methodology: A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science databases, focusing on studies evaluating gut microbiota composition, metabolomic changes, oxidative stress markers, mitochondrial activity, and therapeutic interventions in AD models and patients. Results: Altered gut microbial composition in AD is associated with increased pro-inflammatory taxa (Escherichia-Shigella, Bacteroides) and depletion of short-chain fatty acid (SCFA) producing bacteria (Faecalibacterium, Roseburia). Dysbiosis contributes to systemic inflammation, disrupted intestinal permeability, and microglial activation, leading to oxidative damage and mitochondrial impairment in neurons. Preclinical and clinical studies indicate that probiotics, prebiotics, and fecal microbiota transplantation can restore redox balance, reduce neuroinflammation, and improve cognitive outcomes. Multi-omics and AI-based models are emerging as tools for identifying microbiome-derived biomarkers for early AD detection. Conclusion: The gut microbiota-mitochondria-oxidative stress axis represents a promising therapeutic target in Alzheimer’s disease. Future research should focus on longitudinal human studies, standardized microbial profiling, and personalized microbiome-based interventions to translate these mechanistic insights into clinical benefit. Full article

Mesoscale modeling provides an efficient and cost-effective approach for investigating the damage mechanisms of fiber-reinforced concrete. To address the physical distortion in conventional models that arises from neglecting the volumetric effect of steel fibers and to construct a more realistic random mesoscale model of steel fiber-reinforced concrete (SFRC), this study proposes an efficient modeling method based on geometric entity expansion and point–line topology. First, polygonal aggregates with diverse morphologies are generated using a polar-coordinate perturbation scheme combined with a convex-hull correction algorithm. Next, abandoning the traditional zero-thickness line-segment assumption, steel fibers are expanded into rectangular entities via rigid-body kinematics to explicitly represent their excluded volume. Furthermore, a vector-cross-product-based Point–Line Method is developed to replace conventional circumscribed-circle screening, enabling accurate discrimination of interference interactions between fiber–aggregate and fiber–fiber pairs. An automated framework—consisting of skeleton placement, entity generation, topological discrimination, and mesh mapping—is implemented through a Python 3.13.9 scripting interface, allowing efficient batch generation of high-content mesoscale models with aggregate area fractions up to 70%. The proposed model is then used to simulate the failure process of SFRC specimens under uniaxial compression and benchmarked against experimental results. The results show that the developed mesoscale model accurately reproduces the nonlinear mechanical response and the strengthening–toughening effects of SFRC, achieving a relative error of only 0.31% in peak stress and a root mean square error (RMSE) as low as 1.70 MPa over the full stress–strain curve. The simulations not only confirm the pronounced strength gain due to steel fiber incorporation (~19.7%), but also reveal, at the mesoscale, the mechanism by which fiber bridging suppresses damage localization, thereby demonstrating the reliability and practical effectiveness of the proposed modeling approach. Full article

Lepidoptera constitute a major group of agricultural and forestry pests. Therefore, investigating the immune mechanisms of the model species Bombyx mori may provide valuable insights for the development of improved pest management strategies. In insects, phenoloxidase (PO) and dopa decarboxylase (DDC) in immune melanization have been widely studied individually, yet their relative contributions have rarely been investigated. Here, we demonstrate that pharmacological inhibition of either PO or DDC in Escherichia coli-infected larvae significantly suppresses hemolymph melanization, with PO inhibition causing a more pronounced reduction than DDC inhibition. Consistently, RNA interference-mediated knockdown of BmPPO1 or BmPPO2 markedly decreased hemolymph melanization following infection. This results in both PO and DDC contributing to immune-induced hemolymph melanization, with PO playing a dominant role in this process. In contrast, compared to PO inhibition, DDC inhibition leads to significant damage to hemolymph antibacterial activity and cellular immune responses, including hemocyte aggregation, encapsulation, and phagocytosis. In addition, compared with the knockdown of BmPPO1 or BmPPO2, the knockdown of BmDDC leads to a more severe decrease in antibacterial activity and cellular immune function. Exogenous addition of dopamine can partially rescue cell damage, indicating that both DDC and PO play a role in cellular immunity, but DDC has a slightly stronger effect. Overall, this study provides important insights into the immunity of hemolymph in insects and other arthropods. Full article

Background: Interstitial cystitis (IC) is a debilitating lower urinary tract condition characterised by chronic inflammation of the bladder. As the aetiology remains unknown, current treatments are symptomatic, aiming to reduce inflammation and pain. Cannabidiol (CBD), the most common cannabinoid in industrial Cannabis sativa (hemp), is one of the most important pharmacologically active cannabinoids used in medicine due to its anti-inflammatory and antioxidant effects without psychoactive properties. While other cannabinoids have shown beneficial effects in animal models of IC, the impact of CBD on the urinary bladder and overall animal well-being has not been elucidated. Methods: Using a cyclophosphamide (CYP)-induced mouse model of IC, we investigated the effects of intraperitoneally administered CBD on bladder structure, function, inflammation, and animal behaviour. A multimodal approach was applied, including light and electron microscopy, immunolabeling, qPCR, transepithelial electrical resistance (TEER) measurements, behavioural testing, and monitoring of animals. Results: CBD treatment promoted the restoration of damaged urothelial structure and improved the integrity of the blood–urine barrier. Additionally, CBD exerted an anti-inflammatory effect, reducing oedema and infiltration of inflammatory cells in the bladder wall with chronic cystitis. Finally, the increased burrowing activity of CBD-treated mice suggests a benefit of CBD on overall well-being. Conclusions: Our findings suggest that CBD has a beneficial effect on the inflamed urinary bladder and could potentially serve as an adjunct treatment for patients with IC in the future. Full article

Material considerations are often neglected when developing digital twins, particularly at the relevant length scales that drive material and structural performance. For reinforced composite materials, the microscale has the largest impact on nonlinear material behavior and progressive damage, and thus accurately representing the disordered microstructure of a composite due to processing and manufacturing is critical to developing the material digital twin in the multiscale hierarchy. Automating microstructure characterization is typically done by either training convolutional neural network models using a pretrained encoder or using prompt-based segmentation tools. In this work, a toolset for developing segmentation models is presented, combining these two methods to enable rapid annotation, training, and deployment of microscopy segmentation models for automated material digital twin development without user knowledge of machine learning. Additionally, a Bayesian optimization framework is developed for generating statistically equivalent representative volume elements (SRVE) to a segmented microstructure using a random microstructure generator that implements soft body dynamics. Progressive failure analysis of random, statistically equivalent, and ordered microstructures is compared to the segmented microstructure subject to transverse loading to demonstrate the importance of accurately representing the driving material length scale of a composite digital twin. Ordered microstructures over-predicted crack initiation and ultimate strength and strain. Random and optimized RVE microstructures better agreed with the segmented simulation results, with no significant difference observed between the two methodologies. The improvement in predicted macroscale behavior for models that capture disordered microstructures due to manufacturing processes demonstrates the importance of capturing microstructure features in composites modeling and indicates that SRVEs that capture microstructural features of the physical material can be used in material digital twin development. Further, the toolsets provided in this work allow for rapid development of composite material digital twins without user expertise in machine learning. This has enabled the development of an integrated workflow to automatically characterize and idealize composite microstructures and generate representative geometric models for efficient micromechanics analysis. Full article

In response to the problem that the gears for offshore wind power are prone to cyclic stress and pitting damage under specific conditions, a finite element analysis method was adopted to establish numerical models for the distribution of cyclic stress on the gears and the dynamic expansion of pitting. Based on the material properties of ASTM5140 alloy structural steel, simulations were conducted using ANSYS 2024 R1 for contact stress analysis during gear meshing and COMSOL 6.3 for the evolution of pitting in a corrosive environment over a 120-h period. The results showed significant stress concentration in the tooth root fillet area under cyclic loads, with a maximum equivalent contact stress of 2.838 × 10⁸ Pa, which was identified as the key region for fatigue damage. Based on the simulated stress amplitude and material fatigue parameters, the predicted fatigue life of the gear under typical offshore operating conditions was approximately 13.3 years. In the corrosive environment, pitting pits exhibited an accelerating expansion trend, with pit volume increasing by approximately 125% and internal surface area by approximately 54% over 120 h. The volume growth followed a cubic polynomial, and the surface area growth followed a quadratic polynomial over time. These research results provide a quantitative basis for fatigue life assessment and corrosion protection design of offshore wind power gears. Full article

Traditional homogeneous materials often face an inherent trade-off between strength and toughness, restricting their application in high-performance impact protection. Mechanical metamaterials overcome this fundamental limitation by integrating structure and material. The 3D-printed Bouligand laminates (3DPBLs), a type of mechanical metamaterial, are renowned for their exceptional impact resistance. While the 3DPBLs have been proven to provide superior resistance under normal impact, actual service conditions inevitably involve complex, multi-directional loading. We aimed to investigate the 3DPBLs’ oblique impact resistance here. To this purpose, samples of 3DPBLs with varying helical angles (0°, 7°, 15°, 60°, 90°) were fabricated and subjected to low-velocity drop-weight impact tests at impact angles of 0°, 30°, 45°, and 60° to evaluate their damage evolution and energy dissipation. The experimental investigation exhibited distinct temporal evolutions of contact forces, with the 15° helical configuration identified as the optimal design. Further numerical analysis using a finite element model (validated with a deviation < 10%) is conducted to simulate performance under diverse impact angles in order to validate the reasonability of the experimental investigation. Mechanistically, 3DPBLs enhance impact resistance by increasing fracture tortuosity through their periodically rotated layered structure. These findings establish a theoretical foundation for developing high-performance, lightweight, and toughened protective materials. Full article

Earthquakes remain one of the greatest threats to urban resilience, demanding innovative strategies that go beyond traditional earthquake-resistant design. Among emerging solutions, viscous fluid dampers stand out as one of the most effective mechanisms for controlling structural responses and reducing damage. This research analyzes the seismic performance of a 12-story multifamily building equipped with viscous fluid dampers, developed using a comprehensive Building Information Modeling (BIM) methodology. The architectural model was integrated into a BIM environment, ensuring precision, coordination, and digital consistency. A time-history analysis was conducted in ETABS comparing two configurations—with and without dampers—subjected to seismic records from Lima-Perú, Ica-Perú, and Tarapacá-Chile. The results show that incorporating dampers significantly improves structural behavior, reducing maximum displacements by 52.25% and inter-story drifts by 47.37%. These findings confirm the ability of dampers to effectively dissipate seismic energy. Likewise, BIM integration establishes a robust digital framework for sustainable, coordinated, and resilient seismic design in high-rise buildings. Full article

Deep soft-rock tunnels are prone to large deformations and structural damage. This study used the Guanyinping Tunnel as a prototype and conducted 1/50-scale progressive loading model tests under three support configurations: rock-bolt-free, equal-length rock bolts, and mixed long–short rock bolts. Rock stress, radial rock displacement (u), and rock bolt axial force (F_N) at the vault, arch shoulders, sidewalls, and wall feet were monitored to reveal reinforcement mechanisms and mechanical response. The results indicated that stress evolution in the bolt-free case exhibited significant spatial heterogeneity. The vault experienced horizontal stress concentration, while the arch shoulder underwent vertical stress concentration. u underwent a three-stage nonlinear progression: elastic linear growth, plastic linear growth, and plastic-accelerated growth. Displacement at the vault was markedly larger than that at other locations. Equal-length rock bolts substantially improved the rock mass stability by delaying stress concentration and fracture propagation. This reinforcement raised the elastic response threshold to 96 kPa and substantially reduced u. F_N at the vault and shoulder followed linear growth, accelerated growth, and then gradual decline, whereas F_N at the sidewalls and wall feet exhibited a steady linear trend. Combined long and short rock bolts produced a multi-level anchoring effect. Short bolts induced a shallow arching action, while long bolts provided deep suspension. This synergy raised the elastic response threshold to a maximum of 120 kPa and moderated the stress reduction process. Deep residual stresses increased to 74.3–88.4% of peak values. The displacement gradient between shallow and deep rock masses was significantly reduced. The coordinated deformation capacity within the anchoring zone was markedly enhanced. F_N distribution exhibited spatial differentiation: short bolts carried the load initially, followed by the activation of long bolts. Both anchoring schemes increased residual stress and mitigated rock mass deformation. The deformation control effect was stronger in shallow rock mass than in deep rock mass. Improvements at the vault and arch shoulders exceeded those at the sidewalls and wall feet. The mixed short–long bolt configuration was superior because it maximized the self-bearing capacity of the deep rock mass. The findings provide experimental data and theoretical guidance for the design and optimization of rock-bolt support in deep soft-rock tunnels. Full article

Lumbar spine disorders often require surgical intervention using medical implants to stabilize or replace damaged structures. As the prevalence of these surgeries increases due to an aging population, rigorous preclinical evaluation is critical. This narrative review aims to summarize current testing methods, identify gaps in clinical translatability, and explore the role of emerging computational technologies. Mechanical testing protocols established by the American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) provide essential standardized data on structural integrity but fail to replicate the complex biological interactions of the human spine. Similarly, animal models offer insights into biological responses like osseointegration but are limited by quadrupedal biomechanics and anatomical differences. Recent advancements in Artificial Intelligence (AI) and Finite Element Analysis (FEA) enable rapid, patient-specific modeling and high-throughput screening, significantly reducing the time and cost of physical testing. Future innovations include 3D-printed personalized implants, bio-responsive materials, and genetically modified animal models to bridge existing translatability gaps. In conclusion, improving the clinical success of lumbar spine implants requires an integrated framework that combines mechanical, biological, and computational approaches. This interdisciplinary collaboration is vital for developing safer and more effective treatments for patients. Full article

Additive manufacturing (AM) enables a level of design flexibility that is difficult to achieve with conventional techniques, yet it inherently yields materials marked by significant variability, anisotropy, and sensitivity to defects that challenge classical mechanics-of-materials assumptions. Process-driven microstructural heterogeneity, stochastic defect populations, and residual stresses strongly influence deformation, fatigue, and fracture behavior, often outweighing nominal material properties and constraining the predictive capability of traditional constitutive and fracture mechanics models. Machine learning (ML) has emerged as a powerful means of handling the complexity of AM data; however, many current approaches depend on black-box models that lack physical transparency, extrapolate poorly, and treat uncertainty inadequately. This review contends that ML should augment—rather than replace—mechanics-based modeling, and that dependable prediction of AM material behavior requires mechanics-informed ML frameworks. We critically analyze the central mechanics challenges in AM and evaluate established modeling strategies alongside emerging ML methods relevant to deformation, damage, fatigue, and fracture. Particular emphasis is given to physics-informed and hybrid ML approaches that explicitly incorporate anisotropy, defect sensitivity, residual stress effects, and uncertainty quantification within learning architectures. Recent progress in ML-assisted constitutive modeling, fatigue and fracture prediction, and digital twin development is synthesized, and the implications for qualification, certification, and structural deployment of AM components are discussed. Full article

Sepsis is a life-threatening syndrome driven by dysregulated immune activation and multi-organ dysfunction, with limited effective therapies. Oxylipins are endogenous lipid mediators that promote the resolution of inflammation and tissue repair, yet their therapeutic potential in systemic inflammatory diseases remains incompletely understood. In this study, we evaluated the effects of two oxylipins, 10-epi-Protectin DX (10-epi-PDX) and Resolvin D5_{n-3 DPA} (RvD5_{n-3 DPA}), in a lipopolysaccharide (LPS)-induced murine endotoxemia model. Given that this model recapitulates key features of systemic inflammation and multi-organ injury relevant to sepsis-associated conditions, oxylipin effects were assessed across major organs implicated in systemic inflammatory pathology. Administration of either oxylipin significantly reduced systemic tissue injury and inflammatory damage in the lungs, kidneys, and liver. These protective effects were accompanied by suppression of inflammatory responses and marked improvements in histopathological outcomes. These findings indicate that 10-epi-PDX and RvD5_{n-3 DPA} possess organ-protective, anti-inflammatory properties in endotoxemia and support further investigation of their potential as therapeutic candidates for limiting systemic inflammatory injury. Full article