Search Results (399)

Software-Defined Networking (SDN) centralizes control logic, improving programmability but exposing the controller to volumetric and low-rate Distributed Denial of Service (DDoS) attacks. Entropy-based detectors often raise late alarms or require significant traffic distribution changes, while machine-learning approaches impose high training and inference overhead. To address these issues, this work proposes a Statistical Mean Renyi Entropy (SMRE)-based early-warning system that amplifies micro-level disturbances in flow randomness using a tunable sensitivity weight (μ). The formulation enhances responsiveness to entropy deviations without adding computational complexity, enabling O(n) single-pass execution per monitoring window. The method was implemented on a Mininet testbed (nine switches, 64 hosts, POX controller with the L3_learning module) with mixed benign traffic and hping3/Scapy-generated UDP and TCP flood attack traffic at intensities ranging from 10 to 75%. Experimental results demonstrate that SMRE detects early-stage attacks with 94.7–98.1% accuracy, 0.8–2.3% false positive rate, and 6.5–14 ms detection latency, outperforming Shannon and classical Renyi entropy detectors. ROC analysis (AUC ≈ 0.99) and paired t-tests (p < 0.01) confirm statistical significance. Resource profiling shows negligible CPU and memory overhead, supporting real-time deployment. By eliminating model training and ensuring robust early detection, SMRE offers a lightweight and practical detection mechanism for SDN environments, whose applicability to cloud, edge, and IoT deployments will be further substantiated through validation on real traffic traces and multi-controller architectures. Full article

Human noroviruses (HuNoVs) are a leading cause of acute gastroenteritis worldwide, yet no effective antiviral therapeutics are currently available. Although environmentally induced capsid conformational changes associated with infectivity have been reported in murine noroviruses (MNVs), comparable conformational switching has not been demonstrated in HuNoVs. In this study, we generated HuNoV GII.3 virus-like particles (VLPs) using a baculovirus expression system and identified two distinct T = 3 particle populations coexisting within VLP preparations derived from a single strain through cryo-electron microscopy single-particle analysis. Comparative structural analysis revealed that these two T = 3 capsid conformations correspond to the resting and rising states of the protruding (P) domain. Rearrangement of the P domain alters intermolecular interactions between adjacent capsid subunits, resulting in distinct capsid surface architectures. In the resting state, intermolecular contacts were mediated predominantly by the P2 subdomain, with limited contribution from the P1 subdomain. In contrast, the rising state exhibited a shift toward an alternative interaction interface primarily involving the P1 subdomain. The alteration of the capsid surface accompanying this conformational switching can influence biologically relevant intermolecular interactions with viral hosts and antibodies as demonstrated in murine norovirus. These findings demonstrate previously unrecognized structural polymorphism in the HuNoV capsid and provide evidence that conformational switching may occur in HuNoVs. Our results offer new insights into norovirus capsid dynamics and may inform future structure-based vaccine and antiviral drug development. Full article

Most previous computer virus propagation (CVP) models are smooth, meaning that their right-hand sides are continuously differentiable. However, recovery resources for compromised hosts are often limited, and the aggregate recovery rate may decrease once the number of bursting nodes exceeds a defense threshold. To describe this resource-constrained mechanism, this article proposes a nonsmooth susceptible–latent–bursting–susceptible (SLBS) model with a two-level recovery function and a Holling-II saturated infection rate. Well-posedness, positivity, and positive invariance of the feasible region are first proved. The basic reproduction number is derived by the next-generation matrix method, and its normalized sensitivity indices is provided. The virus-endemic equilibria are obtained by reducing the equilibrium equations to a strictly increasing scalar equation, with special attention to the threshold case at the nonsmooth switching surface. Local stability is established by piecewise linearization and explicit Routh–Hurwitz criteria. Finally, vector-graphic numerical simulations, convergence checks, and parameter robustness tests are reported. The results clarify how limited recovery capacity and saturated infection jointly affect hierarchical control of network viruses. Full article

Tuberculosis (TB) remains a leading cause of death from a single infectious agent, and its outcome is shaped not only by Mycobacterium tuberculosis (Mtb) itself, but also by the host’s metabolic state. This review synthesises current understanding of how Mtb reprograms macrophage immunometabolism and how this reprogramming propagates to a systemic level, culminating in skeletal muscle dysregulation and TB-associated cachexia. We describe the molecular mechanisms by which Mtb subverts phagosomal maturation, the glycolytic (Warburg-like) switch governed by HIF-1α and accumulation of immunomodulatory tricarboxylic acid cycle intermediates, and the M1/M2 polarisation balance that dictates bacterial containment versus persistence. We then trace the cytokine- and metabolite-mediated circuits (TNF-α, IL-6, IL-1β, lactate, ketone bodies, free fatty acids) that link infected macrophages to ubiquitin–proteasome and autophagy–lysosome-driven muscle proteolysis, mitochondrial dysfunction and oxidative stress. Building on these mechanisms, we propose an immunometabolic and muscle-derived biomarker framework that, although still requiring clinical validation, may offer value for diagnosis, host-response stratification and treatment monitoring, and we discuss host-directed therapeutic strategies that target macrophage metabolism and muscle preservation. By integrating immunity, metabolism and systemic pathology at the molecular level, this work highlights translational opportunities relevant to the host immunity, diagnosis and treatment of tuberculosis. Full article

ROS 2 employs a publish–subscribe communication model widely used in robotic and distributed systems. As subscriber counts increase, system-level resource contention may influence latency behavior beyond simple message delivery costs. This study investigates how subscriber fan-out affects latency distributions in ROS 2 communication. Controlled experiments were conducted on a single-host topology while varying the number of subscribers and the publication rate. Latency distributions were analyzed using median, p99, and tail-probability metrics, and system-level scheduling activity was characterized using context-switch measurements. In the evaluated configuration, median latency increased from approximately 0.23 ms at one subscriber to 2.18 ms at 56 subscribers, whereas p99 latency increased from approximately 0.70 ms to 13.23 ms, indicating a much sharper expansion of the upper tail under fan-out. The measured context-switch rate also increased with subscriber fan-out, and a strong positive correlation was observed between context-switch activity and p99 latency. Additional experiments under different publication rates and platform settings showed qualitatively similar tail-growth trends, although absolute latency values varied across configurations. These findings indicate that scalability evaluation in ROS 2 communication should emphasize tail latency rather than central tendency alone, highlighting the importance of tail-aware performance analysis for scalable publish–subscribe system design. Full article

In this study, we present a discrete switching host–parasitoid model that incorporates biological and chemical control interventions within the integrated pest management (IPM) measures. The coupling of multi-tactic control measures induces rich and complex dynamical behaviors in the proposed system. We begin by systematically characterizing the existence and stability of fixed points in the control subsystem. The analysis then proceeds to demonstrate how the system undergoes multiple bifurcation routes, including period-doubling, transcritical, and Neimark–Sacker bifurcations. Building on this theoretical foundation, extensive numerical simulations are conducted, not only corroborating our analytical predictions but also revealing emergent phenomena such as cascading period-doubling routes and chaotic regimes. Finally, high-resolution two-parameter stability diagrams are employed to identify the critical dynamical transition boundaries, and the corresponding ecological implications for practical pest management decision-making are elaborated in depth. Full article

Body-centered cubic (BCC) high-entropy alloys (HEAs) are among the most promising HEA-based solid-state hydrogen-storage materials, yet their behavior is still too often discussed through composition, average phase label, or storage capacity alone. This Perspective argues that such descriptions remain incomplete because hydrogen accommodation in BCC HEAs is governed by the interplay among local interstitial accessibility, site hierarchy, and hydrogen-induced structural evolution. We therefore recast the problem around three linked questions: where hydrogen resides first, how the relative accessibility of tetrahedral and octahedral environments evolves with loading, and how that evolving occupancy redirects the host lattice toward specific hydride-transformation pathways. Recent experimental and computational studies show that hydrogen occupation in BCC HEAs is mixed, selective, and concentration-dependent, rather than fixed to a single ideal interstitial type. They also show that direct BCC-to-FCC/BCT-type hydrogenation routes, as well as pathway failure in structurally unstable BCC-related systems, are best understood from this occupancy-centered viewpoint. On this basis, we suggest that future design of BCC HEA hydrides should move beyond composition screening toward an occupancy-informed framework in which local site hierarchy, pathway integrity, and hydrogen-induced phase switching are treated as central design variables. Full article

The integration of distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems, into distribution networks necessitates accurate estimation of hosting capacity (HC). This paper presents an optimization-based approach for HC assessment and enhancement, which considers both overvoltage and line overload constraints and incorporates the reactive power (VAR) capabilities of DER inverters. Furthermore, the methodology is extended to include network reconfiguration, leveraging switchable branches to alleviate network congestion and further enhance DER integration. The proposed method utilizes a linearized power flow model to ensure computational efficiency and formulates the problem as a convex optimization task when considering only inverter VAR capabilities. The framework jointly addresses overvoltage, line overload, and inverter VAR capability constraints through linear and second-order cone constraints. In the extended formulation that includes network reconfiguration, binary decision variables are introduced to model switch statuses, resulting in a mixed-integer optimization problem. Simulation results based on the IEEE 33-bus system demonstrate that reactive power optimization can effectively redistribute HC across nodes, improving power quality in congested networks. Additionally, the incorporation of network reconfiguration provides further HC enhancement, particularly in scenarios where fixed network topology severely limits DER integration. Simulation studies are further extended to the UKGDS 95-bus system, which is derived from a real UK distribution network and incorporates a 33/11 kV on-load tap changer (OLTC) transformer, thereby providing a more practically representative validation platform. The results demonstrate that the proposed framework is effective across networks of different scales and complexities. The proposed approach offers a flexible and efficient tool for modern distribution network planning, supporting high-penetration DER integration while maintaining grid stability and operational reliability. Full article

Two metallacyclic tetranuclear Fe(III) complexes, [{Fe₂(μ-O)(μ-RCOO)₂(tpon)}₂](BPh₄)₄ [R = Me (1), Ph (2)], where the flexible ditopic ligand tpon (N,N,N′,N′-tetrakis(2-pyridylmethyl)octane-1,8-diamine) links two μ-oxo-bis(μ-carboxylato) triple-bridged dinuclear units, have been prepared. Single-crystal X-ray diffraction establishes that both complexes adopt a 26-membered macrocyclic framework featuring an internal cavity capable of guest inclusion. Notably, incorporation of a monoatomic μ-oxo bridge enforces an outward orientation of the ligand alkyl chains, thereby suppressing the “zipper effect” observed in the previously reported Mn(II) analogue and facilitating the encapsulation of an acetone molecule. UV–vis absorption and diffuse-reflectance spectra confirm that the tetranuclear scaffold remains intact in both the solid state and in solution. These results demonstrate that modulating local coordination directionality via μ-oxo bridging is an effective strategy for controlling the global conformation and host–guest properties of large metallasupramolecular architectures. Full article

Breakthrough invasive fungal infections (bIFIs) are a challenging serious complication in high-risk hematologic patients and allogeneic hematopoietic stem cell transplantation recipients that may negatively impact their outcome. Despite advances in antifungal prophylaxis, diagnostics, and supportive care, bIFI occurrence reflects a complex interaction between host immunosuppression, emergence of resistant pathogens and pharmacological variables, including subtherapeutic drug exposure. Candida spp. have shifted towards non-albicans yeasts, whereas breakthrough mold infections more frequently involve non-fumigatus Aspergillus, Mucorales, Fusarium spp., and Scedosporium/Lomentospora spp. Early clinical recognition, rapid therapy escalation, aggressive diagnostic investigation, a switch to liposomal amphotericin B-based regimens in patients on azole prophylaxis, and therapeutic drug monitoring are essential to improve outcomes. Reducing the growing global burden of bIFIs will also require improved access to high-quality diagnostics and strengthened educational and stewardship efforts that prioritize antifungal resistance as an urgent health concern. Full article

Respiratory infections remain a leading cause of morbidity and mortality worldwide, highlighting the urgent need to better understand host defense mechanisms in the respiratory tract. Recent advances in sequencing technologies have challenged the traditional view of the lungs as sterile organs and revealed the presence of a distinct, low-biomass microbial community known as the lung microbiota. These microbial populations interact closely with airway epithelial cells and immune cells to maintain respiratory homeostasis and regulate host immune responses. In healthy lungs, microbial communities dominated by Firmicutes, Bacteroidetes, and Proteobacteria contribute to immune regulation through interactions with innate and adaptive immune pathways. Microbiota-derived signals are detected by pattern recognition receptors, activating signaling pathways that regulate cytokine production, immune cell recruitment, and T-cell differentiation. In the respiratory mucosa, microbial stimulation can also induce epithelial and antigen-presenting cells to produce B-cell activating factor (BAFF) and a proliferation-inducing ligand (APRIL), which promote immunoglobulin A (IgA) class-switch recombination and support mucosal antibody responses. During pulmonary infection, disruption of microbial communities can lead to dysbiosis that amplifies inflammatory responses, impairs epithelial barrier integrity, and increases susceptibility to secondary bacterial infections. In addition to local microbial interactions, the gut–lung axis represents a key communication pathway linking intestinal microbiota with respiratory immunity through microbial metabolites such as short-chain fatty acids (SCFAs) and immune signaling networks. This review summarizes current insights into microbiota–immune crosstalk in the lung during pulmonary infection and discusses how these interactions may inform mucosal vaccine development. A deeper understanding of host–microbiota interactions may enable microbiome-informed vaccines and therapeutic strategies to improve protection against respiratory diseases. Full article

Both plants and animals have developed a sophisticated two-tiered innate immune system. This involves an initial recognition of microbial patterns conserved on the cell surface (PAMP-triggered immunity) and a subsequent more specific intracellular recognition of pathogenic effectors or their activities (effector-triggered immunity). A common fundamental feature is the use of NLR-like intracellular receptors to detect insider threats. Both plant NLRs (receptors containing nucleotide-binding domains and leucine-rich repeats) and animal NLRs (NOD-like receptors) share a modular tripartite architecture, typically featuring a central nucleotide-binding domain (NBD/NOD) and C-terminal leucine-rich repeats (LRRs). The NBD/NOD is crucial for facilitating the exchange of ADP/ATP, acting as a molecular switch to promote oligomerization and activation of NLRs in both kingdoms. In this review, we summarize the similarities and differences between plant and animal molecular perception and immunity mechanisms. Additionally, we highlight the fact that some human pathogens can infect plants, and crucially, some plant pathogens are capable of causing disease in humans. This suggests conserved molecular strategies to invade and manipulate host cells belonging to different biological kingdoms, uncovering that plant and human pathology may benefit from future investigations in their respective fields. Full article

The explosive growth of temporal graph data has led to significant training overheads for Dynamic Graph Neural Networks (DGNNs), a bottleneck primarily driven by massive data movement between host processors and storage arrays across conventional PCIe I/O buses. While near-data processing with Computational Storage Devices (CSDs) can alleviate this bottleneck, a single CSD is inherently incapable of meeting the terabyte-scale capacity requirements and complex sequence modeling demands of modern large-scale DGNNs. Horizontal scaling with multi-CSD clusters over standard PCIe topologies presents a viable, cost-effective solution, yet our in-depth profiling identifies two critical architectural bottlenecks in naive multi-CSD architectures: host-bounced memory copies significantly compromise inter-device communication efficiency, and sparse graph sampling frequently exceeds the capacity of the tightly constrained local DRAM of CSDs, resulting in excessive flash I/O and performance degradation. To address these interconnected bottlenecks, we propose M-DGNN, a hardware–software co-designed architecture optimized for standard PCIe interconnects. First, M-DGNN orchestrates direct peer-to-peer (P2P) DMA dataflows for inter-CSD hidden state exchange, completely bypassing host operating system intervention and reducing the context-switching overhead. Second, we design a host-assisted caching strategy with a Host-Pinned Memory Extension (HPME) mechanism, which leverages host-pinned memory as an asynchronous DMA extension pool to shield resource-constrained CSDs from high-latency flash I/O during structural subgraph sampling. Extensive experimental evaluations across seven large-scale dynamic graph datasets demonstrate that M-DGNN delivers up to a 6.2× end-to-end speedup over the state-of-the-art DGNN systems. This work establishes an efficient, scalable near-data computing paradigm for large-scale DGNN training. Full article

Understanding the genetic changes that allow avian influenza A viruses (IAVs) to switch their natural hosts and establish productive infection in humans is important for pandemic risk assessment. Adaptations in the IAV polymerase are required to overcome species-specific restrictions imposed by host ANP32 proteins. Notably, avian virus polymerase is generally only poorly supported by human ANP32 proteins due to species-specific differences. Consequently, efficient polymerase adaptation to the binding interface of human ANP32 requires distinct amino acid changes, such as PB2 E627K. A separate adaptation, PB2 M631L, has recently been reported in mammalian-adapted IAV; however, its functional role across divergent viral lineages and its relationship to host ANP32-dependent adaptation remain incompletely defined. Here, we examine PB2 M631L in the polymerases of a 1918 pandemic strain, a recombinant contemporary H1N1pdm09, and a recent clade 2.3.4.4b H5N1 virus. Using polymerase activity and protein-interaction assays, we show that PB2 M631L enhances polymerase activity and ANP32 binding in human—but not avian—contexts, and that this effect is conserved across multiple viral backgrounds. In H1N1pdm09, PB2 M631L also increased virus replication in mammalian cells. These findings indicate that PB2 M631L contributes to enhanced polymerase compatibility with human ANP32 proteins and are consistent with a role in adaptation across multiple influenza virus lineages. Our results highlight how analysis of historical pandemic strains can inform risk assessment for future emerging viruses. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article