Search Results (428)

Framework and runtime selection for containerized microservices are usually guided by request-level benchmarks, yet deployment-facing costs often dominate operational expenditure in Kubernetes environments. This study empirically evaluates four such infrastructure characteristics: container image size, startup time, idle resource consumption, and horizontal scaling latency. Eight microservice framework configurations spanning three execution models are evaluated: JVM (Spring Boot, Spring WebFlux, Quarkus, and Ktor), GraalVM Native image (Quarkus variants, including distroless and UPX-compressed images), and Rust (Actix Web). All metrics are collected technology-agnostically at the container level via cAdvisor and Kubernetes lifecycle events. Three trade-off profiles emerged during the research: Rust achieves a 2.95 MiB idle memory footprint (a 69:1 ratio versus Spring Boot on a working-set basis, or 21:1 on the more conservative proportional-set-size basis) through garbage-collector-free memory management. GraalVM Native image variants start 1.2–1.7× faster and consume up to 1.9× less memory than their JVM equivalents, although this memory advantage is not uniform: the standard reactive Native image consumes more idle memory (115.0 MiB) than the corresponding JVM variant (98.0 MiB). A UPX compression paradox is identified and explained at the kernel level: compression shrinks images by approximately 2.5:1 yet inflates idle memory to 215–229 MiB, above JVM baselines, because decompression into private anonymous memory defeats shared page mapping. Scale-up latency (1.8–3.9 s) is governed by per-instance startup rather than framework-exclusive lifecycle optimizations, partially refuting one of four research hypotheses. The findings yield context-dependent selection guidance and a fully reproducible benchmark suite. All measurements were obtained on a single-node bare-metal K3s cluster, the primary metrics characterize the idle state of a minimal no-operation service, and the only load applied is a single fixed-rate validity check at 100 requests per second. The reported values therefore constitute lower-bound, deployment-facing infrastructure costs rather than predictions of behavior under production business workloads, multi-node topologies, or managed cloud substrates. Full article

Controller placement in software-defined wide area networks (SD-WANs) is commonly optimized assuming continuously available controllers, leaving the control plane vulnerable to overload and abrupt reassignment after controller unavailability. This paper presents Sentinel, an auditable supervisory mechanism that extends GMM-MARL placement into proactive elastic controller management through utilization monitoring, hysteresis-based scaling, learned aiding-controller placement, and bounded incremental migration. Deterministic supervision decides when scaling is authorized, whereas the learned policy selects placement and assignment subject to capacity and latency constraints. Evaluation uses a 95-node composite topology derived from OS3E, Darkstrand, and CRL. An operational track compares proactive Sentinel scale-out with reactive CCA-PSO, and an equal-resource ablation separates resource restoration from placement quality. Sentinel reduces operational average case latency (ACL) from 3988 to 3008 µs and limits maximum utilization to 87.5%, versus 98.4% under CCA-PSO. Under equal resources, Sentinel achieves 3453 ± 627 µs ACL, within 2.9% of static GMM-MARL re-optimization. An auxiliary matched RL application benchmark on OS3E against a reconstruction (RL-HCP*) shows lower ACL and worst-case latency for RL-HCP*, while Sentinel retains competitive load and coordination behavior. Overall, proactive controller headroom restoration, learned placement, and bounded migration provide resilient control-plane management without disruptive global reassignment. Full article

Fog computing enables low-latency processing for Internet of Things (IoT) applications by moving computation closer to end devices. However, fog infrastructures are often deployed on commodity and geographically distributed resources, making them vulnerable to correlated node failures caused by shared power systems, network switches, cooling units, or physical proximity. Most existing fault-tolerant scheduling methods treat node failures as independent events, which limits their ability to anticipate multi-node outages in shared-infrastructure fog environments. This paper presents CorrFault-GNN, a topology-aware fault-tolerant scheduling framework for predicting and mitigating correlated failures in fog computing. The framework models the fog infrastructure as a dynamic weighted graph that captures power, network, and geographic dependencies among fog nodes. A Temporal Graph Convolutional Network (T-GCN) learns spatial and temporal failure patterns and predicts node-level failure risks one scheduling epoch ahead. These predictions drive a proactive migration module that moves tasks away from high-risk nodes, while a Criticality-Aware Reactive Fallback handles unexpected failures. The framework is evaluated in three-tier IoT–Fog–Cloud simulations with correlated failure traces derived from cloud failure data. The results show that CorrFault-GNN improves task success, latency, energy efficiency, and deadline satisfaction compared with representative reactive, proactive, and learning-based baselines, and that its advantage grows as infrastructure sharing and failure correlation increase. Full article

On-demand data sensing and Wireless Power Transfer (WPT) enable sustainable operation in large-scale Industrial Internet of Things (IIoT) networks. Existing scheduling frameworks are inherently reactive, initiating charging only after an IoT node’s residual energy falls below a predefined threshold, which increases the risk of energy outages and service disruption. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework for self-sustainable IIoT networks under stochastic operating conditions. The proposed framework integrates three key components. First, a DT layer continuously mirrors IoT node states and predicts future energy availability over a finite prediction horizon. Second, a predictive charging strategy proactively schedules WPT before energy depletion occurs. Third, physical-layer security and DT-based anomaly detection protect the network against eavesdropping, false-data injection, and energy depletion attacks. Fourth, the scheduling framework is reformulated using chance constraints to explicitly account for uncertainty in wireless channels, energy consumption, and DT prediction errors while providing probabilistic reliability guarantees. The resulting sensing and WPT scheduling problems are formulated as multi-slot integer optimization problems and solved using branch-and-bound, with a low-complexity greedy heuristic for latency-sensitive deployments. Simulation results show that the proposed framework reduces energy outage events to below 1%, maintains reliable operation under increasing energy uncertainty with only a modest sensing-utility reduction, and incurs negligible computational overhead compared with the deterministic formulation. Full article

Cloud platforms must hold utilisation and latency targets while demand shifts and attack traffic arrive together. Reactive threshold scaling meets neither pressure, and an autoscaler blind to attacks funds the load an adversary requested. Recent work shows adversaries can drive this loop into economic denial of sustainability, so the controller sits inside the attack surface. No prior orchestrator couples workload forecasting, unsupervised anomaly detection and learned scaling in one loop, and none reports multi-seed significance testing. This article extends SARO, presented at IMCOM 2026, to close that gap. We formalise the problem as a Markov decision process with a corrected multi-objective reward, and replace the tabular agent with SARO-DQN, a continuous-state controller trained by three-step Double Q-learning. Across ten held-out days and five seeds, SARO-DQN reaches the highest composite reward (200.1 ± 16.5) and the highest utilisation (68.2%) against eight alternatives, and every reward difference is significant under Welch tests ($p<0.05$). Ablations attribute 23.6 reward points to the detector ($p=1.4\times {10}^{-6}$) and 8.9 to the forecast ($p=0.016$). On UNSW-NB15, the detector attains an AUC of 0.888. Two principles follow. Detectors must consume exogenous traffic-shape signals, and detector and policy must be trained as a coupled system. Full article

Human fertility is declining across industrialised populations, while the incidence of hormone-dependent reproductive cancers rises. Endocrine-disrupting chemicals (EDCs) and structurally related emerging pollutants are implicated in both. These two outcomes are generally reviewed as separate studies. This review argues that they are two latencies of a single molecular toxicology. The compounds concerned are structurally diverse: phthalates, bisphenols, per- and polyfluoroalkyl substances (PFASs), pesticides, polychlorinated biphenyls (PCBs) and dioxins, brominated and organophosphate flame retardants, pharmaceuticals and personal-care products (PPCPs), and micro- and nanoplastics. They nonetheless converge on a limited repertoire of molecular lesions. These include the disruption of hypothalamic–pituitary–gonadal (HPG) signalling through kisspeptin/GnRH and gonadotropin gene expression and interference at nuclear and membrane hormone receptors (ERα/β, AR, GPER, thyroid receptors, AhR, PPARγ). They also include the inhibition of steroidogenesis at StAR and the CYP11A1–CYP17A1–CYP19A1/3β-HSD/17β-HSD cascade and reactive-oxygen-species generation with mitochondrial dysfunction and Keap1–Nrf2 disruption. Epigenetic reprogramming through DNA methylation, histone modification and non-coding RNAs, together with crosstalk with metabolic and immune signalling, completes the set. These lesions produce measurable cytotoxic and genotoxic damage to gametes and the early embryo: sperm DNA fragmentation and 8-oxo-dG accumulation, blood–testis-barrier breakdown, oocyte meiotic-spindle defects, and granulosa-cell apoptosis and pyroptosis. The same receptor, oxidative and genotoxic hubs drive hormone-dependent reproductive carcinogenesis over longer latencies. The review makes three contributions. First, it traces these shared hubs continuously from fertility impairment to malignancy rather than treating them as separate fields. Second, it grades the certainty of the human evidence class by class, so that robust associations can be distinguished from provisional ones. Third, it integrates pseudo-persistent pollutants alongside the classical persistent compounds. These are micro- and nanoplastics, which act as both toxicants and vectors for adsorbed co-contaminants, and pharmaceutical and personal-care residues sustained by continuous wastewater input. Their inclusion demonstrates that chronic low-dose exposure does not require chemical persistence. We conclude with mitigation strategies and an explicit account of what the current evidence base cannot yet support. Full article

The transition toward disaggregated, software-defined 5G Standalone (SA) infrastructures has shifted much of the end-to-end latency budget from the radio interface into the transport and computational layers, where bufferbloat and congestion-window collapse degrade Quality of Service before any packet is lost. Contemporary congestion control remains fundamentally reactive, acting only after a bottleneck has materialized. This study designs and evaluates a machine learning-based early warning system that anticipates transport-layer bottlenecks several seconds in advance. We built an isolated, reproducible 5G SA testbed using Open5GS and srsRAN, emulating the air interface through ZeroMQ so that every variation in round-trip time, jitter, and throughput is attributable to queuing and protocol dynamics rather than radio-frequency noise. A stochastic generator injected variable multi-user loads over runs of up to six hours, yielding open one-second telemetry. Round-trip-time forecasting was framed as multivariate, multi-horizon regression under a strict honest-forecasting protocol that prevents temporal leakage. Across Ridge, Random Forest, HistGradientBoosting, GRU, and LSTM models, tree ensembles forecast short horizons accurately ($R^2\approx 0.90$ at one second), temporal memory improves mid-range horizons, and all models converge to the trivial baseline at twenty seconds. As a binary early-warning task, the system catches most impending bottlenecks at high precision at five seconds. The dataset and pipeline are fully documented to support reproducibility and provide a transparent benchmark for proactive, zero-touch network orchestration. Full article

Herpesviruses establish lifelong persistence through acute replication, immune evasion, latency, and reactivation. Nucleotide-binding oligomerization domain-like receptors (NLRs) are most commonly discussed in the context of inflammasome sensors, pyroptosis, and IL-1 family cytokines. Here, we depart from this canonical framework to focus on three regulatory NLRs—NLRC3, NLRC5, and NLRX1—that act outside inflammasome assembly. Using HSV-1 as the central model and other herpesviruses as comparators, we propose a conceptual framework in which these NLRs modulate three host-control layers: cGAS-STING-dependent DNA sensing, MHC class I antigen presentation, and MAVS-mediated mitochondrial antiviral signaling. We explicitly frame these NLRs as regulatory hypotheses for HSV-1 biology rather than established restriction factors, and we provide prioritized, falsifiable predictions to guide future experimentation. Current evidence is strongest at the pathway level; direct tests of NLRC3, NLRC5, and NLRX1 in acute infection, latency, and reactivation remain necessary. The evidence is asymmetric: NLRC3 has been functionally tested in HSV-1-infected cells and mice, whereas direct HSV-1 regulation by NLRC5 or NLRX1 remains unestablished. Comparative herpesvirus and pathway studies, therefore, support testable hypotheses for NLRC5 and NLRX1 in acute infection, latency and reactivation. Full article

Modern smart agriculture increasingly relies on edge computing for real-time, high-concurrency tasks such as wide-area drone-based crop monitoring. However, highly volatile workloads and severe environmental noise in agricultural Internet of Things (IoT) networks often lead to resource congestion and high latency when relying on traditional reactive scheduling. To address these challenges, this paper proposes a hybrid prediction-driven proactive resource scheduling method for edge computing. We construct a Variational Mode Decomposition-Convolutional Neural Network-Attention-Bidirectional Long Short-Term Memory (VMD-CNN-Attention-BiLSTM) model to filter environmental noise and accurately capture the spatio-temporal features of bursty traffic. Furthermore, a deep reinforcement learning scheduling algorithm based on Proximal Policy Optimization (PPO) incorporates future workload trends into its state space, dynamically optimizing task offloading. To evaluate the proposed Predictive Computational Scheduling Framework (PCSF), we developed a custom edge computing simulation environment and synthesized a hybrid dataset combining real-world server logs from the Alibaba Cluster Trace with deep learning inference workloads derived from a Wheat Plant Diseases image repository. Simulations demonstrate that the prediction model achieves a Root Mean Square Error of 0.030 and a Mean Absolute Error of 0.0215. Compared to static and reactive baselines, the PCSF reduces average task timeout violations to 2.2 and total system energy consumption by nearly 40%. This proactive mechanism effectively overcomes decision-making lags, enabling efficient, low-latency computing resource allocation for modern agricultural facilities. Full article

Objective: To explore the association of glucocorticoid (GC) exposure with objectively assessed sleep architecture in patients with rheumatoid arthritis (RA). Methods: A single-center exploratory pilot study involving 20 consecutive patients with RA (9 GC users and 11 non-users; mean age 64.9 years) was conducted. Using single-channel electroencephalography (EEG) and accelerometry, objective sleep architecture and autonomic balance were evaluated. Sleep parameters included wake after sleep onset (WASO), sleep stages, and delta EEG power during the first sleep cycle, while heart rate variability, expressed as low frequency/high frequency (LF/HF) ratio, was used as an index of autonomic balance. RA disease activity was evaluated using the Disease Activity Score in 28 joints based on C-reactive protein (DAS28-CRP). Associations between clinical variables and objective sleep parameters were evaluated using Spearman rank correlation analysis. Results: Disease activity, assessed by DAS28-CRP, did not differ significantly between GC users and non-users. Compared with non-users, GC users had longer WASO (p = 0.011), shorter non-rapid eye movement (NREM) stage N3 duration (p = 0.010), and lower delta power (p = 0.014) than non-users. A higher nighttime-to-daytime LF/HF ratio was also observed in GC users, although this difference did not reach statistical significance. Total sleep time, sleep latency, and sleep efficiency were comparable between the groups. WASO was positively correlated with age, GC use, and GC dose, whereas NREM stage N3 duration and delta power were negatively correlated with GC exposure. No significant associations were observed between age or sex and objective measures of deep sleep. Conclusions: Among patients with RA, GC exposure was associated with poorer sleep continuity and reduced deep sleep despite comparable disease activity. Given the small sample size of this exploratory single-center study, these findings should be interpreted cautiously. Larger longitudinal studies using objective sleep measures are warranted to confirm these associations. Full article

Bovine alphaherpesvirus 1 (BoHV-1) acute infection induces respiratory tract disorders and conjunctivitis and suppresses immune responses that may cause bacterial pneumonia. BoHV-1 infection establishes lifelong latency in sensory neurons in trigeminal ganglia (TG), the central nervous system, and certain cells in the pharyngeal tonsil. BoHV-1 is a chronic problem in the cattle industry because stress, including the synthetic corticosteroid dexamethasone, triggers reactivation from latency after an intravenous injection. The BoHV-1 immediate early transcription unit 1 (IEtu1) promoter drives expression of infected cell protein 0 (bICP0) and bICP4, two viral transcriptional regulators. Stress activates the glucocorticoid receptor (GR), and Krüppel-like factor 15 (KLF15) cooperatively transactivates the BoHV-1 IEtu1 promoter if both GR response elements (GREs) are intact. Since the bICP0 gene contains a separate early (E) promoter, we tested the hypothesis that GR+KLF family members transactivate the bICP0 E-promoter. GR+KLF4, both pioneer transcription factors, cooperatively stimulate bICP0 E-promoter activity in mouse neuroblastoma cells (Neuro-2A), and stimulate productive infection. Notably, the bICP0 E-promoter lacks GREs, suggesting that a novel mechanism triggers transactivation. CA motifs and C-rich Sp1 binding sites in bICP0 E-promoter sequences are crucial for transactivation and binding to GR and KLF4. Full article

Background/Objectives: Drug provocation tests (DPTs) are the gold standard for evaluating delayed drug hypersensitivity reactions (DHRs), yet the lack of standardized protocols for non-mild reactions can lead to lifelong, unnecessary drug avoidance. Gradual DPTs (gDPTs) incorporating washout intervals offer an under-investigated strategy to minimize cumulative drug exposure. Our aim was to describe the safety and clinical outcomes of a gradual reintroduction strategy using washout intervals. Methods: We retrospectively analyzed patients evaluated at the University Hospital of Montpellier (primarily June 2018–September 2023) with mainly moderate-to-severe delayed DHRs who underwent gDPTs across diverse drug classes. Washout intervals ranging from 24 h to 14 days were tailored to drug half-life, index DHR latency and severity. Results: Sixty-five patients (median age 54 years) reported 82 delayed DHRs; the 66 index reactions analyzed were predominantly triggered by beta-lactam antibiotics (83.3%), and danger signs were present in 74.2%. Seventy-seven tailored gDPTs were performed, mostly involving 3 or 4 steps and 7-day washout intervals (64.9%). Nine (11.7%, 95% CI: 5.5%–21.0%) were positive. gDPT events were elicited by culprit drugs, occurred within 48 h of the reactive dose, and were generally milder than index reactions; 88.9% occurred at ≤50% of the total planned dose. The workup enabled the delabeling of 68 previously discontinued drugs (52.7%). Conclusions: Our study provides a preliminary, hypothesis-generating proof-of-concept showing that washout intervals can limit cumulative exposure and identify early eliciting doses. Large-scale, standardized prospective studies are required to validate these findings and establish optimal protocols. Full article

Human cytomegalovirus (CMV) remains a significant infectious complication after kidney transplantation, reflecting gaps in the understanding of the molecular and immunological mechanisms regulating the transition from latency to productive infection. Following primary infection, CMV establishes lifelong latency in hematopoietic and myeloid lineage cells, maintained by viral chromatin repression and robust CMV-specific immune surveillance. CMV reactivation is associated with graft dysfunction, increased risk of rejection, opportunistic infections, and reduced patient survival. In kidney transplantation, CMV reactivation is driven by the interplay between tissue injury, inflammation, and immunosuppression. Ischemia–reperfusion injury and peri-operative stress produce reactive oxygen species, DNA damage, and pro-inflammatory cytokines (e.g., TNF-α, IL-6), which activate transcription factors such as NF-κB and AP-1. These factors regulate the CMV major immediate-early promoter (MIEP), thereby triggering lytic viral gene expression. At the same time, immunosuppressive therapies impair antiviral immune surveillance and, in some cases, induce cytokine release, potentially contributing to the pro-inflammatory environment that favors viral reactivation. In this review, we summarize current molecular and immunologic mechanisms governing CMV latency and reactivation with a focus on how immunosuppressive strategies and injury-associated pathways converge to promote CMV reactivation. We also discuss implications of risk stratification and the development of targeted therapeutic strategies to prevent CMV reactivation in kidney transplant recipients (KTRs). Full article

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by immune-mediated demyelination and neurodegeneration. Although the exact cause of MS remains unclear, accumulating epidemiological and immunological evidence strongly implicates Epstein–Barr virus (EBV) infection as a major environmental factor associated with disease development. Nearly all individuals with MS are EBV seropositive, and longitudinal studies have demonstrated that EBV infection precedes MS onset, supporting a causal relationship. EBV establishes lifelong latency in B cells and can profoundly influence host immune responses, providing several potential mechanisms through which it may contribute to autoimmunity. In this review, we summarize current knowledge of EBV biology and discuss epidemiological findings linking EBV infection with MS risk. We then examine alterations in EBV-specific immune responses observed in MS, including dysregulated humoral and cellular immunity. Particular attention is given to molecular mimicry involving the Epstein–Barr nuclear antigen 1 (EBNA1) and central nervous system proteins, which may promote cross-reactive autoimmune responses. Finally, we discuss evidence for the presence and potential role of EBV-infected immune cells within the MS brain and highlight key unanswered questions that remain critical for understanding EBV-driven neuroinflammation. Full article

Industrial Internet of Things (IIoT) networks use on-demand sensing and wireless power transfer (WPT) for self-sustainable operation. Existing scheduling frameworks are fundamentally limited because they react only after energy levels decline. Consequently, IoT nodes enter charging mode only when their residual energy falls below a threshold, causing energy outages, increased latency, and missed sensing tasks while preventing proactive WPT resource allocation. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework that transforms IIoT scheduling from reactive to proactive. The key innovation is a closed-loop virtual–real integration in which a DT layer, co-located with the control centre, maintains a Kalman filter predictor to forecast node energy over an H-slot horizon, enabling scheduling decisions before energy shortages occur. Physical layer security (PLS) constraints and DT-based anomaly detection protect against eavesdropping, energy depletion, and false data injection attacks. A multi-objective formulation jointly optimises sensing utility and WPT efficiency while accounting for DT synchronisation overhead and uplink bandwidth consumption. The resulting multi-slot Binary Integer Linear Programmes (BILP) are solved using branch-and-bound with a reliability branching rule, and a fast greedy heuristic is also developed. Simulation results over 50 Monte Carlo iterations show that the proposed framework reduces energy outage events by approximately 70% compared with the reactive baseline, activates less than 50% of available sensing nodes, and schedules less than 60% of energy transmitters for WPT. Ablation studies confirm that DT prediction is the primary contributor to the outage reduction. DT-based anomaly detection achieves a false alarm rate below 3% while maintaining a detection rate above 95%. The proposed framework improves the sustainability, efficiency, and security of IIoT networks with practical computational overhead, making it well suited for Industry 5.0 deployments. Full article