Search Results (307)

Urban environments increasingly rely on interconnected digital infrastructures like IoT devices, SDN-enabled networks, and cloud platforms to support essential municipal services. Ensuring the resilience of these systems requires advanced, data-driven mechanisms capable of detecting and mitigating cyber disruptions. This study presents Persian Eagle, a hybrid machine learning and deep learning framework designed to enhance the cyber-resilience of urban digital infrastructures by providing high-precision detection of Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt service availability by flooding targets with massive malicious traffic orchestrated through botnets, and in critical infrastructures, disruptions can be life-threatening. The proposed framework integrates multi-stage data preprocessing, SMOTE-based class balancing, and a four-phase feature-selection pipeline combining filtering, statistical ranking, PCA, and XGBoost. Seven complementary classifiers, including Random Forest, SVM, Gaussian Naive Bayes, XGBoost, MLP, LSTM, and Autoencoder, are bonded through a stacking cooperative with a Gradient Boosting meta-learner. The framework was evaluated on CICDDoS2019 and CICIDS2017 datasets, and achieved near-perfect performance up to 99.9998% accuracy, demonstrating strong generalization across diverse attack scenarios. By offering a scalable, transparent, and data-driven detection mechanism, Persian Eagle maintains urban digital-risk management and supports the continuity and resilience of critical smart-city services. Full article

This study elucidates the multi-targeted antineoplastic mechanisms of Parkia speciosa empty pod extract (PSET) against HCT-116 and HT-29 colorectal cancer (CRC) cells through integrated transcriptomic and proteomic analyses. Phytochemical profiling indicates that PSET is rich in bioactive metabolites, notably quercetin, rutin, and pyrogallol, which orchestrate its profound ability to inhibit tumor proliferation, migration, and invasion. Transcriptomic data revealed that PSET profoundly suppresses the oncogenic Wnt/β-catenin signaling axis while simultaneously activating p53-mediated cell cycle arrest. Complementary proteomic profiling uncovered critical metabolic vulnerabilities, demonstrating that PSET abrogates the Warburg effect by disrupting key glycolytic enzymes (e.g., ENO1, GAPDH, LDHA), thereby inducing metabolic starvation. Furthermore, the extract precipitated a catastrophic collapse of the cytoskeletal architecture and downregulated epithelial–mesenchymal transition (EMT) markers, effectively paralyzing the cells’ metastatic machinery. The integrated transcriptomic and proteomic signatures also highlighted an irrecoverable state of cellular stress, characterized by an overwhelming unfolded protein response and dysregulated RNA splicing, ultimately driving the cells toward apoptosis. In conclusion, this integrated omics approach provides robust molecular validation that PSET systemically dismantles colorectal cancer survival networks, highlighting its strong potential as a natural, multi-targeted therapeutic agent. Full article

Renal-resident macrophages (RMs) are essential regulators of kidney homeostasis and repair, yet the mechanisms governing RM niche regeneration after acute depletion remain poorly defined. To overcome these limitations, we have developed an inducible human CD59- intermedilysin (hCD59-ILY) ablation system, enabling rapid, specific, and reversible depletion of targeted macrophage populations, and subsequent replenishment of RMs, followed by longitudinal scRNA-seq analysis of kidneys at baseline and days 1, 3, and 7 post-ablation. RM ablation triggered a rapid and sustained upregulation of Cx3cl1, predominantly in proximal tubular epithelial cells (PTC1/PTC2), establishing a persistent chemotactic niche signal that coincided with macrophage repopulation. Regenerating RMs transitioned from inflammatory/stress-associated states toward metabolically active and proliferative phenotypes enriched in glycolysis, oxidative phosphorylation, MYC, and cell-cycle programs, with attenuation of canonical inflammatory pathways. Cell–cell communication analysis revealed an early burst of intercellular signaling at day 1, followed by progressive normalization, with fibronectin (Fn1), osteopontin (Spp1), chemokine (Ccl), and amyloid precursor protein (App) axes emerging as key mediators of niche restoration. Transcriptional network analysis identified a conserved regulatory module (Tfe3, Mitf, Hif1a, Myc, Gabpa, Rcor1) coordinating macrophage differentiation and regenerative programming, linking metabolic adaptation to lineage reconstitution. Sub-clustering revealed five dynamically shifting RM subsets with distinct inflammatory, remodeling, proliferative, and surveillance states, reflecting a hierarchical regeneration process. Functional validation using clodronate-mediated depletion in Secreted Phosphoprotein 1 (Spp1) (Opn)-deficient mice demonstrated impaired macrophage repopulation, establishing osteopontin as a critical regulator of RM regeneration. Together, these data define a coordinated epithelial–immune circuit in which Cx3cl1-driven chemotaxis, Spp1-dependent signaling, and a core transcriptional network orchestrate macrophage niche reconstitution and kidney repair following acute immune cell ablation. Full article

Smart tourism destinations, embedded by the internet and information and communication technologies, have been improving tourists’ experiences and connectivity. However, Destination Management Organisations (DMOs) still lack knowledge of how digital technologies can enhance their role and bring greater competitive advantage to destinations. In this sense, this study aims to develop an integrated smart tourism destination management ecosystem model that clarifies the relationships between digital technologies, managerial functions, benefits and implementation barriers within the broader smart city context. The study adopts a mixed-review design, combining bibliometric analysis and a systematic literature review. Bibliometric mapping was conducted using VOSviewer to analyse co-occurrence networks, thematic clusters and research trends. At the same time, the systematic review, with a systems thinking approach, enabled an in-depth qualitative examination of technological applications, managerial roles and governance implications. Data was gathered from 29 Scopus-indexed articles. The analysis identifies key benefits, including enhanced visitor experiences, improved decision-making and increased destination competitiveness, alongside persistent barriers related to governance, digital literacy, interoperability and cybersecurity. Based on these findings, the study proposes a conceptual ecosystem model that illustrates how DMOs can orchestrate digital technologies to support smart, sustainable and adaptive destination management. This research contributes to the smart tourism and smart cities literature by integrating bibliometric insights with a systems thinking perspective to develop a holistic destination management ecosystem model. Unlike prior reviews that address technologies or outcomes in isolation, this study offers a structured and actionable framework that advances theoretical understanding of smart tourism destinations while providing practical guidance for DMOs engaged in digital transformation. Full article

Tyrosinase is a pivotal therapeutic target for hyperpigmentation disorders, yet current inhibitors frequently exhibit limited potency and suboptimal safety. Here, we employed structure-based virtual screening of an FDA-approved drug library against a refined human tyrosinase homology model, identifying methotrexate and selinexor as potent anti-melanogenic candidates. Both compounds markedly suppressed cellular tyrosinase activity and melanin synthesis (IC₅₀ < 1 µM) in MNT-1 melanoma cells. Mechanistically, they orchestrate a multi-pronged downregulation of microphthalmia-associated transcription factor (MITF) by attenuating cAMP/PKA/CREB signaling, promoting β-catenin degradation, and accelerating MITF proteolysis via AKT/ERK activation. Additionally, they bolster the intracellular antioxidant defense system. These findings unveil a sophisticated regulatory network and suggest that with strict control of systemic exposure through optimized topical formulations, these FDA-approved agents could be further investigated as potential localized treatments for pigmentary disorders. Full article

Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an offloaded task can be executed only when the required runtime container is active, and a newly activated container must be supported by a locally cached service image. This dependency couples task placement, runtime container caching, and persistent image caching under limited RAM and ROM resources. To address this challenge, this paper proposes HAM-MADDPG, a dependency-aware hierarchical action-masked multi-agent reinforcement learning algorithm for joint task offloading and image–container caching in containerized MEC networks. HAM-MADDPG decomposes the monolithic orchestration decision into three causally ordered policy layers: task offloading, runtime container caching, and persistent image caching. Each layer learns a structured subproblem conditioned on upstream realized decisions, while dynamic action masking and feasibility-aware action realization guide the learned policies toward executable decisions satisfying task–container and container–image constraints. Extensive simulations under dynamic service demands and heterogeneous edge resources show that HAM-MADDPG achieves more stable convergence than non-hierarchical reinforcement learning baselines and reduces long-term system latency by approximately 14–25% compared with representative heuristic and flat DRL baselines. Full article

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains. Full article

This work proposes a vendor-independent integration method for International Electrotechnical Commission (IEC) 61850 Manufacturing Message Specification (MMS) communication protocol into Programmable Logic Controller (PLC) systems that support an open network communication interface available for the PLC program. IEC 61850 is globally well accepted for electrical substation control, and the protocol MMS is used for integrating the electrical substation bay level into the station level, where the PLC orchestrates the process level of the substation and parallel processes. This method was created because most PLCs lines do not natively support any protocol of IEC 61850, although it often needs to be used for the control of electrical substations. For the development of the prototype presented in this paper, PLCs from the Siemens AG families S7-1500 and S7-410, which support open communication over Transmission Control Protocol/Internet Protocol (TCP/IP) with external systems, were used for validation. Different results regarding network communication and PLC program performance are presented in this paper. The implemented solution presents a meaningful implementation of the MMS application layer into the PLC program and was successfully validated with real industrial, single and redundant PLC systems. Full article

The proliferation of digital financial news offers unprecedented opportunities for automated analysis of market-moving events. This paper presents a framework for event-based sentiment analysis of financial news that leverages Large Language Models (LLMs). The approach brings together three complementary ideas: Retrieval-Augmented Generation (RAG) for contextual enhancement, Graph Neural Networks (GNNs) for modeling relationships between events, and a multi-agent ensemble for orchestrated reasoning. The methodology targets well-known difficulties in financial text processing, including domain-specific terminology, implicit event detection, and temporal reasoning, and it combines transformer-based event extraction with sentiment classification enhanced by external knowledge retrieval. We evaluate six model configurations on an aggregated corpus of 14,851 financial news samples. On the event-detection task, every configuration reaches a weighted F1-score of 100%; we show that this is a ceiling effect produced by a binary event/no-event formulation over a highly imbalanced dataset rather than evidence of a difficult problem being solved, and we discuss what it implies for how such systems should be evaluated. On three-way sentiment classification, the strongest configuration—the multi-agent ensemble—reaches 87.4% accuracy, narrowly ahead of a RoBERTa (Robustly Optimized BERT Pretraining Approach) baseline at 87.2%; however, because the gaps reported between models are small and we did not run significance testing, we report them as indicative rather than definitive. The GNN component is described as part of the proposed design, but it has not yet been validated experimentally, and we state this limitation explicitly. The framework produces interpretable, structured outputs suited to downstream use in algorithmic trading, risk assessment, and investment decision support, and the paper contributes a reusable financial NLP pipeline together with a candid account of where the current evidence is, and is not, convincing. Full article

Polycomb repressive complex 2 (PRC2) regulates the expression of pluripotency genes in embryonic stem cells (ESCs) and suppresses multiple genes associated with development, cell fate determination, and differentiation. Mouse embryonic stem cells (mESCs) derived from protein kinase C inhibition (PKCi) exhibit self-renewal and pluripotency comparable to those ESCs captured by the classical 2iL (CHIR99021, PD0325901, and leukemia inhibitory factor) system. However, the dynamic expression pattern of PRC2 in PKCi-mESCs and its role in regulating pluripotency remain unclear. This study demonstrated that the expression level of the enhancer of zeste 2 gene (Ezh2), of which protein is the catalytic subunit of PRC2 responsible for the trimethylation of lysine 27 on nucleosome histone H3 subunit (H3K27me3), is significantly higher in PKCi-mESCs than in 2iL-mESCs. EZH2 knockdown enhances the self-renewal capacity of PKCi-mESCs, as evidenced by a significant increase in the number of undifferentiated mESCs colonies. The effect of an EZH2 reduced expression is accompanied by the upregulation of specific core pluripotency gene Nanog, along with the general downregulation of differentiational genes representing the three germ layers. Conversely, EZH2 overexpression promotes a significant differentiation of PKCi-mESCs, resulting in the downregulation of pluripotency genes, including core pluripotency genes Nanog and Sox2, as well as naïve pluripotency genes Klf4, Fgf4, and Esrrb, while with a wide upregulation of three germ layer associated genes. Importantly, Cleavage Under Targets and Tagmentation (CUT&Tag) demonstrates that EZH2 directly controls H3K27me3 enrichment at the Nanog promoter near the transcription start site. Thus, EZH2, a core subunit of PRC2, exhibits the distinct regulatory functions orchestrating mESCs at a poised state between self-renewal and differentiation under PKC inhibition. EZH2 exerts histone H3 methyltransferase activity to regulate Nanog expression as one of its key targets, thereby modulating the transcriptional regulatory network that maintains pluripotency and lineage specification in mESCs. Full article

Power grids are becoming more connected with 5G networks and edge-computing systems, including in civilian, emergency, and military critical-infrastructure environments. Because of this, optimization is no longer only a power-system problem or only a communication-network problem. It now involves energy, network, and computing resources simultaneously. This review focuses on grid telemetry supported by 5G network slicing and edge analytics. In this setting, data from PMUs, SCADA systems, IEDs, and AMI devices are used not only for monitoring but also for supporting state estimation, anomaly detection, and control decisions. The article reviews several AI-based optimization methods. These include learning-to-optimize, reinforcement learning, safe learning, multi-agent learning, federated learning, AirComp, graph-based models, and hybrid approaches. The review discusses these methods in relation to smart energy control, network slicing, military-resilient power and communication service, edge orchestration, and end-to-end system evaluation. Particular attention is given to tail latency, jitter, reliability, runtime, compute limits, and SLA violations, since average metrics alone are insufficient for critical grid operations. The review also proposes a practical roadmap from pilot co-simulation to edge-first analytics, slicing assurance, security hardening, and continuous monitoring, aiming to support reliable, sustainable and military-relevant smart-grid operation. Full article

In prokaryotes, translation initiation orchestrates protein synthesis through a network of dynamic interactions among the ribosome, mRNA, initiator tRNA^fMet, and initiation factors (IFs). Traditional approaches that rely on radioactive labeling or surface immobilization are hindered by inherent safety risks and methodological constraints. We present a fluorescence-based analytical platform that integrates microscale thermophoresis (MST) as a unified, multiparametric toolkit for comprehensive interrogation of bacterial translation initiation at the molecular level. By systematically applying MST to a panel of fluorescently labeled components—initiator tRNA^fMet, mRNAs, and initiation factors—we quantify assembly pathways and equilibria as initiation progresses from simple bimolecular interactions to higher-order, multicomponent complexes. To broaden the fluorescence toolbox for ribosomal studies, we developed a robust BODIPY-labeling protocol for 70S ribosomes and confirmed preservation of structural integrity and function by nano differential scanning fluorimetry, stopped-flow kinetic assays, and peptide-synthesis activity tests. Our microscale fluorescent system facilitates probing initiation at a variety of steps, since the role of magnesium ions and initiation factors upon 30S initiation complex formation. The same platform can be applied to investigate the effects of different compounds on translation initiation, as demonstrated for a number of antibiotics, aptamers, and antimicrobial peptides. Using this approach, we determined the antibiotic streptomycin dissociation constant for both 30S and 70S ribosomes, which proved identical at 0.3 ± 0.1 μM, and demonstrated the effect of the antimicrobial peptide rumicidin-1 on translation initiation. Offering a cost-effective and high-sensitivity alternative to conventional methods, this approach advances mechanistic understanding of prokaryotic translation and provides a versatile framework for the discovery of novel protein synthesis inhibitors. Full article

Embedding large language model (LLM) coordinators in production electronic systems, connected vehicles, multi-robot fabrics, IoT control loops, telecommunications orchestration, demands a pre-delivery filter stage that preserves ethical guarantees under adversarial influence at deployment scale. We present a constitutional governance layer that filters compiled influence policies before they reach a heterogeneous population of grounded LLM agents whose hybrid decision model combines a game-theoretic base probability with an LLM-evaluated narrative shift attenuated by per-agent resistance. Four experiments on a Barabási–Albert scale-free network of 30 agents powered by Llama-3.3-70B-Instruct show that the filter holds an Ethical Cooperation Score (ECS) of $0.176$ (multi-seed mean $0.163$, 95% confidence interval (CI) $[0.150,0.174]$) against an unconstrained baseline of $\mathrm{ECS}=0$, enforced by a hard integrity gate ($1.000$ vs. $0.000$). We surface an autonomy paradox in which unconstrained agents resist manipulation more forcefully ($0.856$ vs. $0.728$) yet collapse to $\mathrm{ECS}=0$, establishing that system-level integrity cannot be delegated to agent-level defence. The advantage is monotonic in resistance ($+0.174$ to $+0.183$), seed-stable (Cliff’s $\delta =1.0$, complete separation), topology- and backbone-invariant across five contemporary LLMs, robust to alternative ECS formulations, and reproduces at N = 100. Against constitutional artificial intelligence (CAI) critique-revise and LlamaGuard-style safety-classifier baselines, the framework matches the integrity floor and adds a measurable margin on the secondary risk surface (burst timing, composite manipulation risk). The filter runs at 0.78 μs/call ($\approx 1.3\times {10}^6$ decisions/s/core), supporting always-on deployment as a stateless, model-agnostic component of LLM agent pipelines in adversarially contested electronic systems. Full article

The gut–brain axis is a central regulatory network linking dietary habits, metabolic homeostasis, and brain function through bidirectional communication among the intestine, microbiota, and central nervous system. Acting as a key mediator, the gut microbiota translates environmental and nutritional factors into systemic outcomes that influence both health and disease. Within this context, serine and glycine metabolism emerges as a critical yet underexplored hub connecting microbial activity with brain regulation. Changes in gut microbial composition can profoundly affect host one-carbon metabolism and amino acid availability, shaping systemic physiology and neural processes. In this review, we outline a biochemical framework in which gut microbiota alterations influence brain and liver serine/glycine (ser/gly) metabolism, driving the hepatic production of secondary metabolites, including taurine-conjugated bile acids. We delineate how gut–brain axis pathways orchestrate systemic and neural functions, and highlight glioblastoma (GBM) as a pathological context where hijacked serine–glycine metabolism fuels tumor growth, stemness, and therapy resistance. By focusing on the interplay between gut microbiota, ser/gly metabolism, and brain tumor biology, this review offers a cohesive perspective on translational interventions. Glycine-centered pathways emerge as promising targets to modulate the gut–brain–tumor axis, opening new avenues to influence GBM progression and enhance therapeutic strategies. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article